|
1
|
Miao X, Chen T, Lang Z, Wu Y, Wu X, Zhu Z, Xu RX. Design, fabrication, and application of bioengineering vascular networks based on microfluidic strategies. J Mater Chem B 2025; 13:1252-1269. [PMID: 39691980 DOI: 10.1039/d4tb02047b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2024]
Abstract
Vascularization is a critical component of tissue engineering research and is essential for enhancing the success rate of tissue construction and function. Over the past decade, researchers have explored various methods to construct in vitro vascular networks, including 3D printing, cell sphere technology, and microfluidics. Microfluidic technology has garnered significant attention due to its notable advantages in precision, controllability, flexibility, and applicability. It can be primarily classified into two modes: (i) the pre-designed mode, which involves creating vascular networks by pre-designing vascular channels and seeding endothelial cells, encompassing microfluidic chips and microfluidic spinning technologies; and (ii) the self-assembly mode, where cell spheres are fabricated using microfluidic technology and subsequently self-assemble into vascular networks. In this review, we first provide a brief overview of the normal physiological and pathological characteristics of vascular networks, followed by a discussion of the factors to be considered in designing in vitro vascular networks, and conclude with an examination of the classification of technologies for the preparation of microfluidic vascular networks and recent advancements. It is anticipated that in vitro vascular network models will soon be successfully applied in regenerative medicine and drug development.
Collapse
Affiliation(s)
- Xiaoping Miao
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, P. R. China
| | - Tianao Chen
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, P. R. China
| | - Zhongliang Lang
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, P. R. China
- Department of Plastic Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, P. R. China.
| | - Yongqi Wu
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, P. R. China
| | - Xizhi Wu
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, P. R. China
| | - Zhiqiang Zhu
- Department of Plastic Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, P. R. China.
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China
| | - Ronald X Xu
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, P. R. China
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China
| |
Collapse
|
|
2
|
Qadir A, Singh N, Moe AAK, Cahoon G, Lye J, Chao M, Foroudi F, Uribe S. Potential of MRI in Assessing Treatment Response After Neoadjuvant Radiation Therapy Treatment in Breast Cancer Patients: A Scoping Review. Clin Breast Cancer 2025; 25:e1-e9.e2. [PMID: 38906720 DOI: 10.1016/j.clbc.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/07/2024] [Accepted: 05/26/2024] [Indexed: 06/23/2024]
Abstract
The objective of this scoping review is to evaluate the potential of Magnetic Resonance Imaging (MRI) and to determine which of the available MRI techniques reported in the literature are the most promising for assessing treatment response in breast cancer patients following neoadjuvant radiotherapy (NRT). Ovid Medline, Embase, CINAHL, and Cochrane databases were searched to identify relevant studies published from inception until March 13, 2023. After primary selection, 2 reviewers evaluated each study using a standardized data extraction template, guided by set inclusion and exclusion criteria. A total of 5 eligible studies were selected. The positive and negative predictive values for MRI predicting pathological complete response across the studies were 67% to 88% and 76% to 85%, respectively. MRI's potential in assessing postradiotherapy tumor sizes was greater for volume measurements than uni-dimensional longest diameter measurements; however, overestimation in surgical tumor sizes was observed. Apparent diffusion coefficient (ADC) values and Time to Enhance (TTE) was seen to increase post-NRT, with a notable difference between responders and nonresponders at 6 months, indicating a potential role in assessing treatment response. In conclusion, this review highlights tumor volume measurements, ADC, and TTE as promising MRI metrics for assessing treatment response post-NRT in breast cancer. However, further research with larger cohorts is needed to confirm their utility. If MRI can accurately identify responders from nonresponders to NRT, it could enable a more personalized and tailored treatment approach, potentially minimizing radiation therapy related toxicity and enhancing cosmetic outcomes.
Collapse
Affiliation(s)
- Ayyaz Qadir
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia.
| | - Nabita Singh
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia
| | - Aung Aung Kywe Moe
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia
| | - Glenn Cahoon
- Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Jessica Lye
- Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Michael Chao
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia; Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Farshad Foroudi
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia; Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Sergio Uribe
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia
| |
Collapse
|
|
3
|
Moloney B, Li X, Hirano M, Saad Eddin A, Lim JY, Biswas D, Kazerouni AS, Tudorica A, Li I, Bryant ML, Wille C, Pyle C, Rahbar H, Hsieh SK, Rice-Stitt TL, Dintzis SM, Bashir A, Hobbs E, Zimmer A, Specht JM, Phadke S, Fleege N, Holmes JH, Partridge SC, Huang W. Initial experience in implementing quantitative DCE-MRI to predict breast cancer therapy response in a multi-center and multi-vendor platform setting. Front Oncol 2024; 14:1395502. [PMID: 39678499 PMCID: PMC11638047 DOI: 10.3389/fonc.2024.1395502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 10/28/2024] [Indexed: 12/17/2024] Open
Abstract
Quantitative dynamic contrast-enhanced (DCE) MRI as a promising method for the prediction of breast cancer response to neoadjuvant chemotherapy (NAC) has been demonstrated mostly in single-center and single-vendor platform studies. This preliminary study reports the initial experience in implementing quantitative breast DCE-MRI in multi-center (MC) and multi-vendor platform (MP) settings to predict NAC response. MRI data, including B1 mapping, variable flip angle (VFA) measurements of native tissue R1 (R1,0), and DCE-MRI, were acquired during NAC at three sites using 3T systems with Siemens, Philips, and GE platforms, respectively. High spatiotemporal resolution DCE-MRI was performed using similar vendor product sequences with k-space undersampling during acquisition and view sharing during reconstruction. A breast phantom was used for quality assurance/quality control (QA/QC) across sites. The Tofts model (TM) and shutter-speed model (SSM) were used for pharmacokinetic (PK) analysis of the DCE data. Additionally, tumor region of interest (ROI)- vs. voxel-based analyses in combination with the use of VFA-measured R1,0 vs. fixed, literature-reported R1,0 were investigated to determine the optimal analysis approach. Results from 15 patients who completed the study are reported. Voxel-based PK analysis using fixed R1,0 was deemed the optimal approach, which allowed the inclusion of data from one vendor platform where VFA measurements produced ≥100% overestimation of R1,0. The semi-quantitative signal enhancement ratio (SER) and quantitative PK parameters outperformed the tumor longest diameter (LD) in the prediction of pathologic complete response (pCR) vs. non-pCR after the first NAC cycle, whereas Ktrans consistently provided more accurate predictions than both SER and LD after the first NAC cycle and at the NAC midpoint. Both TM and SSM Ktrans and kep were excellent predictors of response at the NAC midpoint with ROC AUC >0.90, while the SSM parameters (AUC ≥0.80) performed better than their TM counterparts (AUC <0.80) after the first NAC cycle. The initial experience of this ongoing study indicates the importance of QA/QC using a phantom and suggests that deploying voxel-based PK analysis using a fixed R1,0 may mitigate random errors from R1,0 measurements across platforms and potentially eliminate the need for B1 and VFA acquisitions in MC and MP trials.
Collapse
Affiliation(s)
- Brendan Moloney
- Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, United States
| | - Xin Li
- Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, United States
| | - Michael Hirano
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Assim Saad Eddin
- Department of Radiology, University of Iowa, Iowa City, IA, United States
| | - Jeong Youn Lim
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Debosmita Biswas
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Anum S. Kazerouni
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Alina Tudorica
- Department of Diagnostic Radiology, Oregon Health and Science University, Portland, OR, United States
| | - Isabella Li
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Mary Lynn Bryant
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Courtney Wille
- Institute for Clinical and Translational Science, University of Iowa, Iowa City, IA, United States
| | - Chelsea Pyle
- Department of Diagnostic Radiology, Oregon Health and Science University, Portland, OR, United States
| | - Habib Rahbar
- Department of Radiology, University of Washington, Seattle, WA, United States
- Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Su Kim Hsieh
- Department of Radiology, University of Iowa, Iowa City, IA, United States
| | - Travis L. Rice-Stitt
- Department of Pathology, Oregon Health and Science University, Portland, OR, United States
| | - Suzanne M. Dintzis
- Fred Hutchinson Cancer Center, Seattle, WA, United States
- Department of Pathology, University of Washington, Seattle, WA, United States
| | - Amani Bashir
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States
- Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | - Evthokia Hobbs
- Hematology and Medical Oncology Division, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Alexandra Zimmer
- Hematology and Medical Oncology Division, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Jennifer M. Specht
- Fred Hutchinson Cancer Center, Seattle, WA, United States
- Division of Hematology and Oncology, University of Washington, Seattle, WA, United States
| | - Sneha Phadke
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States
- Department of Internal Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | - Nicole Fleege
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States
- Department of Internal Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | - James H. Holmes
- Department of Radiology, University of Iowa, Iowa City, IA, United States
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States
| | - Savannah C. Partridge
- Department of Radiology, University of Washington, Seattle, WA, United States
- Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, United States
| |
Collapse
|
|
4
|
Yin X, Ruan X, Zhu Y, Yin Y, Huang R, Liang C. Prediction of peritoneal free cancer cells in gastric cancer patients by golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging. J Zhejiang Univ Sci B 2024; 25:617-627. [PMID: 39011681 PMCID: PMC11254683 DOI: 10.1631/jzus.b2300929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/21/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVES Peritoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging (GRASP DCE-MRI) to predict the presence of peritoneal free cancer cells in gastric cancer patients. METHODS All enrolled patients were consecutively divided into analysis and validation groups. Preoperative magnetic resonance imaging (MRI) scans and perfusion were performed in patients with gastric cancer undergoing surgery, and peritoneal lavage specimens were collected for examination. Based on the peritoneal lavage cytology (PLC) results, patients were divided into negative and positive lavage fluid groups. The data collected included clinical and MR information. A nomogram prediction model was constructed to predict the positive rate of peritoneal lavage fluid, and the validity of the model was verified based on data from the verification group. RESULTS There was no statistical difference between the proportion of PLC-positive cases predicted by GRASP DCE-MR and the actual PLC test. MR tumor stage, tumor thickness, and perfusion parameter Tofts-Ketty model volume transfer constant (Ktrans) were independent predictors of positive peritoneal lavage fluid. The nomogram model featured a concordance index (C-index) of 0.785 and 0.742 for the modeling and validation groups, respectively. CONCLUSIONS GRASP DCE-MR could effectively predict peritoneal free cancer cells in gastric cancer patients. The nomogram model constructed using these predictors may help clinicians to better predict the risk of peritoneal free cancer cells being present in gastric cancer patients.
Collapse
Affiliation(s)
- Xueqing Yin
- The First Affiliated Hospital of Ningbo University, Ningbo 315000, China.
| | - Xinzhong Ruan
- The First Affiliated Hospital of Ningbo University, Ningbo 315000, China
| | - Yongmeng Zhu
- The First Affiliated Hospital of Ningbo University, Ningbo 315000, China
| | - Yongfang Yin
- The First Affiliated Hospital of Ningbo University, Ningbo 315000, China
| | - Rui Huang
- The First Affiliated Hospital of Ningbo University, Ningbo 315000, China
| | - Chao Liang
- Ningbo Medical Center Lihuili Hospital, Ningbo 315000, China
| |
Collapse
|
|
5
|
Zhang Y, Wang F, Huang Y. PDZK1 is correlated with DCE-MRI perfusion parameters in high-grade glioma. Clinics (Sao Paulo) 2024; 79:100367. [PMID: 38692010 PMCID: PMC11070665 DOI: 10.1016/j.clinsp.2024.100367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/11/2024] [Accepted: 04/11/2024] [Indexed: 05/03/2024] Open
Abstract
OBJECTIVE This study investigated the relationship between PDZK1 expression and Dynamic Contrast-Enhanced MRI (DCE-MRI) perfusion parameters in High-Grade Glioma (HGG). METHODS Preoperative DCE-MRI scanning was performed on 80 patients with HGG to obtain DCE perfusion transfer coefficient (Ktrans), vascular plasma volume fraction (vp), extracellular volume fraction (ve), and reverse transfer constant (kep). PDZK1 in HGG patients was detected, and its correlation with DCE-MRI perfusion parameters was assessed by the Pearson method. An analysis of Cox regression was performed to determine the risk factors affecting survival, while Kaplan-Meier and log-rank tests to evaluate PDZK1's prognostic significance, and ROC curve analysis to assess its diagnostic value. RESULTS PDZK1 was upregulated in HGG patients and predicted poor overall survival and progression-free survival. Moreover, PDZK1 expression distinguished grade III from grade IV HGG. PDZK1 expression was positively correlated with Ktrans 90, and ve_90, and negatively correlated with kep_max, and kep_90. CONCLUSION PDZK1 is upregulated in HGG, predicts poor survival, and differentiates tumor grading in HGG patients. PDZK1 expression is correlated with DCE-MRI perfusion parameters.
