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Gu P, Mendonca O, Carter D, Dube S, Wang P, Huang X, Li D, Moore JH, McGovern DPB. AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD. Inflamm Bowel Dis 2024; 30:2467-2485. [PMID: 38452040 DOI: 10.1093/ibd/izae030] [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: 10/17/2023] [Indexed: 03/09/2024]
Abstract
Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Shishir Dube
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Virarkar M, Daoud T, Sun J, Montanarella M, Menendez-Santos M, Mahmoud H, Saleh M, Bhosale P. MRI Radiomics Data Analysis for Differentiation between Malignant Mixed Müllerian Tumors and Endometrial Carcinoma. Cancers (Basel) 2024; 16:2647. [PMID: 39123375 PMCID: PMC11312193 DOI: 10.3390/cancers16152647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 07/17/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) and analyzed various radiomic features and gray-level co-occurrence matrix (GLCM) features. These variables and patient clinicopathologic characteristics were compared between EC and MMMTs using the Wilcoxon Rank sum and Fisher's exact test. The area under the curve of the receiving operating characteristics (AUC ROC) was calculated for univariate analysis in predicting EC status. Logistic regression with elastic net regularization was performed for texture feature selection. This study showed that skewness (p = 0.045) and tumor volume (p = 0.007) significantly differed between EC and MMMTs. The range of cluster shade, the angular variance of cluster shade, and the range of the sum of squares variance were significant predictors of EC status (p ≤ 0.05). The regularized Cox regression analysis identified the "256 Angular Variance of Energy" texture feature as significantly associated with OS independently of the EC/MMMT grouping (p = 0.004). The volume and texture features of the tumor region may help distinguish between EC and MMMTs and predict patient outcomes.
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Affiliation(s)
- Mayur Virarkar
- Department of Radiology, University of Florida College of Medicine-Jacksonville, Jacksonville, FL 32209, USA; (M.V.); (M.M.)
| | - Taher Daoud
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
| | - Jia Sun
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
| | - Matthew Montanarella
- Department of Radiology, University of Florida College of Medicine-Jacksonville, Jacksonville, FL 32209, USA; (M.V.); (M.M.)
| | - Manuel Menendez-Santos
- Department of Radiology, University of Florida College of Medicine-Jacksonville, Jacksonville, FL 32209, USA; (M.V.); (M.M.)
| | - Hagar Mahmoud
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
| | - Mohammed Saleh
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
| | - Priya Bhosale
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
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De Kock I, Bos S, Delrue L, Van Welden S, Bunyard P, Hindryckx P, De Vos M, Villeirs G, Laukens D. MRI texture analysis of T2-weighted images is preferred over magnetization transfer imaging for readily longitudinal quantification of gut fibrosis. Eur Radiol 2023; 33:5943-5952. [PMID: 37071162 DOI: 10.1007/s00330-023-09624-x] [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: 12/12/2022] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 04/19/2023]
Abstract
OBJECTIVES To investigate the value of magnetization transfer (MT) MRI and texture analysis (TA) of T2-weighted MR images (T2WI) in the assessment of intestinal fibrosis in a mouse model. METHODS Chronic colitis was induced in mice by cyclic administration of dextran sodium sulphate (DSS) resulting in chronic inflammation and progressive bowel fibrosis. Mice underwent 7-T MR imaging at various time points. Bowel wall MT ratio (MTR) and textural features (skewness, kurtosis, entropy), extracted by a filtration histogram technique, were correlated with histopathology. Performance of both techniques were validated using antifibrotic therapy. Finally, a retrospective study was conducted in five patients with Crohn's disease (CD) who underwent bowel surgery. RESULTS MTR and texture entropy correlated with histopathological fibrosis (r = .85 and .81, respectively). Entropy was superior to MTR for monitoring bowel fibrosis in the presence of coexisting inflammation (linear regression R2 = .93 versus R2 = .01). Furthermore, texture entropy was able to assess antifibrotic therapy response (placebo mice versus treated mice at endpoint scan; Δmean = 0.128, p < .0001). An increase in entropy was indicative of fibrosis accumulation in human CD strictures (inflammation: 1.29; mixed strictures: 1.4 and 1.48; fibrosis: 1.73 and 1.9). CONCLUSION MT imaging and TA of T2WI can both noninvasively detect established intestinal fibrosis in a mouse model. However, TA is especially useful for the longitudinal quantification of fibrosis in mixed inflammatory-fibrotic tissue, as well as for antifibrotic treatment response evaluation. This accessible post-processing technique merits further validation as the benefits for clinical practice as well as antifibrotic trial design would be numerous. KEY POINTS • Magnetization transfer MRI and texture analysis of T2-weighted MR images can detect established bowel fibrosis in an animal model of gut fibrosis. • Texture entropy is able to identify and monitor bowel fibrosis progression in an inflammatory context and can assess the response to antifibrotic treatment. • A proof-of-concept study in five patients with Crohn's disease suggests that texture entropy can detect and grade fibrosis in human intestinal strictures.
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Affiliation(s)
- Isabelle De Kock
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Simon Bos
- Department of Internal Medicine and Pediatrics, Ghent University, Corneel Heymanslaan 10, 0MRB2, B-9000, Ghent, Belgium
- VIB Center for Inflammation Research, Ghent, Belgium
| | - Louke Delrue
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Sophie Van Welden
- Department of Internal Medicine and Pediatrics, Ghent University, Corneel Heymanslaan 10, 0MRB2, B-9000, Ghent, Belgium
- VIB Center for Inflammation Research, Ghent, Belgium
| | | | - Pieter Hindryckx
- Department of Gastroenterology, Ghent University Hospital, Ghent, Belgium
| | - Martine De Vos
- Department of Gastroenterology, Ghent University Hospital, Ghent, Belgium
| | - Geert Villeirs
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Debby Laukens
- Department of Internal Medicine and Pediatrics, Ghent University, Corneel Heymanslaan 10, 0MRB2, B-9000, Ghent, Belgium.