Collapse
Affiliation(s)
- Yi Zhang
- Department of Radiology, The First People's Hospital of Shuangliu District, (West China Airport Hospital of Sichuan University), Chengdu City, Sichuan Province, China.
| | - Feng Wang
- Department of Radiology, The First People's Hospital of Shuangliu District, (West China Airport Hospital of Sichuan University), Chengdu City, Sichuan Province, China
| | - YongLi Huang
- Department of Radiology, The First People's Hospital of Shuangliu District, (West China Airport Hospital of Sichuan University), Chengdu City, Sichuan Province, China
| |
Collapse
|
|
6
|
Abstract
The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.
Collapse
Affiliation(s)
- Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - James H Holmes
- Radiology, Biomedical Engineering, and Holden Cancer Center, University of Iowa, 169 Newton Road, Iowa City, IA 52242, USA.
| |
Collapse
|
|
7
|
Li Z, Ma Q, Gao Y, Qu M, Li J, Lei J. Diagnostic performance of MRI for assessing axillary lymph node status after neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis. Eur Radiol 2024; 34:930-942. [PMID: 37615764 DOI: 10.1007/s00330-023-10155-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 06/09/2023] [Accepted: 07/08/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVE This systematic review examined the diagnostic performance of magnetic resonance imaging (MRI) for assessing axillary lymph node status (ALNS) after neoadjuvant chemotherapy (NAC) in breast cancer patients. METHODS We searched PubMed, Embase, Cochrane Library, and Web of Science to identify relevant studies and used the QUADAS-2 tool to assess methodological quality of eligible studies. We used STATA version 12.0 to perform data pooling, heterogeneity testing, subgroup analysis, and sensitivity analysis. RESULTS For the 21 enrolled studies, including 2875 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were respectively 0.63 (95% CI: 0.53-0.72), 0.75 (95% CI: 0.68-0.81), 2.52 (95% CI: 1.98-3.19), 0.50 (95% CI: 0.39-0.63), and 5.08 (95% CI: 3.38-7.63). The AUC was 0.76 (95% CI: 0.72-0.79). I2 values of sensitivity (I2 = 94.41%) and specificity (I2 = 88.97%) were both > 50%. For the initial positive ALN patients, the pooled sensitivity and specificity were 0.64 (95% CI: 0.53-0.75) and 0.74 (95% CI: 0.64-0.82), respectively. Sensitivity analyses by focusing on studies with MRI performed post-NAC, studies using DCE-MRI, or studies with low risk of bias showed similar results to the primary analyses. CONCLUSION MRI may have suboptimal diagnostic value in assessing ALNS after NAC for breast cancer patients. Due to the inconsistency of NAC regimens, the variability of axillary surgery, and the lack of time interval between MRI and surgery, further studies are needed to confirm our findings. CLINICAL RELEVANCE STATEMENT Our study provided the diagnostic value of MRI in assessing axillary lymph node status after neoadjuvant chemotherapy for breast cancer patients. KEY POINTS • MRI may have suboptimal diagnostic value in assessing axillary lymph node status after NAC for general breast cancer patients. • The initial axillary lymph node status has little impact on the diagnostic efficacy of MRI. • The substantial heterogeneity among studies highlights the need for further studies to provide more high-quality evidence in this field.
Collapse
Affiliation(s)
- Zhifan Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Qinqin Ma
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Mengmeng Qu
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Jinkui Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
- Department of Radiology, the First Hospital of Lanzhou University, Chengguan District, No. 1 Donggang West Road, Lanzhou, 730000, Gansu Province, China
| | - Junqiang Lei
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China.
- Department of Radiology, the First Hospital of Lanzhou University, Chengguan District, No. 1 Donggang West Road, Lanzhou, 730000, Gansu Province, China.
| |
Collapse
|
|
8
|
Børretzen A, Reisæter LAR, Ringheim A, Gravdal K, Haukaas SA, Fasmer KE, Haldorsen IHS, Beisland C, Akslen LA, Halvorsen OJ. Microvascular proliferation is associated with high tumour blood flow by mpMRI and disease progression in primary prostate cancer. Sci Rep 2023; 13:17949. [PMID: 37863961 PMCID: PMC10589248 DOI: 10.1038/s41598-023-45158-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 10/17/2023] [Indexed: 10/22/2023] Open
Abstract
Active angiogenesis may be assessed by immunohistochemistry using Nestin, a marker of newly formed vessels, combined with Ki67 for proliferating cells. Here, we studied microvascular proliferation by Nestin-Ki67 co-expression in prostate cancer, focusing on relations to quantitative imaging parameters from anatomically matched areas obtained by preoperative mpMRI, clinico-pathological features and prognosis. Tumour slides from 67 patients (radical prostatectomies) were stained for Nestin-Ki67. Proliferative microvessel density (pMVD) and presence of glomeruloid microvascular proliferation (GMP) were recorded. From mpMRI, forward volume transfer constant (Ktrans), reverse volume transfer constant (kep), volume of EES (ve), blood flow, and apparent diffusion coefficient (ADC) were obtained. High pMVD was associated with high blood flow (p = 0.008) and low ADC (p = 0.032). High Ktrans, kep, and blood flow were associated with high Gleason score. High pMVD, GMP, and low ADC were associated with most adverse clinico-pathological factors. Regarding prognosis, high pMVD, Ktrans, kep, and low ADC were associated with reduced biochemical recurrence-free- and metastasis-free survival (p ≤ 0.044) and high blood flow with reduced time to biochemical- and clinical recurrence (p < 0.026). In multivariate analyses however, microvascular proliferation was a stronger predictor compared with blood flow. Indirect, dynamic markers of angiogenesis from mpMRI and direct, static markers of angiogenesis from immunohistochemistry may aid in the stratification and therapy planning of prostate cancer patients.
Collapse
Affiliation(s)
- Astrid Børretzen
- Centre for Cancer Biomarkers CCBIO, Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
- Department of Pathology, Haukeland University Hospital, 5021, Bergen, Norway.
| | - Lars A R Reisæter
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Anders Ringheim
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Karsten Gravdal
- Department of Pathology, Haukeland University Hospital, 5021, Bergen, Norway
| | - Svein A Haukaas
- Department of Urology, Haukeland University Hospital, Bergen, Norway
| | - Kristine E Fasmer
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Ingfrid H S Haldorsen
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Christian Beisland
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Urology, Haukeland University Hospital, Bergen, Norway
| | - Lars A Akslen
- Centre for Cancer Biomarkers CCBIO, Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Pathology, Haukeland University Hospital, 5021, Bergen, Norway
| | - Ole J Halvorsen
- Centre for Cancer Biomarkers CCBIO, Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| |
Collapse
|
|
9
|
Hirschler L, Sollmann N, Schmitz‐Abecassis B, Pinto J, Arzanforoosh F, Barkhof F, Booth T, Calvo‐Imirizaldu M, Cassia G, Chmelik M, Clement P, Ercan E, Fernández‐Seara MA, Furtner J, Fuster‐Garcia E, Grech‐Sollars M, Guven NT, Hatay GH, Karami G, Keil VC, Kim M, Koekkoek JAF, Kukran S, Mancini L, Nechifor RE, Özcan A, Ozturk‐Isik E, Piskin S, Schmainda K, Svensson SF, Tseng C, Unnikrishnan S, Vos F, Warnert E, Zhao MY, Jancalek R, Nunes T, Emblem KE, Smits M, Petr J, Hangel G. Advanced MR Techniques for Preoperative Glioma Characterization: Part 1. J Magn Reson Imaging 2023; 57:1655-1675. [PMID: 36866773 PMCID: PMC10946498 DOI: 10.1002/jmri.28662] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.
Collapse
Affiliation(s)
- Lydiane Hirschler
- C.J. Gorter MRI Center, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Nico Sollmann
- Department of Diagnostic and Interventional RadiologyUniversity Hospital UlmUlmGermany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Bárbara Schmitz‐Abecassis
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
- Medical Delta FoundationDelftThe Netherlands
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
| | | | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Thomas Booth
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of NeuroradiologyKing's College Hospital NHS Foundation TrustLondonUK
| | | | | | - Marek Chmelik
- Department of Technical Disciplines in Medicine, Faculty of Health CareUniversity of PrešovPrešovSlovakia
| | - Patricia Clement
- Department of Diagnostic SciencesGhent UniversityGhentBelgium
- Department of Medical ImagingGhent University HospitalGhentBelgium
| | - Ece Ercan
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Maria A. Fernández‐Seara
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Julia Furtner
- Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Research Center of Medical Image Analysis and Artificial IntelligenceDanube Private UniversityKrems an der DonauAustria
| | - Elies Fuster‐Garcia
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y ComunicacionesUniversitat Politècnica de ValènciaValenciaSpain
| | - Matthew Grech‐Sollars
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
| | - Nazmiye Tugay Guven
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Gokce Hale Hatay
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Golestan Karami
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Vera C. Keil
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Mina Kim
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
| | - Johan A. F. Koekkoek
- Department of NeurologyLeiden University Medical CenterLeidenThe Netherlands
- Department of NeurologyHaaglanden Medical CenterThe HagueThe Netherlands
| | - Simran Kukran
- Department of BioengineeringImperial College LondonLondonUK
- Department of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, Institute of NeurologyUniversity College LondonLondonUK
| | - Ruben Emanuel Nechifor
- Department of Clinical Psychology and PsychotherapyInternational Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes‐Bolyai UniversityCluj‐NapocaRomania
| | - Alpay Özcan
- Electrical and Electronics Engineering DepartmentBogazici University IstanbulIstanbulTurkey
| | - Esin Ozturk‐Isik
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Senol Piskin
- Department of Mechanical Engineering, Faculty of Natural Sciences and EngineeringIstinye University IstanbulIstanbulTurkey
| | - Kathleen Schmainda
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Siri F. Svensson
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
- Department of PhysicsUniversity of OsloOsloNorway
| | - Chih‐Hsien Tseng
- Medical Delta FoundationDelftThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Saritha Unnikrishnan
- Faculty of Engineering and DesignAtlantic Technological University (ATU) SligoSligoIreland
- Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), ATU SligoSligoIreland
| | - Frans Vos
- Medical Delta FoundationDelftThe Netherlands
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Esther Warnert
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
- Stanford Cardiovascular InstituteStanford UniversityStanfordCaliforniaUSA
| | - Radim Jancalek
- Department of NeurosurgerySt. Anne's University Hospital, BrnoBrnoCzech Republic
- Faculty of Medicine, Masaryk UniversityBrnoCzech Republic
| | - Teresa Nunes
- Department of NeuroradiologyHospital Garcia de OrtaAlmadaPortugal
| | - Kyrre E. Emblem
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
| | - Marion Smits
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Brain Tumour CentreErasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Jan Petr
- Helmholtz‐Zentrum Dresden‐RossendorfInstitute of Radiopharmaceutical Cancer ResearchDresdenGermany
| | - Gilbert Hangel
- Department of NeurosurgeryMedical University of ViennaViennaAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging BiomarkersViennaAustria
- Medical Imaging ClusterMedical University of ViennaViennaAustria
| |
Collapse
|
|
10
|
Mourad C, Cosentino A, Nicod Lalonde M, Omoumi P. Advances in Bone Marrow Imaging: Strengths and Limitations from a Clinical Perspective. Semin Musculoskelet Radiol 2023; 27:3-21. [PMID: 36868241 PMCID: PMC9984270 DOI: 10.1055/s-0043-1761612] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Conventional magnetic resonance imaging (MRI) remains the modality of choice to image bone marrow. However, the last few decades have witnessed the emergence and development of novel MRI techniques, such as chemical shift imaging, diffusion-weighted imaging, dynamic contrast-enhanced MRI, and whole-body MRI, as well as spectral computed tomography and nuclear medicine techniques. We summarize the technical bases behind these methods, in relation to the common physiologic and pathologic processes involving the bone marrow. We present the strengths and limitations of these imaging methods and consider their added value compared with conventional imaging in assessing non-neoplastic disorders like septic, rheumatologic, traumatic, and metabolic conditions. The potential usefulness of these methods to differentiate between benign and malignant bone marrow lesions is discussed. Finally, we consider the limitations hampering a more widespread use of these techniques in clinical practice.
Collapse
Affiliation(s)
- Charbel Mourad
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,Department of Diagnostic and Interventional Radiology, Hôpital Libanais Geitaoui- CHU, Beyrouth, Lebanon
| | - Aurelio Cosentino
- Department of Radiology, Hôpital Riviera-Chablais, Vaud-Valais, Rennaz, Switzerland
| | - Marie Nicod Lalonde
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| |
Collapse
|
|
11
|
Makihara K, Yamaguchi M, Ito K, Sakaguchi K, Hori Y, Semba T, Funahashi Y, Fujii H, Terada Y. New Cluster Analysis Method for Quantitative Dynamic Contrast-Enhanced MRI Assessing Tumor Heterogeneity Induced by a Tumor-Microenvironmental Ameliorator (E7130) Treatment to a Breast Cancer Mouse Model. J Magn Reson Imaging 2022; 56:1820-1831. [PMID: 35524730 DOI: 10.1002/jmri.28226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/23/2022] [Accepted: 04/25/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can provide insight into tumor perfusion. However, a method that can quantitatively measure the intra-tumor distribution of tumor voxel clusters with a distinct range of Ktrans and ve values remains insufficiently explored. HYPOTHESIS Two-dimensional cluster analysis may quantify the distribution of a tumor voxel subregion with a distinct range of Ktrans and ve values in human breast cancer xenografts. STUDY TYPE Prospective longitudinal study. ANIMAL MODEL Twenty-two female athymic nude mice with MCF-7 xenograft, treated with E7130, a tumor-microenvironmental ameliorator, or saline. FIELD STRENGTH/SEQUENCE 9.4 Tesla, turbo rapid acquisition with relaxation enhancement, and spoiled gradient-echo sequences. ASSESSMENT We performed two-dimensional k-means clustering to identify tumor voxel clusters with a distinct range of Ktrans and ve values on Days 0, 2, and 5 after treatment, calculated the ratio of the number of tumor voxels in each cluster to the total number of tumor voxels, and measured the normalized distances defined as the ratio of the distance between each tumor voxel and the nearest tumor margin to a tumor radius. STATISTICAL TESTS Unpaired t-tests, Dunnett's multiple comparison tests, and Chi-squared test were used. RESULTS The largest and second largest clusters constituted 44.4% and 27.5% of all tumor voxels with cluster centroid values of Ktrans at 0.040 min-1 and 0.116 min-1 , and ve at 0.131 and 0.201, respectively. At baseline (Day 0), the average normalized distances for the largest and second largest clusters were 0.33 and 0.24, respectively. E7130-treated group showed the normalized distance of the initial largest cluster decreasing to 0.25, while that of the second largest cluster increasing to 0.31. Saline-treated group showed no change. DATA CONCLUSION A two-dimensional cluster analysis might quantify the spatial distribution of a tumor subregion with a distinct range of Ktrans and ve values. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 1.
Collapse
Affiliation(s)
- Kazuyuki Makihara
- Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan.,Division of Functional Imaging, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Masayuki Yamaguchi
- Division of Functional Imaging, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Ken Ito
- Division of Functional Imaging, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan.,Oncology Tsukuba Research Development, Discovery, Medicine Creation, Eisai Co., Ltd., Tsukuba-shi, Japan
| | - Kazuya Sakaguchi
- Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan.,Division of Functional Imaging, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Yusaku Hori
- Division of Functional Imaging, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan.,Oncology Tsukuba Research Development, Discovery, Medicine Creation, Eisai Co., Ltd., Tsukuba-shi, Japan
| | - Taro Semba
- Oncology Tsukuba Research Development, Discovery, Medicine Creation, Eisai Co., Ltd., Tsukuba-shi, Japan
| | - Yasuhiro Funahashi
- Lenvima Co-Global Lead, Oncology Business Group, Eisai Co., Ltd., Woodcliff Lake, New Jersey, USA
| | - Hirofumi Fujii
- Division of Functional Imaging, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Yasuhiko Terada
- Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan.,Division of Functional Imaging, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| |
Collapse
|
|
12
|
Thawani R, Gao L, Mohinani A, Tudorica A, Li X, Mitri Z, Huang W. Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study. BMC Med Imaging 2022; 22:182. [PMID: 36266631 PMCID: PMC9585714 DOI: 10.1186/s12880-022-00908-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 09/15/2022] [Accepted: 09/30/2022] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT. METHODS Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the Ktrans, ve, kep, and τi parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of all MRI metrics. RESULTS Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT Ktrans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965. CONCLUSION Accurate prediction of recurrence pre- and/or post-NACT through integration of imaging markers and clinicopathological variables may help improve clinical decision making in adjusting NACT and/or adjuvant treatment regimens to reduce the risk of recurrence and improve survival outcome.
Collapse
Affiliation(s)
- Rajat Thawani
- Division of Hematology and Oncology, Knight Cancer Institute, Oregon Health & Science University, Sam Jackson Park Road, OCH14110, 97239, Portland, OR, US.
| | - Lina Gao
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Ajay Mohinani
- Department of Internal Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Alina Tudorica
- Department of Radiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Zahi Mitri
- Division of Hematology and Oncology, Knight Cancer Institute, Oregon Health & Science University, Sam Jackson Park Road, OCH14110, 97239, Portland, OR, US
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| |
Collapse
|
|
13
|
Ramtohul T, Tescher C, Vaflard P, Cyrta J, Girard N, Malhaire C, Tardivon A. Prospective Evaluation of Ultrafast Breast MRI for Predicting Pathologic Response after Neoadjuvant Therapies. Radiology 2022; 305:565-574. [PMID: 35880977 DOI: 10.1148/radiol.220389] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Ultrafast dynamic contrast-enhanced (DCE) MRI parameters are associated with breast cancer aggressiveness. However, the role of these parameters as predictive biomarkers for pathologic response after neoadjuvant chemotherapy (NAC) has been poorly investigated. Purpose To assess whether semiquantitative perfusion parameters calculated at initial ultrafast DCE MRI are associated with early prediction for pathologic response after NAC in participants with breast cancer. Materials and Methods This prospective single-center study included consecutive women with nonmetastatic invasive breast cancer treated with NAC followed by surgery who underwent initial ultrafast DCE MRI between December 2020 and August 2021. Six semiquantitative ultrafast DCE MRI parameters were calculated for each participant from the fitted time-signal intensity curve. Multivariable logistic regression was used to identify independent predictors of pathologic complete response (pCR) and residual cancer burden (RCB). Results Fifty women (mean age, 49 years ± 12 [SD]) were included in the study; 20 achieved pCR and 25 achieved low RCB (RCB-0 and I). A wash-in slope (WIS) cutoff value of 1.6% per second had a sensitivity of 94% (17 of 18 participants) and a specificity of 59% (19 of 32 participants) for pCR. A WIS of more than 1.6% per second (odds ratio [OR], 8.4 [95% CI: 1.5, 48.2]; P = .02), human epidermal growth factor receptor 2 (HER2) positivity (OR, 6.3 [95% CI: 1.5, 27.4]; P = .01), and tumor-infiltrating lymphocytes of more than 10% (OR, 6.9 [95% CI: 1.3, 37.7]; P = .03) were independent predictive factors of pCR. The area under the receiver operating characteristic curve of the three-component model, which included WIS, tumor-infiltrating lymphocytes, and HER2 positivity, was 0.92 (95% CI: 0.84, 0.99). A WIS of more than 1.6% per second was associated with higher pCR rates in the HER2-positive (OR, 21.7 [95% CI: 1.8, 260.6]; P = .02) breast cancer subgroup. For luminal HER2-negative and triple-negative breast cancers, a WIS of more than 1.6% per second was associated with low RCB (OR, 11.0 [95% CI: 1.1, 106.4]; P = .04). Conclusion The wash-in slope (WIS) assessment at initial ultrafast dynamic contrast-enhanced MRI may be used to predict pathologic complete response (pCR) in participants with breast cancer. The WIS value was used to identify two subsets of human epidermal growth factor receptor 2-positive cancers with distinct pCR rates. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.
Collapse
Affiliation(s)
- Toulsie Ramtohul
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Clara Tescher
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Pauline Vaflard
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Joanna Cyrta
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Noémie Girard
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Caroline Malhaire
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Anne Tardivon
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| |
Collapse
|
|
14
|
Wang R, Quan Z, Zheng T, Wang K, Liu Y, Han Z, Wang X, Ma S, Liu L, Lau WY, Sun X. Pathophysiological mechanisms of ALPPS: experimental model. Br J Surg 2022; 109:510-519. [PMID: 35576390 DOI: 10.1093/bjs/znac007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 01/05/2022] [Accepted: 01/05/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND Associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) is a two-stage strategy that may increase hepatic tumour resectability and reduce postoperative liver failure rate by inducing rapid hypertrophy of the future liver remnant (FLR). Pathophysiological mechanisms after the first stage of ALPPS are poorly understood. METHODS An ALPPS model was established in rabbits with liver VX2 tumour. The pathophysiological mechanisms after the first stage of ALPPS in the FLR and tumour were assessed by multiplexed positron emission tomography (PET) tracers, dynamic contrast-enhanced MRI (DCE-MRI) and histopathology. RESULTS Tumour volume in the ALPPS model differed from post-stage 1 ALPPS at day 14 compared to control animals. 18F-FDG uptake of tumour increased from day 7 onwards in the ALPPS model. Valid volumetric function measured by 18F-methylcholine PET showed good values in accurately monitoring dynamics and time window for functional liver regeneration (days 3 to 7). DCE-MRI revealed changes in the vascular hyperpermeability function, with a peak on day 7 for tumour and FLR. CONCLUSION Molecular and functional imaging are promising non-invasive methods to investigate the pathophysiological mechanisms of ALPPS with potential for clinical application.
Collapse
Affiliation(s)
- Ruifeng Wang
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China
- Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, China
- Department of Gastroenterology, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
| | - Zhen Quan
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China
- Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, China
| | - Tongsen Zheng
- Department of Gastrointestinal Medical Oncology, The Affiliated Tumour Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
| | - Kai Wang
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China
- Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, China
| | - Yang Liu
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China
- Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, China
| | - Zhaoguo Han
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China
- Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, China
- Biomedical Research Imaging Center, Department of Radiology, and UNC Lineberger Comprehensive Cancer Center, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Xiance Wang
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China
- Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, China
| | - Shiling Ma
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China
- Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, China
| | - Lianxin Liu
- Department of Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, Heilongjiang Province 150001, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Wan Yee Lau
- Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR 999077, China
| | - Xilin Sun
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China
- Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, China
| |
Collapse
|
|
15
|
Rata M, Khan K, Collins DJ, Koh DM, Tunariu N, Bali MA, d'Arcy J, Winfield JM, Picchia S, Valeri N, Chau I, Cunningham D, Fassan M, Leach MO, Orton MR. DCE-MRI is more sensitive than IVIM-DWI for assessing anti-angiogenic treatment-induced changes in colorectal liver metastases. Cancer Imaging 2021; 21:67. [PMID: 34924031 PMCID: PMC8684660 DOI: 10.1186/s40644-021-00436-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 11/24/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Diffusion weighted imaging (DWI) with intravoxel incoherent motion (IVIM) modelling can inform on tissue perfusion without exogenous contrast administration. Dynamic-contrast-enhanced (DCE) MRI can also characterise tissue perfusion, but requires a bolus injection of a Gadolinium-based contrast agent. This study compares the use of DCE-MRI and IVIM-DWI methods in assessing response to anti-angiogenic treatment in patients with colorectal liver metastases in a cohort with confirmed treatment response. METHODS This prospective imaging study enrolled 25 participants with colorectal liver metastases to receive Regorafenib treatment. A target metastasis > 2 cm in each patient was imaged before and at 15 days after treatment on a 1.5T MR scanner using slice-matched IVIM-DWI and DCE-MRI protocols. MRI data were motion-corrected and tumour volumes of interest drawn on b=900 s/mm2 diffusion-weighted images were transferred to DCE-MRI data for further analysis. The median value of four IVIM-DWI parameters [diffusion coefficient D (10-3 mm2/s), perfusion fraction f (ml/ml), pseudodiffusion coefficient D* (10-3 mm2/s), and their product fD* (mm2/s)] and three DCE-MRI parameters [volume transfer constant Ktrans (min-1), enhancement fraction EF (%), and their product KEF (min-1)] were recorded at each visit, before and after treatment. Changes in pre- and post-treatment measurements of all MR parameters were assessed using Wilcoxon signed-rank tests (P<0.05 was considered significant). DCE-MRI and IVIM-DWI parameter correlations were evaluated with Spearman rank tests. Functional MR parameters were also compared against Response Evaluation Criteria In Solid Tumours v.1.1 (RECIST) evaluations. RESULTS Significant treatment-induced reductions of DCE-MRI parameters across the cohort were observed for EF (91.2 to 50.8%, P<0.001), KEF (0.095 to 0.045 min-1, P<0.001) and Ktrans (0.109 to 0.078 min-1, P=0.002). For IVIM-DWI, only D (a non-perfusion parameter) increased significantly post treatment (0.83 to 0.97 × 10-3 mm2/s, P<0.001), while perfusion-related parameters showed no change. No strong correlations were found between DCE-MRI and IVIM-DWI parameters. A moderate correlation was found, after treatment, between Ktrans and D* (r=0.60; P=0.002) and fD* (r=0.67; P<0.001). When compared to RECIST v.1.1 evaluations, KEF and D correctly identified most clinical responders, whilst non-responders were incorrectly identified. CONCLUSION IVIM-DWI perfusion-related parameters showed limited sensitivity to the anti-angiogenic effects of Regorafenib treatment in colorectal liver metastases and showed low correlation with DCE-MRI parameters, despite profound and significant post-treatment reductions in DCE-MRI measurements. TRIAL REGISTRATION NCT03010722 clinicaltrials.gov; registration date 6th January 2015.
Collapse
Affiliation(s)
- Mihaela Rata
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom.
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
- Royal Marsden NHS Foundation Trust & Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK.
| | - Khurum Khan
- Department of Medicine, GI and Lymphoma Unit, The Royal Marsden NHS Foundation Trust, London and Sutton, United Kingdom
| | - David J Collins
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Dow-Mu Koh
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Nina Tunariu
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Maria Antonietta Bali
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - James d'Arcy
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK National Cancer Imaging Translational Accelerator (NCITA), London, United Kingdom
| | - Jessica M Winfield
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Simona Picchia
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Nicola Valeri
- Department of Medicine, GI and Lymphoma Unit, The Royal Marsden NHS Foundation Trust, London and Sutton, United Kingdom
- Centre for Evolution and Cancer, The Institute of Cancer Research, London and Sutton, United Kingdom
- Division of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Ian Chau
- Department of Medicine, GI and Lymphoma Unit, The Royal Marsden NHS Foundation Trust, London and Sutton, United Kingdom
| | - David Cunningham
- Department of Medicine, GI and Lymphoma Unit, The Royal Marsden NHS Foundation Trust, London and Sutton, United Kingdom
| | - Matteo Fassan
- Department of Medicine (DIMED), Surgical Pathology Unit, University of Padua, Padua, Italy
- Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Martin O Leach
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Matthew R Orton
- Department of Radiology, MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| |
Collapse
|
|
16
|
Qiu J, Tao ZC, Deng KX, Wang P, Chen CY, Xiao F, Luo Y, Yuan SY, Chen H, Huang H. Diagnostic accuracy of dynamic contrast-enhanced magnetic resonance imaging for distinguishing pseudoprogression from glioma recurrence: a meta-analysis. Chin Med J (Engl) 2021; 134:2535-2543. [PMID: 34748524 PMCID: PMC8577681 DOI: 10.1097/cm9.0000000000001445] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND It is crucial to differentiate accurately glioma recurrence and pseudoprogression which have entirely different prognosis and require different treatment strategies. This study aimed to assess the diagnostic accuracy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a tool for distinguishing glioma recurrence and pseudoprogression. METHODS According to particular criteria of inclusion and exclusion, related studies up to May 1, 2019, were thoroughly searched from several databases including PubMed, Embase, Cochrane Library, and Chinese biomedical databases. The quality assessment of diagnostic accuracy studies was applied to evaluate the quality of the included studies. By using the "mada" package in R, the heterogeneity, overall sensitivity, specificity, and diagnostic odds ratio were calculated. Moreover, funnel plots were used to visualize and estimate the publication bias in this study. The area under the summary receiver operating characteristic (SROC) curve was computed to display the diagnostic efficiency of DCE-MRI. RESULTS In the present meta-analysis, a total of 11 studies covering 616 patients were included. The results showed that the pooled sensitivity, specificity, and diagnostic odds ratio were 0.792 (95% confidence interval [CI] 0.707-0.857), 0.779 (95% CI 0.715-0.832), and 16.219 (97.5% CI 9.123-28.833), respectively. The value of the area under the SROC curve was 0.846. In addition, the SROC curve showed high sensitivities (>0.6) and low false positive rates (<0.5) from most of the included studies, which suggest that the results of our study were reliable. Furthermore, the funnel plot suggested the existence of publication bias. CONCLUSIONS While the DCE-MRI is not the perfect diagnostic tool for distinguishing glioma recurrence and pseudoprogression, it was capable of improving diagnostic accuracy. Hence, further investigations combining DCE-MRI with other imaging modalities are required to establish an efficient diagnostic method for glioma patients.
Collapse
Affiliation(s)
- Jun Qiu
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Zhen-Chao Tao
- Department of Radiation Oncology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Ke-Xue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Peng Wang
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Chuan-Yu Chen
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Fang Xiao
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Yi Luo
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Shu-Ya Yuan
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Hao Chen
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Huan Huang
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| |
Collapse
|
|
17
|
Kousi E, Messiou C, Miah A, Orton M, Haas R, Thway K, Hopkinson G, Zaidi S, Smith M, Barquin E, Moskovic E, Fotiadis N, Strauss D, Hayes A, Schmidt MA. Descriptive analysis of MRI functional changes occurring during reduced dose radiotherapy for myxoid liposarcomas. Br J Radiol 2021; 94:20210310. [PMID: 34545764 PMCID: PMC9328045 DOI: 10.1259/bjr.20210310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES Myxoid liposarcomas (MLS) show enhanced response to radiotherapy due to their distinctive vascular pattern and therefore could be effectively treated with lower radiation doses. This is a descriptive study to explore the use of functional MRI to identify response in a uniform cohort of MLS patients treated with reduced dose radiotherapy. METHODS 10 patients with MLS were imaged pre-, during, and post-radiotherapy receiving reduced dose radiotherapy and the response to treatment was histopathologically assessed post-radiotherapy. Apparent diffusion coefficient (ADC), T2* relaxation time, volume transfer constant (Ktrans), initial area under the gadolinium curve over 60 s (IAUGC60) and (Gd) were estimated for a central tumour volume. RESULTS All parameters showed large inter- and intrasubject variabilities. Pre-treatment (Gd), IAUGC60 and Ktrans were significantly different between responders and non-responders. Post-radiotherapy reductions from baseline were demonstrated for T2*, (Gd), IAUGC60 and Ktrans for responders. No statistically significant ADC differences were demonstrated between the two response groups. Significantly greater early tumour volume reductions were observed for responders. CONCLUSIONS MLS are heterogenous lesions, characterised by a slow gradual contrast-agent uptake. Pre-treatment vascular parameters, early changes to tumour volume, vascular parameters and T2* have potential in identifying response to treatment. The delayed (Gd) is a suitable descriptive parameter, relying simply on T1 measurements. Volume changes precede changes in MLS functionality and could be used to identify early response. ADVANCES IN KNOWLEDGE MLS are are characterised by slow gradual contrast-agent uptake. Measurement of the delayed contrast-agent uptake (Gd) is simple to implement and able to discriminate response.
Collapse
Affiliation(s)
- Evanthia Kousi
- MRI unit, The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, UK
| | - Christina Messiou
- Radiology department, The Royal Marsden NHS Foundation Trust, London, UK
| | - Aisha Miah
- Sarcoma Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - Matthew Orton
- MRI unit, The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, UK
| | - Rick Haas
- Sarcoma Unit, Department of Radiotherapy, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Radiotherapy, Leiden University Medical Center, Leiden, The Netherlands
| | - Khin Thway
- Molecular pathology, The Royal Marsden NHS Foundation Trust, London, UK
| | - Georgina Hopkinson
- MRI unit, The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, UK
| | - Shane Zaidi
- Sarcoma Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - Myles Smith
- Sarcoma Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | | | - Eleanor Moskovic
- Radiology department, The Royal Marsden NHS Foundation Trust, London, UK
| | - Nicos Fotiadis
- Department of Interventional Radiology, The Royal Marsden NHS Foundation trust, London, UK
| | - Dirk Strauss
- Sarcoma/Melanoma Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - Andrew Hayes
- Sarcoma/Melanoma Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - Maria A Schmidt
- MRI unit, The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, UK
| |
Collapse
|
|
18
|
Quantitative dynamic contrast-enhanced MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging in differentiating parotid gland tumors. Neuroradiology 2021; 63:1709-1719. [PMID: 34241661 DOI: 10.1007/s00234-021-02758-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 06/20/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE To evaluate the ability of quantitative dynamic contrast-enhanced (DCE)-MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging (RESOLVE-DWI) in differentiating parotid tumors (PTs) with different histological types. METHODS In this retrospective study, 123 patients with 145 histologically proven PTs who underwent both RESOLVE-DWI and DCE-MRI were enrolled including 51 pleomorphic adenomas (PAs), 52 Warthin's tumors (WTs), 27 other benign neoplasms (OBNs), and 15 malignant tumors (MTs). Quantitative parameters of DCE-MRI (Ktrans, Kep, and Ve) and the apparent diffusion coefficient (ADC) of lesions were calculated and analyzed. Kruskal-Wallis tests with Dunn-Bonferroni correction, logistic regression analyses, and receiver operating characteristic curve were used for statistical analyses. RESULTS PAs exhibited a lowest Ktrans among these four PTs. WTs demonstrated the highest Kep and lowest Ve values. WTs and MTs showed lower ADCmin values than PAs and OBNs. The combination of Kep and Ve provided 98.1% sensitivity, 85% specificity, and 98.7% accuracy for differentiating WTs from the other three PTs. The ADCmin cutoff value of ≤ 0.826 yielded 80.0% sensitivity, 92.3% specificity, and 90.3% accuracy for the differentiation of MTs from PAs and OBNs. Ktrans with a cutoff value of ≤ 0.185 achieved a sensitivity, specificity, and accuracy of 84.3, 70.4, and 79.5%, respectively, for discriminating PAs from OBNs. CONCLUSION The combination of quantitative DCE-MRI and RESOLVE-DWI is beneficial for characterizing four histological types of PTs.
Collapse
|
|
19
|
Kim H, Jin S, Choi H, Kang M, Park SG, Jun H, Cho H, Kang S. Target-switchable Gd(III)-DOTA/protein cage nanoparticle conjugates with multiple targeting affibody molecules as target selective T 1 contrast agents for high-field MRI. J Control Release 2021; 335:269-280. [PMID: 34044091 DOI: 10.1016/j.jconrel.2021.05.029] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 12/15/2022]
Abstract
Magnetic resonance imaging (MRI) is a non-invasive in vivo imaging tool, providing high enough spatial resolution to obtain both the anatomical and the physiological information of patients. However, MRI generally suffers from relatively low sensitivity often requiring the aid of contrast agents (CA) to enhance the contrast of vessels and/or the tissues of interest from the background. The targeted delivery of diagnostic probes to the specific lesion is a powerful approach for early diagnosis and signal enhancement leading to the effective treatment of various diseases. Here, we established targeting ligand switchable nanoplatforms using lumazine synthase protein cage nanoparticles derived from Aquifex aeolicus (AaLS) by genetically introducing the SpyTag peptide (ST) to the C-terminus of the AaLS subunits to form an ST-displaying AaLS (AaLS-ST). Conversely, multiple targeting ligands were constructed by genetically fusing SpyCatcher protein (SC) to either HER2 or EGFR targeting affibody molecules (SC-HER2Afb or SC-EGFRAfb). Gd(III)-DOTA complexes were chemically attached to the AaLS-ST and the external surface of the Gd(III)-DOTA conjugated AaLS-ST (Gd(III)-DOTA-AaLS-ST) were successfully decorated with either the HER2Afb or the EGFRAfb. The resulting Gd(III)-DOTA-AaLS/HER2Afb and Gd(III)-DOTA-AaLS/EGFR2Afb exhibited high r1 relaxivity values of 57 and 25 mM-1 s-1 at 1.4 and 7 T, respectively, which were 10-fold or higher than those of the clinically used Dotarem. Their target-selective contrast enhancements were confirmed with in vitro cell-based MRI scans and the in vivo MR imaging of tumor-bearing mouse models at 7 T. A target-switchable AaLS-based nanoplatform that was developed in this study might serve as a promising T1 CA developing platform at a high magnetic field to detect various tumor sites in a target-specific manner in future clinical applications.
Collapse
Affiliation(s)
- Hansol Kim
- Department of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Seokha Jin
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Hyukjun Choi
- Department of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - MungSoo Kang
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Seong Guk Park
- Department of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Heejin Jun
- Department of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - HyungJoon Cho
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.
| | - Sebyung Kang
- Department of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.
| |
Collapse
|
|
20
|
Gwilliam MN, Collins DJ, Leach MO, Orton MR. Quantifying MRI T1 relaxation in flowing blood: implications for arterial input function measurement in DCE-MRI. Br J Radiol 2021; 94:20191004. [PMID: 33507818 PMCID: PMC8011233 DOI: 10.1259/bjr.20191004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To investigate the feasibility of accurately quantifying the concentration of MRI contrast agent in flowing blood by measuring its T1 in a large vessel. Such measures are often used to obtain patient-specific arterial input functions for the accurate fitting of pharmacokinetic models to dynamic contrast enhanced MRI data. Flow is known to produce errors with this technique, but these have so far been poorly quantified and characterised in the context of pulsatile flow with a rapidly changing T1 as would be expected in vivo. METHODS A phantom was developed which used a mechanical pump to pass fluid at physiologically relevant rates. Measurements of T1 were made using high temporal resolution gradient recalled sequences suitable for DCE-MRI of both constant and pulsatile flow. These measures were used to validate a virtual phantom that was then used to simulate the expected errors in the measurement of an AIF in vivo. RESULTS The relationship between measured T1 values and flow velocity was found to be non-linear. The subsequent error in quantification of contrast agent concentration in a measured AIF was shown. CONCLUSIONS The T1 measurement of flowing blood using standard DCE- MRI sequences are subject to large measurement errors which are non-linear in relation to flow velocity. ADVANCES IN KNOWLEDGE This work qualitatively and quantitatively demonstrates the difficulties of accurately measuring the T1 of flowing blood using DCE-MRI over a wide range of physiologically realistic flow velocities and pulsatilities. Sources of error are identified and proposals made to reduce these.
Collapse
Affiliation(s)
- Matthew N Gwilliam
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| | - David J Collins
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| | - Martin O Leach
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| | - Matthew R Orton
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| |
Collapse
|
|
21
|
Liu YJ, Yang HT, Yao MMS, Lin SC, Cho DY, Shen WC, Juan CJ, Chan WP. Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function. Sci Rep 2021; 11:2920. [PMID: 33536471 PMCID: PMC7859214 DOI: 10.1038/s41598-021-82300-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 01/19/2021] [Indexed: 11/09/2022] Open
Abstract
The purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters Ktrans, ve, and vp, derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal–Wallis test with a Dunn–Bonferroni post hoc analysis were performed. In simulation, Ktrans and ve increased as the peak in the AIF decreased, but vp increased when delay time in the AIF increased. In humans, the estimated Ktrans and ve were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.
Collapse
Affiliation(s)
- Yi-Jui Liu
- Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan.,Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan
| | - Hou-Ting Yang
- Ph.D. Program in Electrical and Communication Engineering in Feng Chia University, Taichung, Taiwan.,Department of Nuclear Medicine, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Melissa Min-Szu Yao
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, 111 Hsing-Long Road, Section 3, Taipei, 116, Taiwan.,Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Shao-Chieh Lin
- Ph.D. Program in Electrical and Communication Engineering in Feng Chia University, Taichung, Taiwan
| | - Der-Yang Cho
- Department of Neurosurgery, China Medical University Hospital, Taichung, Taiwan
| | - Wu-Chung Shen
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan
| | - Chun-Jung Juan
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan. .,Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan. .,Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County, 302, Taiwan.
| | - Wing P Chan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, 111 Hsing-Long Road, Section 3, Taipei, 116, Taiwan. .,Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| |
Collapse
|
|
22
|
Abstract
Perfusion imaging allows for the quantitative extraction of physiological perfusion parameters of the liver microcirculation at levels far below the spatial the resolution of CT and MR imaging. Because of its peculiar structure and architecture, perfusion imaging is more challenging in the liver than in other organs. Indeed, the liver is a mobile organ and significantly deforms with respiratory motion. Moreover, it has a dual vascular supply and the sinusoidal capillaries are fenestrated in the normal liver. Using extracellular contrast agents, perfusion imaging has shown its ability to discriminate patients with various stages of liver fibrosis. The recent introduction of hepatobiliary contrast agents enables quantification of both the liver perfusion and the hepatocyte transport function using advanced perfusion models. The purpose of this review article is to describe the characteristics of liver perfusion imaging to assess chronic liver disease, with a special focus on CT and MR imaging.
Collapse
|
|
23
|
Bendau E, Smith J, Zhang L, Ackerstaff E, Kruchevsky N, Wu B, Koutcher JA, Alfano R, Shi L. Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy. JOURNAL OF BIOPHOTONICS 2020; 13:e202000005. [PMID: 32219996 PMCID: PMC7433748 DOI: 10.1002/jbio.202000005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/17/2020] [Accepted: 03/18/2020] [Indexed: 05/10/2023]
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African-American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time-consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label-free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole-tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.
Collapse
Affiliation(s)
- Ethan Bendau
- Department of Biomedical Engineering, Columbia University, New York, New York
| | - Jason Smith
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | - Lin Zhang
- Institute for Ultrafast Spectroscopy and Lasers, The City College of New York, New York, New York
| | - Ellen Ackerstaff
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Natalia Kruchevsky
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Binlin Wu
- Physics Department, CSCU Center for Nanotechnology, Southern Connecticut State University, New Haven, Connecticut
| | - Jason A. Koutcher
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Medical Physics and Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Weill Cornell Medical College, Cornell University, New York, New York
| | - Robert Alfano
- Institute for Ultrafast Spectroscopy and Lasers, The City College of New York, New York, New York
| | - Lingyan Shi
- Department of Bioengineering, University of California, San Diego, La Jolla, California
| |
Collapse
|
|
24
|
Stringfield O, Arrington JA, Johnston SK, Rognin NG, Peeri NC, Balagurunathan Y, Jackson PR, Clark-Swanson KR, Swanson KR, Egan KM, Gatenby RA, Raghunand N. Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme. ACTA ACUST UNITED AC 2020; 5:135-144. [PMID: 30854451 PMCID: PMC6403044 DOI: 10.18383/j.tom.2018.00052] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain were used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that correspond to variations in blood flow, interstitial edema, and cellular density. We hypothesized that the distribution of these distinct tumor ecological "habitats" at the time of presentation will impact the course of the disease. We retrospectively analyzed initial MRI scans in 2 groups of patients diagnosed with GBM, a long-term survival group comprising subjects who survived >36 month postdiagnosis, and a short-term survival group comprising subjects who survived ≤19 month postdiagnosis. The single-institution discovery cohort contained 22 subjects in each group, while the multi-institution validation cohort contained 15 subjects per group. MRI voxel intensities were calibrated, and tumor voxels clustered on contrast-enhanced T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images into 6 distinct "habitats" based on low- to medium- to high-contrast enhancement and low-high signal on FLAIR scans. Habitat 6 (high signal on calibrated contrast-enhanced T1-weighted and FLAIR sequences) comprised a significantly higher volume fraction of tumors in the long-term survival group (discovery cohort, 35% ± 6.5%; validation cohort, 34% ± 4.8%) compared with tumors in the short-term survival group (discovery cohort, 17% ± 4.5%, P < .03; validation cohort, 16 ± 4.0%, P < .007). Of the 6 distinct MRI-defined habitats, the fractional tumor volume of habitat 6 at diagnosis was significantly predictive of long- or short-term survival. We discuss a possible mechanistic basis for this association and implications for habitat-driven adaptive therapy of GBM.
Collapse
Affiliation(s)
| | - John A Arrington
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Sandra K Johnston
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ.,Department of Radiology, University of Washington, Seattle, WA; and
| | | | - Noah C Peeri
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL
| | | | - Pamela R Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kamala R Clark-Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kathleen M Egan
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Robert A Gatenby
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Natarajan Raghunand
- Cancer Physiology, and.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| |
Collapse
|
|
25
|
Machireddy A, Thibault G, Tudorica A, Afzal A, Mishal M, Kemmer K, Naik A, Troxell M, Goranson E, Oh K, Roy N, Jafarian N, Holtorf M, Huang W, Song X. Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps. ACTA ACUST UNITED AC 2020; 5:90-98. [PMID: 30854446 PMCID: PMC6403033 DOI: 10.18383/j.tom.2018.00046] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (Ktrans), in the training and testing sets, respectively. The differences in AUC were statistically significant (P < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.
Collapse
Affiliation(s)
| | | | | | - Aneela Afzal
- Oregon Health and Science University, Portland, OR
| | - May Mishal
- Oregon Health and Science University, Portland, OR
| | | | - Arpana Naik
- Oregon Health and Science University, Portland, OR
| | | | | | - Karen Oh
- Oregon Health and Science University, Portland, OR
| | - Nicole Roy
- Oregon Health and Science University, Portland, OR
| | | | | | - Wei Huang
- Oregon Health and Science University, Portland, OR
| | - Xubo Song
- Oregon Health and Science University, Portland, OR
| |
Collapse
|
|
26
|
Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements Using Radiomics. J Thorac Imaging 2019; 34:103-115. [PMID: 30664063 DOI: 10.1097/rti.0000000000000390] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics.
Collapse
|
|
27
|
Wei L, Osman S, Hatt M, El Naqa I. Machine learning for radiomics-based multimodality and multiparametric modeling. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 63:323-338. [PMID: 31527580 DOI: 10.23736/s1824-4785.19.03213-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in oncology has been mainly related to combining anatomical and functional imaging to improve diagnostic specificity and/or target definition, such as positron emission tomography/computed tomography (PET/CT) and single-photon emission CT (SPECT)/CT. More recently, the fusion of various images, such as multiparametric magnetic resonance imaging (MRI) sequences, different PET tracer images, PET/MRI, has become more prevalent, which has enabled more comprehensive characterization of the tumor phenotype. In order to take advantage of these valuable multimodal data for clinical decision making using radiomics, we present two ways to implement the multimodal image analysis, namely radiomic (handcrafted feature) based and deep learning (machine learned feature) based methods. Applying advanced machine (deep) learning algorithms across multimodality images have shown better results compared with single modality modeling for prognostic and/or prediction of clinical outcomes. This holds great potentials for providing more personalized treatment for patients and achieve better outcomes.
Collapse
Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Sarah Osman
- Centre for Cancer Research and Cell Biology, Queens' University, Belfast, UK
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA -
| |
Collapse
|
|
28
|
Knight SP, Meaney JF, Fagan AJ. DCE‐MRI protocol for constraining absolute pharmacokinetic modeling errors within specific accuracy limits. Med Phys 2019; 46:3592-3602. [DOI: 10.1002/mp.13635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 04/30/2019] [Accepted: 05/21/2019] [Indexed: 01/01/2023] Open
Affiliation(s)
- Silvin P. Knight
- School of Medicine Trinity College University of Dublin Dublin Ireland
- National Centre for Advanced Medical Imaging (CAMI) St James's Hospital Dublin Ireland
| | - James F. Meaney
- School of Medicine Trinity College University of Dublin Dublin Ireland
- National Centre for Advanced Medical Imaging (CAMI) St James's Hospital Dublin Ireland
| | | |
Collapse
|
|
29
|
Haldorsen IS, Lura N, Blaakær J, Fischerova D, Werner HMJ. What Is the Role of Imaging at Primary Diagnostic Work-Up in Uterine Cervical Cancer? Curr Oncol Rep 2019; 21:77. [PMID: 31359169 PMCID: PMC6663927 DOI: 10.1007/s11912-019-0824-0] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE OF REVIEW For uterine cervical cancer, the recently revised International Federation of Gynecology and Obstetrics (FIGO) staging system (2018) incorporates imaging and pathology assessments in its staging. In this review we summarize the reported staging performances of conventional and novel imaging methods and provide an overview of promising novel imaging methods relevant for cervical cancer patient care. RECENT FINDINGS Diagnostic imaging during the primary diagnostic work-up is recommended to better assess tumor extent and metastatic disease and is now reflected in the 2018 FIGO stages 3C1 and 3C2 (positive pelvic and/or paraaortic lymph nodes). For pretreatment local staging, imaging by transvaginal or transrectal ultrasound (TVS, TRS) and/or magnetic resonance imaging (MRI) is instrumental to define pelvic tumor extent, including a more accurate assessment of tumor size, stromal invasion depth, and parametrial invasion. In locally advanced cervical cancer, positron emission tomography-computed tomography (PET-CT) or computed tomography (CT) is recommended, since the identification of metastatic lymph nodes and distant metastases has therapeutic consequences. Furthermore, novel imaging techniques offer visualization of microstructural and functional tumor characteristics, reportedly linked to clinical phenotype, thus with a potential for further improving risk stratification and individualization of treatment. Diagnostic imaging by MRI/TVS/TRS and PET-CT/CT is instrumental for pretreatment staging in uterine cervical cancer and guides optimal treatment strategy. Novel imaging techniques may also provide functional biomarkers with potential relevance for developing more targeted treatment strategies in cervical cancer.
Collapse
Affiliation(s)
- Ingfrid S Haldorsen
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Liesvei 65, Postbox 7800, 5021, Bergen, Norway.
- Section for Radiology, Department of Clinical Medicine, University of Bergen, 5020, Bergen, Norway.
| | - Njål Lura
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Liesvei 65, Postbox 7800, 5021, Bergen, Norway
| | - Jan Blaakær
- Department of Obstetrics and Gynaecology, Odense University Hospital, Odense, Denmark
| | - Daniela Fischerova
- Gynecological Oncology Centre, Department of Obstetrics and Gynaecology, First Faculty of Medicine, Charles University, General University Hospital in Prague, Prague, Czech Republic
| | - Henrica M J Werner
- Department of Obstetrics and Gynaecology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Clinical Science, University of Bergen, 5020, Bergen, Norway
| |
Collapse
|
|
30
|
Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, Huang W, Noworolski SM, Young RJ, Shiroishi MS, Kim H, Coolens C, Laue H, Chung C, Rosen M, Boss M, Jackson EF. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019; 49:e101-e121. [PMID: 30451345 PMCID: PMC6526078 DOI: 10.1002/jmri.26518] [Citation(s) in RCA: 256] [Impact Index Per Article: 42.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 09/06/2018] [Accepted: 09/06/2018] [Indexed: 12/14/2022] Open
Abstract
Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.
Collapse
Affiliation(s)
- Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nancy A. Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, USA
| | | | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | | | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Susan M. Noworolski
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Robert J. Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mark S. Shiroishi
- Division of Neuroradiology, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Harrison Kim
- Department of Radiology, University of Alabama at Birmingham, Birmingham AL, USA
| | - Catherine Coolens
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | | | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Mark Rosen
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Michael Boss
- Applied Physics Division, National Institute of Standards and Technology, Boulder, CO, USA
| | - Edward F. Jackson
- Departments of Medical Physics, Radiology, and Human Oncology, University of Wisconsin School of Medicine, Madison, WI, USA
| |
Collapse
|
|
31
|
Huang W, Chen Y, Fedorov A, Li X, Jajamovich GH, Malyarenko DI, Aryal MP, LaViolette PS, Oborski MJ, O'Sullivan F, Abramson RG, Jafari-Khouzani K, Afzal A, Tudorica A, Moloney B, Gupta SN, Besa C, Kalpathy-Cramer J, Mountz JM, Laymon CM, Muzi M, Kinahan PE, Schmainda K, Cao Y, Chenevert TL, Taouli B, Yankeelov TE, Fennessy F, Li X. The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II. Tomography 2019; 5:99-109. [PMID: 30854447 PMCID: PMC6403046 DOI: 10.18383/j.tom.2018.00027] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.
Collapse
Affiliation(s)
- Wei Huang
- Oregon Health and Science University, Portland, OR
| | - Yiyi Chen
- Oregon Health and Science University, Portland, OR
| | - Andriy Fedorov
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Xia Li
- General Electric Global Research, Niskayuna, NY
| | | | | | | | | | | | | | | | | | - Aneela Afzal
- Oregon Health and Science University, Portland, OR
| | | | | | | | - Cecilia Besa
- Icahn School of Medicine at Mt Sinai, New York, NY
| | | | | | | | - Mark Muzi
- University of Washington, Seattle, WA; and
| | | | | | - Yue Cao
- University of Michigan, Ann Arbor, MI
| | | | | | | | - Fiona Fennessy
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Xin Li
- Oregon Health and Science University, Portland, OR
| |
Collapse
|
|
32
|
Foltz W, Driscoll B, Laurence Lee S, Nayak K, Nallapareddy N, Fatemi A, Ménard C, Coolens C, Chung C. Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements. Tomography 2019; 5:77-89. [PMID: 30854445 PMCID: PMC6403037 DOI: 10.18383/j.tom.2019.00001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Accurate, patient-specific measurement of arterial input functions (AIF) may improve model-based analysis of vascular permeability. This study investigated factors affecting AIF measurements from magnetic resonance imaging (MRI) magnitude (AIFMAGN) and phase (AIFPHA) signals, and compared them against computed tomography (CT) (AIFCT), under controlled conditions relevant to clinical protocols using a multimodality flow phantom. The flow phantom was applied at flip angles of 20° and 30°, flow rates (3-7.5 mL/s), and peak bolus concentrations (0.5-10 mM), for in-plane and through-plane flow. Spatial 3D-FLASH signal and variable flip angle T1 profiles were measured to investigate in-flow and radiofrequency-related biases, and magnitude- and phase-derived Gd-DTPA concentrations were compared. MRI AIF performance was tested against AIFCT via Pearson correlation analysis. AIFMAGN was sensitive to imaging orientation, spatial location, flip angle, and flow rate, and it grossly underestimated AIFCT peak concentrations. Conversion to Gd-DTPA concentration using T1 taken at the same orientation and flow rate as the dynamic contrast-enhanced acquisition improved AIFMAGN accuracy; yet, AIFMAGN metrics remained variable and significantly reduced from AIFCT at concentrations above 2.5 mM. AIFPHA performed equivalently within 1 mM to AIFCT across all tested conditions. AIFPHA, but not AIFMAGN, reported equivalent measurements to AIFCT across the range of tested conditions. AIFPHA showed superior robustness.
Collapse
Affiliation(s)
- Warren Foltz
- Department of Medical Physics, Princess Margaret Cancer Center and University Health Network, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Brandon Driscoll
- Department of Medical Physics, Princess Margaret Cancer Center and University Health Network, Toronto, ON, Canada
| | | | - Krishna Nayak
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
| | - Naren Nallapareddy
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
| | - Ali Fatemi
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Cynthia Ménard
- Department of Radiation Oncology, Centre Hospitalier Universite de Montreal, Montreal, Canada
- Department of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada; and
| | - Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Center and University Health Network, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
- Department of Radiation Oncology, Centre Hospitalier Universite de Montreal, Montreal, Canada
- Department of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada; and
| | - Caroline Chung
- TECHNA Institute, University Health Network, Toronto, ON, Canada
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX
| |
Collapse
|
|
33
|
da Silva Neto OP, Araújo JDL, Caldas Oliveira AG, Cutrim M, Silva AC, Paiva AC, Gattass M. Pathophysiological mapping of tumor habitats in the breast in DCE-MRI using molecular texture descriptor. Comput Biol Med 2019; 106:114-125. [PMID: 30711799 DOI: 10.1016/j.compbiomed.2019.01.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 01/16/2019] [Accepted: 01/19/2019] [Indexed: 11/25/2022]
Abstract
BACKGROUND We propose a computational methodology capable of detecting and analyzing breast tumor habitats in images acquired by magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI), based on the pathophysiological behavior of the contrast agent (CA). METHODS The proposed methodology comprises three steps. In summary, the first step is the acquisition of images from the Quantitative Imaging Network Breast. In the second step, the segmentation of the breasts is performed to remove the background, noise, and other unwanted objects from the image. In the third step, the generation of habitats is performed by applying two techniques: the molecular texture descriptor (MTD) that highlights the CA regions in the breast, and pathophysiological texture mapping (MPT), which generates tumor habitats based on the behavior of the CA. The combined use of these two techniques allows the automatic detection of tumors in the breast and analysis of each separate habitat with respect to their malignancy type. RESULTS The results found in this study were promising, with 100% of breast tumors being identified. The segmentation results exhibited an accuracy of 99.95%, sensitivity of 71.07%, specificity of 99.98%, and volumetric similarity of 77.75%. Moreover, we were able to classify the malignancy of the tumors, with 6 classified as malignant type III (WashOut) and 14 as malignant type II (Plateau), for a total of 20 cases. CONCLUSION We proposed a method for the automatic detection of tumors in the breast in DCE-MRI and performed the pathophysiological mapping of tumor habitats by analyzing the behavior of the CA, combining MTD and MPT, which allowed the mapping of internal tumor habitats.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil.
| |
Collapse
|
|
34
|
Young TL, Zychowski KE, Denson JL, Campen MJ. Blood-brain barrier at the interface of air pollution-associated neurotoxicity and neuroinflammation. ROLE OF INFLAMMATION IN ENVIRONMENTAL NEUROTOXICITY 2019. [DOI: 10.1016/bs.ant.2018.10.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
|
35
|
Huang YT, Huang YL, Ng KK, Lin G. Current Status of Magnetic Resonance Imaging in Patients with Malignant Uterine Neoplasms: A Review. Korean J Radiol 2018; 20:18-33. [PMID: 30627019 PMCID: PMC6315066 DOI: 10.3348/kjr.2018.0090] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022] Open
Abstract
In this study, we summarize the clinical role of magnetic resonance imaging (MRI) in the diagnosis of patients with malignant uterine neoplasms, including leiomyosarcoma, endometrial stromal sarcoma, adenosarcoma, uterine carcinosarcoma, and endometrial cancer, with emphasis on the challenges and disadvantages. MRI plays an essential role in patients with uterine malignancy, for the purpose of tumor detection, primary staging, and treatment planning. MRI has advanced in scope beyond the visualization of the many aspects of anatomical structures, including diffusion-weighted imaging, dynamic contrast enhancement-MRI, and magnetic resonance spectroscopy. Emerging technologies coupled with the use of artificial intelligence in MRI are expected to lead to progressive improvement in case management of malignant uterine neoplasms.
Collapse
Affiliation(s)
- Yu-Ting Huang
- Department of Medical Imaging and Intervention, Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Department of Diagnostic Radiology, Chang Gung Memorial Hospital at Keelung, Keelung, Taiwan.,Gynecologic Cancer Research Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Yen-Ling Huang
- Department of Medical Imaging and Intervention, Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Gynecologic Cancer Research Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Koon-Kwan Ng
- Department of Medical Imaging and Intervention, Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Department of Diagnostic Radiology, Chang Gung Memorial Hospital at Keelung, Keelung, Taiwan.,Gynecologic Cancer Research Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Clinical Metabolomic Core Laboratory, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan, Taiwan.,Gynecologic Cancer Research Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| |
Collapse
|
|
36
|
El Rafei M, Teixeira P, Norberciak L, Badr S, Cotten A, Budzik JF. Dynamic contrast-enhanced MRI perfusion of normal muscle in adult hips: Variation of permeability and semi-quantitative parameters. Eur J Radiol 2018; 108:92-98. [PMID: 30396677 DOI: 10.1016/j.ejrad.2018.08.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 07/30/2018] [Accepted: 08/26/2018] [Indexed: 12/31/2022]
Abstract
The purpose of this prospective study was to ascertain the degree of variation of semi-quantitative and permeability parameters on DCE-MRI of normally appearing striated muscles. Dynamic contrast-enhanced MRI of the right hip was performed in 20 women and 24 men. Mean age was 39.1 ± 12.4 years. Two regions of interest (ROI) were drawn in twelve muscles of anterior, medial and gluteal compartments: a free-form ROI covering the largest muscle section and a smaller elliptical ROI. Semi-quantitative and permeability parameters were calculated using the extended Tofts model. Statistical analysis was performed with a linear mixed model to assess perfusion parameters variation. Intra- and inter-observer agreements were assessed. The intra-observer agreement was considered to be good for free-form ROI (minimum Intra-Class Coefficient (ICC) = 0.72) and moderate for elliptical ROI (minimum ICC = 0.51), while the inter-observer agreement was considered to be bad in both cases (minimum ICC = 0.11). There was a high inter-individual variation in most of the perfusion parameters evaluated. The average coefficients of variation were: Time To Peak = 9%, Area Under the Curve = 44%, Ve = 61%, Kep = 90%, Initial Slope = 99%, and Ktrans = 128%. A considerable variation in resting muscle perfusion parameters was seen. This could lead to errors in the analysis of muscle DCE-MRI studies or oncologic/non oncologic studies using muscle as a referential. Further studies targeted on acquisition protocols and post-processing software are necessary to improve the performance of muscle MR perfusion.
Collapse
Affiliation(s)
- Mazen El Rafei
- Lille Regional University Hospital, Musculoskeletal Imaging Department, University of Lille Nord de France, Lille, France.
| | - Pedro Teixeira
- Nancy Regional University Hospital, Imaging Department, University of Lorraine, Nancy, France.
| | - Laurène Norberciak
- Lille Catholic University Hospitals, Biostatistics Department, Lille Catholic University, Lille, France.
| | - Sammy Badr
- Lille Regional University Hospital, Musculoskeletal Imaging Department, University of Lille Nord de France, Lille, France; PMOI Physiopathology of Inflammatory Bone Diseases, EA 4490, University of Lille Nord de France, Lille, France.
| | - Anne Cotten
- Lille Regional University Hospital, Musculoskeletal Imaging Department, University of Lille Nord de France, Lille, France; PMOI Physiopathology of Inflammatory Bone Diseases, EA 4490, University of Lille Nord de France, Lille, France.
| | - Jean-François Budzik
- PMOI Physiopathology of Inflammatory Bone Diseases, EA 4490, University of Lille Nord de France, Lille, France; Lille Catholic University Hospitals, Imaging Department, Lille Catholic University, Lille, France.
| |
Collapse
|
|
37
|
Chen L, Zeng X, Wu Y, Yan X, Huang X, Chen H, Zhang J, Wang J, Feng L. A Study of the Correlation of Perfusion Parameters in High‐Resolution GRASP MRI With Microvascular Density in Lung Cancer. J Magn Reson Imaging 2018; 49:1186-1194. [PMID: 30390364 DOI: 10.1002/jmri.26340] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 08/27/2018] [Accepted: 08/27/2018] [Indexed: 02/06/2023] Open
Affiliation(s)
- Lihua Chen
- Department of RadiologySouthwest Hospital, Army Medical University (Third Military Medical University) Chongqing P.R. China
- Department of RadiologyPLA 101st Hospital Wuxi Jiangsu P.R. China
| | - Xianchun Zeng
- Department of RadiologyGuizhou Provincial People's Hospital Guizhou P.R. China
| | - Youli Wu
- Department of PathologySouthwest Hospital, Army Medical University (Third Military Medical University) Chongqing P.R. China
| | - Xiaochu Yan
- Department of PathologySouthwest Hospital, Army Medical University (Third Military Medical University) Chongqing P.R. China
| | - Xuequan Huang
- Department of RadiologySouthwest Hospital, Army Medical University (Third Military Medical University) Chongqing P.R. China
| | - Hui Chen
- Department of RadiologySouthwest Hospital, Army Medical University (Third Military Medical University) Chongqing P.R. China
| | - Jiuquan Zhang
- Department of RadiologyChongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital Chongqing P.R. China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University)Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital Chongqing P.R. China
| | - Jian Wang
- Department of RadiologySouthwest Hospital, Army Medical University (Third Military Medical University) Chongqing P.R. China
| | - Li Feng
- Department of Medical PhysicsMemorial Sloan Kettering Cancer Center New York New York USA
| |
Collapse
|
|
38
|
Fasmer KE, Bjørnerud A, Ytre-Hauge S, Grüner R, Tangen IL, Werner HMJ, Bjørge L, Salvesen ØO, Trovik J, Krakstad C, Haldorsen IS. Preoperative quantitative dynamic contrast-enhanced MRI and diffusion-weighted imaging predict aggressive disease in endometrial cancer. Acta Radiol 2018; 59:1010-1017. [PMID: 29137496 DOI: 10.1177/0284185117740932] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Background Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) may yield preoperative tumor biomarkers relevant for prognosis and therapy in cancer. Purpose To explore the value of preoperative DCE-MRI and DWI for the prediction of aggressive disease in endometrial cancer patients. Material and Methods Preoperative MRI (1.5-T) from 177 patients were analyzed and imaging parameters reflecting tumor microvasculature (from DCE-MRI) and tumor microstructure (from DWI) were estimated. The derived imaging parameters were explored in relation to clinico-pathological stage, histological subtype and grade, molecular markers, and patient outcome. Results Low tumor blood flow (Fb) and low rate constant for contrast agent intravasation (kep) were associated with high-risk histological subtype ( P ≤ 0.04 for both) and tended to be associated with poor prognosis ( P ≤ 0.09). Low tumor apparent diffusion coefficient (ADC) value and large tumor volume were both significantly associated with deep myometrial invasion ( P < 0.001 for both) and were also unfavorable prognostic factors ( P = 0.05 and P < 0.001, respectively). Conclusion DCE-MRI and DWI represent valuable supplements to conventional MRI by providing preoperative imaging biomarkers that predict aggressive disease in endometrial cancer patients.
Collapse
Affiliation(s)
- Kristine E Fasmer
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Atle Bjørnerud
- The Intervention Centre, Oslo University Hospital, Oslo, Norway
| | - Sigmund Ytre-Hauge
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Renate Grüner
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Physics and Technology, University of Bergen, Bergen, Norway
| | - Ingvild L Tangen
- Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Henrica MJ Werner
- Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Line Bjørge
- Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Øyvind O Salvesen
- Unit for applied Clinical Research, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jone Trovik
- Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Camilla Krakstad
- Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ingfrid S Haldorsen
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| |
Collapse
|
|
39
|
Khan K, Rata M, Cunningham D, Koh DM, Tunariu N, Hahne JC, Vlachogiannis G, Hedayat S, Marchetti S, Lampis A, Damavandi MD, Lote H, Rana I, Williams A, Eccles SA, Fontana E, Collins D, Eltahir Z, Rao S, Watkins D, Starling N, Thomas J, Kalaitzaki E, Fotiadis N, Begum R, Bali M, Rugge M, Temple E, Fassan M, Chau I, Braconi C, Valeri N. Functional imaging and circulating biomarkers of response to regorafenib in treatment-refractory metastatic colorectal cancer patients in a prospective phase II study. Gut 2018; 67:1484-1492. [PMID: 28790159 PMCID: PMC6204951 DOI: 10.1136/gutjnl-2017-314178] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 05/16/2017] [Accepted: 05/23/2017] [Indexed: 12/25/2022]
Abstract
OBJECTIVE Regorafenib demonstrated efficacy in patients with metastatic colorectal cancer (mCRC). Lack of predictive biomarkers, potential toxicities and cost-effectiveness concerns highlight the unmet need for better patient selection. DESIGN Patients with RAS mutant mCRC with biopsiable metastases were enrolled in this phase II trial. Dynamic contrast-enhanced (DCE) MRI was acquired pretreatment and at day 15 post-treatment. Median values of volume transfer constant (Ktrans), enhancing fraction (EF) and their product KEF (summarised median values of Ktrans× EF) were generated. Circulating tumour (ct) DNA was collected monthly until progressive disease and tested for clonal RAS mutations by digital-droplet PCR. Tumour vasculature (CD-31) was scored by immunohistochemistry on 70 sequential tissue biopsies. RESULTS Twenty-seven patients with paired DCE-MRI scans were analysed. Median KEF decrease was 58.2%. Of the 23 patients with outcome data, >70% drop in KEF (6/23) was associated with higher disease control rate (p=0.048) measured by RECIST V. 1.1 at 2 months, improved progression-free survival (PFS) (HR 0.16 (95% CI 0.04 to 0.72), p=0.02), 4-month PFS (66.7% vs 23.5%) and overall survival (OS) (HR 0.08 (95% CI 0.01 to 0.63), p=0.02). KEF drop correlated with CD-31 reduction in sequential tissue biopsies (p=0.04). RAS mutant clones decay in ctDNA after 8 weeks of treatment was associated with better PFS (HR 0.21 (95% CI 0.06 to 0.71), p=0.01) and OS (HR 0.28 (95% CI 0.07-1.04), p=0.06). CONCLUSIONS Combining DCE-MRI and ctDNA predicts duration of anti-angiogenic response to regorafenib and may improve patient management with potential health/economic implications.
Collapse
Affiliation(s)
- Khurum Khan
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London and Sutton, UK
| | - Mihaela Rata
- Division of Radiotherapy and Imaging, Cancer Research UK Imaging Centre, The Institute of Cancer Research and Royal Marsden Hospital, London, UK
| | - David Cunningham
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Dow-Mu Koh
- Division of Radiotherapy and Imaging, Cancer Research UK Imaging Centre, The Institute of Cancer Research and Royal Marsden Hospital, London, UK
| | - Nina Tunariu
- Division of Radiotherapy and Imaging, Cancer Research UK Imaging Centre, The Institute of Cancer Research and Royal Marsden Hospital, London, UK
| | - Jens C Hahne
- Division of Molecular Pathology, The Institute of Cancer Research, London and Sutton, UK
| | - George Vlachogiannis
- Division of Molecular Pathology, The Institute of Cancer Research, London and Sutton, UK
| | - Somaieh Hedayat
- Division of Molecular Pathology, The Institute of Cancer Research, London and Sutton, UK
| | - Silvia Marchetti
- Division of Molecular Pathology, The Institute of Cancer Research, London and Sutton, UK
| | - Andrea Lampis
- Division of Molecular Pathology, The Institute of Cancer Research, London and Sutton, UK
| | | | - Hazel Lote
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London and Sutton, UK
| | - Isma Rana
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Anja Williams
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Suzanne A Eccles
- Division of Cancer Therapeutics, The Institute of Cancer Research, London and Sutton, UK
| | - Elisa Fontana
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - David Collins
- Division of Radiotherapy and Imaging, Cancer Research UK Imaging Centre, The Institute of Cancer Research and Royal Marsden Hospital, London, UK
| | - Zakaria Eltahir
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Sheela Rao
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - David Watkins
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Naureen Starling
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Jan Thomas
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Eleftheria Kalaitzaki
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
- Department of Statistics, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Nicos Fotiadis
- Division of Radiotherapy and Imaging, Cancer Research UK Imaging Centre, The Institute of Cancer Research and Royal Marsden Hospital, London, UK
| | - Ruwaida Begum
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Maria Bali
- Division of Radiotherapy and Imaging, Cancer Research UK Imaging Centre, The Institute of Cancer Research and Royal Marsden Hospital, London, UK
| | - Massimo Rugge
- Department of Medicine (DIMED) and Surgical Pathology, University of Padua, Padua, Italy
| | - Eleanor Temple
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Matteo Fassan
- Department of Medicine (DIMED) and Surgical Pathology, University of Padua, Padua, Italy
| | - Ian Chau
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
| | - Chiara Braconi
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
- Division of Cancer Therapeutics, The Institute of Cancer Research, London and Sutton, UK
| | - Nicola Valeri
- Department of Medicine, The Royal Marsden NHS Trust, London and Sutton, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London and Sutton, UK
| |
Collapse
|
|
40
|
Machireddy A, Thibault G, Huang W, Song X. Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:682-685. [PMID: 30440488 DOI: 10.1109/embc.2018.8512301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Positive response to neoadjuvant chemotherapy (NACT) has been correlated to better long-term outcomes in breast cancer treatment. Early prediction of response to NACT can help modify the regimen for non-responding patients, sparing them of potential toxicities of ineffective therapies. It has been observed that tumor functions such as vascularization and vascular permeability change even before noticeable changes occur in the tumor size in response to the treatment. Therefore, it is essential to have reliable imaging based features to measure these changes. Texture analysis on parametric maps from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown to be a good predictor of breast cancer response to NACT at an early stage. But hand crafted texture features might not be able to capture the rich spatio-temporal information in the parametric maps. In this work, we studied the ability of convolutional neural networks in predicting the response to NACT at an early stage.
Collapse
|
|
41
|
Vargas MI, Delattre BMA, Boto J, Gariani J, Dhouib A, Fitsiori A, Dietemann JL. Advanced magnetic resonance imaging (MRI) techniques of the spine and spinal cord in children and adults. Insights Imaging 2018; 9:549-557. [PMID: 29858818 PMCID: PMC6108966 DOI: 10.1007/s13244-018-0626-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 03/19/2018] [Accepted: 04/05/2018] [Indexed: 12/14/2022] Open
Abstract
Abstract In this article, we illustrate the main advanced magnetic resonance imaging (MRI) techniques used for imaging of the spine and spinal cord in children and adults. This work focuses on daily clinical practice and aims to address the most common questions and needs of radiologists. We will also provide tips to solve common problems with which we were confronted. The main clinical indications for each MR technique, possible pitfalls and the challenges faced in spine imaging because of anatomical and physical constraints will be discussed. The major advanced MRI techniques dealt with in this article are CSF, (cerebrosopinal fluid) flow, diffusion, diffusion tensor imaging (DTI), MRA, dynamic contrast-enhanced T1-weighted perfusion, MR angiography, susceptibility-weighted imaging (SWI), functional imaging (fMRI) and spectroscopy. Teaching Points • DWI is essential to diagnose cord ischaemia in the acute stage. • MRA is useful to guide surgical planning or endovascular embolisation of AVMs. • Three Tesla is superior to 1.5 T for spine MR angiography and spectroscopy. • Advanced sequences should only be used together with conventional morphological sequences.
Collapse
Affiliation(s)
- M I Vargas
- Division of Neuroradiology, DISIM, Geneva University Hospitals and Faculty of Medicine, Rue Gabrielle-Perret-Gentil 4, 1211, Geneva 14, Switzerland.
| | - B M A Delattre
- Division of Radiology, DISIM, Geneva University Hospitals, Geneva, Switzerland
| | - J Boto
- Division of Neuroradiology, DISIM, Geneva University Hospitals and Faculty of Medicine, Rue Gabrielle-Perret-Gentil 4, 1211, Geneva 14, Switzerland
| | - J Gariani
- Division of Radiology, DISIM, Geneva University Hospitals, Geneva, Switzerland
| | - A Dhouib
- Division of Radiology, DISIM, Geneva University Hospitals, Geneva, Switzerland
| | - A Fitsiori
- Division of Neuroradiology, DISIM, Geneva University Hospitals and Faculty of Medicine, Rue Gabrielle-Perret-Gentil 4, 1211, Geneva 14, Switzerland
| | - J L Dietemann
- Division of Neuroradiology, Strasbourg University Hospitals, Strasbourg, France
| |
Collapse
|
|
42
|
Wan CF, Liu XS, Wang L, Zhang J, Lu JS, Li FH. Quantitative contrast-enhanced ultrasound evaluation of pathological complete response in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy. Eur J Radiol 2018; 103:118-123. [PMID: 29803376 DOI: 10.1016/j.ejrad.2018.04.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Revised: 03/29/2018] [Accepted: 04/03/2018] [Indexed: 12/11/2022]
Abstract
PURPOSE To clarify whether the quantitative parameters of contrast-enhanced ultrasound (CEUS) can be used to predict pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). MATERIAL AND METHODS Fifty-one patients with histologically proved locally advanced breast cancer scheduled for NAC were enrolled. The quantitative data for CEUS and the tumor diameter were collected at baseline and before surgery, and compared with the pathological response. Multiple logistic regression analysis was performed to examine quantitative parameters at CEUS and the tumor diameter to predict the pCR, and receiver operating characteristic (ROC) curve analysis was used as a summary statistic. RESULTS Multiple logistic regression analysis revealed that PEAK (the maximum intensity of the time-intensity curve during bolus transit), PEAK%, TTP% (time to peak), and diameter% were significant independent predictors of pCR, and the area under the ROC curve was 0.932(Az1), and the sensitivity and specificity to predict pCR were 93.7% and 80.0%. The area under the ROC curve for the quantitative parameters was 0.927(Az2), and the sensitivity and specificity to predict pCR were 81.2% and 94.3%. For diameter%, the area under the ROC curve was 0.786 (Az3), and the sensitivity and specificity to predict pCR were 93.8% and 54.3%. The values of Az1 and Az2 were significantly higher than that of Az3 (P = 0.027 and P = 0.034, respectively). However, there was no significant difference between the values of Az1 and Az2 (P = 0.825). CONCLUSION Quantitative analysis of tumor blood perfusion with CEUS is superior to diameter% to predict pCR, and can be used as a functional technique to evaluate tumor response to NAC.
Collapse
Affiliation(s)
- Cai-Feng Wan
- Department of Ultrasound, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Xue-Song Liu
- Department of Ultrasound, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Lin Wang
- Department of Ultrasound, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Jie Zhang
- Department of Breast Surgeon, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Jin-Song Lu
- Department of Breast Surgeon, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
| | - Feng-Hua Li
- Department of Ultrasound, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
| |
Collapse
|
|
43
|
Variability induced by the MR imager in dynamic contrast-enhanced imaging of the prostate. Diagn Interv Imaging 2018; 99:255-264. [DOI: 10.1016/j.diii.2017.12.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Revised: 12/03/2017] [Accepted: 12/07/2017] [Indexed: 12/22/2022]
|
|
44
|
Chatterjee A, He D, Fan X, Wang S, Szasz T, Yousuf A, Pineda F, Antic T, Mathew M, Karczmar GS, Oto A. Performance of Ultrafast DCE-MRI for Diagnosis of Prostate Cancer. Acad Radiol 2018; 25:349-358. [PMID: 29167070 PMCID: PMC6535050 DOI: 10.1016/j.acra.2017.10.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2017] [Revised: 09/22/2017] [Accepted: 10/16/2017] [Indexed: 01/19/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to test high temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for different zones of the prostate and evaluate its performance in the diagnosis of prostate cancer (PCa). Determine whether the addition of ultrafast DCE-MRI improves the performance of multiparametric MRI. MATERIALS AND METHODS Patients (n = 20) with pathologically confirmed PCa underwent preoperative 3T MRI with T2-weighted, diffusion-weighted, and high temporal resolution (~2.2 seconds) DCE-MRI using gadoterate meglumine (Guerbet, Bloomington, IN) without an endorectal coil. DCE-MRI data were analyzed by fitting signal intensity with an empirical mathematical model to obtain parameters: percent signal enhancement, enhancement rate (α), washout rate (β), initial enhancement slope, and enhancement start time along with apparent diffusion coefficient (ADC) and T2 values. Regions of interests were placed on sites of prostatectomy verified malignancy (n = 46) and normal tissue (n = 71) from different zones. RESULTS Cancer (α = 6.45 ± 4.71 s-1, β = 0.067 ± 0.042 s-1, slope = 3.78 ± 1.90 s-1) showed significantly (P <.05) faster signal enhancement and washout rates than normal tissue (α = 3.0 ± 2.1 s-1, β = 0.034 ± 0.050 s-1, slope = 1.9 ± 1.4 s-1), but showed similar percentage signal enhancement and enhancement start time. Receiver operating characteristic analysis showed area under the curve for DCE parameters was comparable to ADC and T2 in the peripheral (DCE 0.67-0.82, ADC 0.80, T2 0.89) and transition zones (DCE 0.61-0.72, ADC 0.69, T2 0.75), but higher in the central zone (DCE 0.79-0.88, ADC 0.45, T2 0.45) and anterior fibromuscular stroma (DCE 0.86-0.89, ADC 0.35, T2 0.12). Importantly, combining DCE with ADC and T2 increased area under the curve by ~30%, further improving the diagnostic accuracy of PCa detection. CONCLUSION Quantitative parameters from empirical mathematical model fits to ultrafast DCE-MRI improve diagnosis of PCa. DCE-MRI with higher temporal resolution may capture clinically useful information for PCa diagnosis that would be missed by low temporal resolution DCE-MRI. This new information could improve the performance of multiparametric MRI in PCa detection.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Aytekin Oto
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637.
| |
Collapse
|
|
45
|
Little RA, Barjat H, Hare JI, Jenner M, Watson Y, Cheung S, Holliday K, Zhang W, O'Connor JPB, Barry ST, Puri S, Parker GJM, Waterton JC. Evaluation of dynamic contrast-enhanced MRI biomarkers for stratified cancer medicine: How do permeability and perfusion vary between human tumours? Magn Reson Imaging 2018; 46:98-105. [PMID: 29154898 DOI: 10.1016/j.mri.2017.11.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 11/08/2017] [Accepted: 11/13/2017] [Indexed: 12/18/2022]
Abstract
BACKGROUND Solid tumours exhibit enhanced vessel permeability and fenestrated endothelium to varying degree, but it is unknown how this varies in patients between and within tumour types. Dynamic contrast-enhanced (DCE) MRI provides a measure of perfusion and permeability, the transfer constant Ktrans, which could be employed for such comparisons in patients. AIM To test the hypothesis that different tumour types exhibit systematically different Ktrans. MATERIALS AND METHODS DCE-MRI data were retrieved from 342 solid tumours in 230 patients. These data were from 18 previous studies, each of which had had a different analysis protocol. All data were reanalysed using a standardised workflow using an extended Tofts model. A model of the posterior density of median Ktrans was built assuming a log-normal distribution and fitting a simple Bayesian hierarchical model. RESULTS 12 histological tumour types were included. In glioma, median Ktrans was 0.016min-1 and for non-glioma tumours, median Ktrans ranged from 0.10 (cervical) to 0.21min-1 (prostate metastatic to bone). The geometric mean (95% CI) across all the non-glioma tumours was 0.15 (0.05, 0.45)min-1. There was insufficient separation between the posterior densities to be able to predict the Ktrans value of a tumour given the tumour type, except that the median Ktrans for gliomas was below 0.05min-1 with 80% probability, and median Ktrans measurements for the remaining tumour types were between 0.05 and 0.4min-1 with 80% probability. CONCLUSION With the exception of glioma, our hypothesis that different tumour types exhibit different Ktrans was not supported. Studies in which tumour permeability is believed to affect outcome should not simply seek tumour types thought to exhibit high permeability. Instead, Ktrans is an idiopathic parameter, and, where permeability is important, Ktrans should be measured in each tumour to personalise that treatment.
Collapse
Affiliation(s)
- Ross A Little
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, UK.
| | - Hervé Barjat
- Formerly AstraZeneca, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK.
| | - Jennifer I Hare
- IMED Oncology, AstraZeneca, Li Ka Shing Centre, Cambridge CB2 0RE, UK.
| | - Mary Jenner
- Formerly AstraZeneca, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK
| | - Yvonne Watson
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, UK.
| | - Susan Cheung
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, UK.
| | - Katherine Holliday
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, UK.
| | - Weijuan Zhang
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, UK.
| | - James P B O'Connor
- Division of Cancer Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Oxford Road, Manchester M13 9PL, UK. James.O'
| | - Simon T Barry
- IMED Oncology, AstraZeneca, Li Ka Shing Centre, Cambridge CB2 0RE, UK.
| | - Sanyogitta Puri
- AstraZeneca, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK.
| | - Geoffrey J M Parker
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, UK; Bioxydyn Ltd., Rutherford House, Manchester M15 6SZ, UK.
| | - John C Waterton
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, UK; Formerly AstraZeneca, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK; Bioxydyn Ltd., Rutherford House, Manchester M15 6SZ, UK.
| |
Collapse
|
|
46
|
Abstract
Molecular imaging (mainly PET and MR imaging) has played important roles in gynecologic oncology. Emerging MR-based technologies, including DWI, CEST, DCE-MR imaging, MRS, and DNP, as well as FDG-PET and many novel PET radiotracers, will continuously improve practices. In combination with radiomics analysis, a new era of decision making in personalized medicine and precisely guided radiation treatment planning or real-time surgical interventions is being entered into, which will directly impact on patient survival. Prospective trials with well-defined endpoints are encouraged to evaluate the multiple facets of these emerging imaging tools in the management of gynecologic malignancies.
Collapse
Affiliation(s)
- Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 5 Fu-Shin Street, Kueishan, Taoyuan 333, Taiwan
| | - Chyong-Huey Lai
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 5 Fu-Shin Street, Kueishan, Taoyuan 333, Taiwan.
| | - Tzu-Chen Yen
- Department of Nuclear Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 5 Fu-Shin Street, Kueishan, Taoyuan 333, Taiwan
| |
Collapse
|
|
47
|
Joint Head and Neck Radiotherapy-MRI Development Cooperative, Ger RB, Mohamed ASR, Awan MJ, Ding Y, Li K, Fave XJ, Beers AL, Driscoll B, Elhalawani H, Hormuth DA, Houdt PJV, He R, Zhou S, Mathieu KB, Li H, Coolens C, Chung C, Bankson JA, Huang W, Wang J, Sandulache VC, Lai SY, Howell RM, Stafford RJ, Yankeelov TE, Heide UAVD, Frank SJ, Barboriak DP, Hazle JD, Court LE, Kalpathy-Cramer J, Fuller CD. A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations. Sci Rep 2017; 7:11185. [PMID: 28894197 PMCID: PMC5593829 DOI: 10.1038/s41598-017-11554-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 08/18/2017] [Indexed: 11/15/2022] Open
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff's alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.
Collapse
|
|
48
|
He D, Zamora M, Oto A, Karczmar GS, Fan X. Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments. Phys Med Biol 2017; 62:N445-N459. [PMID: 28786402 DOI: 10.1088/1361-6560/aa84d6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (K trans and v e) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then K trans and v e for the ROI was extracted. The simulations were run 100 times for each ROI with new K trans and v e generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n = 6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (<5 s) of Gd-DTPA for a total imaging duration of ~10 min. K trans and v e were calculated in two ways: (i) by fitting C(t) for each pixel, and then averaging the pixel values over the entire ROI, and (ii) by averaging C(t) over the entire ROI, and then fitting averaged C(t) to extract K trans and v e. The simulation results showed that in heterogeneous ROIs, the pixel-by-pixel averaged K trans was ~25% to ~50% larger (p < 0.01) than the ROI-averaged K trans. At higher noise levels, the pixel-averaged K trans was greater than the 'true' K trans, but the ROI-averaged K trans was lower than the 'true' K trans. The ROI-averaged K trans was closer to the true K trans than pixel-averaged K trans for high noise levels. In pre-clinical experiments, the pixel-by-pixel averaged K trans was ~15% larger than the ROI-averaged K trans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.
Collapse
Affiliation(s)
- Dianning He
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, People's Republic of China. Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America
| | | | | | | | | |
Collapse
|
|
49
|
Manganaro L, Saldari M, Pozza C, Vinci V, Gianfrilli D, Greco E, Franco G, Sergi ME, Scialpi M, Catalano C, Isidori AM. Dynamic contrast-enhanced and diffusion-weighted MR imaging in the characterisation of small, non-palpable solid testicular tumours. Eur Radiol 2017; 28:554-564. [DOI: 10.1007/s00330-017-5013-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 07/05/2017] [Accepted: 08/01/2017] [Indexed: 12/26/2022]
|
|
50
|
Thibault G, Tudorica A, Afzal A, Chui SYC, Naik A, Troxell ML, Kemmer KA, Oh KY, Roy N, Jafarian N, Holtorf ML, Huang W, Song X. DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response. ACTA ACUST UNITED AC 2017; 3:23-32. [PMID: 28691102 PMCID: PMC5500247 DOI: 10.18383/j.tom.2016.00241] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
This study investigates the effectiveness of hundreds of texture features extracted from voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps for early prediction of breast cancer response to neoadjuvant chemotherapy (NAC). In total, 38 patients with breast cancer underwent DCE-MRI before (baseline) and after the first of the 6-8 NAC cycles. Quantitative pharmacokinetic (PK) parameters and semiquantitative metrics were estimated from DCE-MRI time-course data. The residual cancer burden (RCB) index value was computed based on pathological analysis of surgical specimens after NAC completion. In total, 1043 texture features were extracted from each of the 13 parametric maps of quantitative PK or semiquantitative metric, and their capabilities for early prediction of RCB were examined by correlating feature changes between the 2 MRI studies with RCB. There were 1069 pairs of feature-map combinations that showed effectiveness for response prediction with 4 correlation coefficients >0.7. The 3-dimensional gray-level cooccurrence matrix was the most effective feature extraction method for therapy response prediction, and, in general, the statistical features describing texture heterogeneity were the most effective features. Quantitative PK parameters, particularly those estimated with the shutter-speed model, were more likely to generate effective features for prediction response compared with the semiquantitative metrics. The best feature-map pair could predict pathologic complete response with 100% sensitivity and 100% specificity using our cohort. In conclusion, breast tumor heterogeneity in microvasculature as measured by texture features of voxel-based DCE-MRI parametric maps could be a useful biomarker for early prediction of NAC response.
Collapse
Affiliation(s)
- Guillaume Thibault
- Center Spatial Systems Biomedicine, BME, Oregon Health & Science University, Portland, Oregon
| | - Alina Tudorica
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, Oregon
| | - Aneela Afzal
- Department of Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon
| | - Stephen Y-C Chui
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon.,Department of Medical Oncology, Oregon Health & Science University, Portland, Oregon
| | - Arpana Naik
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon.,Department of Surgical Oncology, Oregon Health & Science University, Portland, Oregon
| | - Megan L Troxell
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon.,Department of Pathology, Oregon Health & Science University, Portland, Oregon
| | - Kathleen A Kemmer
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon.,Department of Medical Oncology, Oregon Health & Science University, Portland, Oregon
| | - Karen Y Oh
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, Oregon
| | - Nicole Roy
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, Oregon
| | - Neda Jafarian
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, Oregon
| | - Megan L Holtorf
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
| | - Wei Huang
- Department of Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon.,Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
| | - Xubo Song
- Center for Spoken Language Understanding, Oregon Health & Science University, Portland, Oregon
| |
Collapse
|