- VIB Center for Inflammation Research, Ghent, Belgium.
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Maccioni F, Busato L, Valenti A, Cardaccio S, Longhi A, Catalano C. Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future Prospectives. Diagnostics (Basel) 2023; 13:2410. [PMID: 37510154 PMCID: PMC10378103 DOI: 10.3390/diagnostics13142410] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
This review focuses on the role of magnetic resonance imaging (MRI) in the evaluation of the gastrointestinal tract (GI MRI), analyzing the major technical advances achieved in this field, such as diffusion-weighted imaging, molecular imaging, motility studies, and artificial intelligence. Today, MRI performed with the more advanced imaging techniques allows accurate assessment of many bowel diseases, particularly inflammatory bowel disease and rectal cancer; in most of these diseases, MRI is invaluable for diagnosis, staging, and disease monitoring under treatment. Several MRI parameters are currently considered activity biomarkers for inflammation and neoplastic disease. Furthermore, in younger patients with acute or chronic GI disease, MRI can be safely used for short-term follow-up studies in many critical clinical situations because it is radiation-free. MRI assessment of functional gastro-esophageal and small bowel disorders is still in its infancy but very promising, while it is well established and widely used for dynamic assessment of anorectal and pelvic floor dysfunction; MRI motility biomarkers have also been described. There are still some limitations to GI MRI related to high cost and limited accessibility. However, technical advances are expected, such as faster sequences, more specific intestinal contrast agents, AI analysis of MRI data, and possibly increased accessibility to GI MRI studies. Clinical interest in the evaluation of bowel disease using MRI is already very high, but is expected to increase significantly in the coming years.
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Affiliation(s)
- Francesca Maccioni
- Department of Radiological Sciences, Pathology and Oncology, Policlinico Umberto I Hospital, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Ludovica Busato
- Department of Radiological Sciences, Pathology and Oncology, Policlinico Umberto I Hospital, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Alessandra Valenti
- Department of Radiological Sciences, Pathology and Oncology, Policlinico Umberto I Hospital, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Sara Cardaccio
- Department of Radiological Sciences, Pathology and Oncology, Policlinico Umberto I Hospital, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Alessandro Longhi
- Department of Radiological Sciences, Pathology and Oncology, Policlinico Umberto I Hospital, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Carlo Catalano
- Department of Radiological Sciences, Pathology and Oncology, Policlinico Umberto I Hospital, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
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Zhu X, Ye DD, Wang JH, Li J, Liu SW. Diagnostic performance of texture analysis in the differential diagnosis of perianal fistulising Crohn’s disease and glandular anal fistula. World J Gastrointest Surg 2023; 15:882-891. [PMID: 37342861 PMCID: PMC10277959 DOI: 10.4240/wjgs.v15.i5.882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/16/2023] [Accepted: 03/30/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Perianal fistulising Crohn's disease (PFCD) and glandular anal fistula have many similarities on conventional magnetic resonance imaging. However, many patients with PFCD show concomitant active proctitis, but only few patients with glandular anal fistula have active proctitis.
AIM To explore the value of differential diagnosis of PFCD and glandular anal fistula by comparing the textural feature parameters of the rectum and anal canal in fat suppression T2-weighted imaging (FS-T2WI).
METHODS Patients with rectal water sac implantation were screened from the first part of this study (48 patients with PFCD and 22 patients with glandular anal fistula). Open-source software ITK-SNAP (Version 3.6.0, http://www.itksnap.org/) was used to delineate the region of interest (ROI) of the entire rectum and anal canal wall on every axial section, and then the ROIs were input in the Analysis Kit software (version V3.0.0.R, GE Healthcare) to calculate the textural feature parameters. Textural feature parameter differences of the rectum and anal canal wall between the PFCD group vs the glandular anal fistula group were analyzed using Mann-Whitney U test. The redundant textural parameters were screened by bivariate Spearman correlation analysis, and binary logistic regression analysis was used to establish the model of textural feature parameters. Finally, diagnostic accuracy was assessed by receiver operating characteristic-area under the curve (AUC) analysis.
RESULTS In all, 385 textural parameters were obtained, including 37 parameters with statistically significant differences between the PFCD and glandular anal fistula groups. Then, 16 texture feature parameters remained after bivariate Spearman correlation analysis, including one histogram parameter (Histogram energy); four grey level co-occurrence matrix (GLCM) parameters (GLCM energy_all direction_offset1_SD, GLCM entropy_all direction_ offset4_SD, GLCM entropy_all direction_offset7_SD, and Haralick correlation_all direction_ offset7_SD); four texture parameters (Correlation_all direction_offset1_SD, cluster prominence _angle 90_offset4, Inertia_all direction_offset7_SD, and cluster shade_angle 45_offset7); five grey level run-length matrix parameters (grey level nonuniformity_angle 90_offset1, grey level nonuniformity_all direction_offset4_SD, long run high grey level emphasis_all direction_offset1_SD, long run emphasis_all direction_ offset4_ SD, and long run high grey level emphasis_all direction_offset4_SD); and two form factor parameters (surface area and maximum 3D diameter). The AUC, sensitivity, and specificity of the model of textural feature parameters were 0.917, 85.42%, and 86.36%, respectively.
CONCLUSION The model of textural feature parameters showed good diagnostic performance for PFCD. The texture feature parameters of the rectum and anal canal in FS-T2WI are helpful to distinguish PFCD from glandular anal fistula.
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Affiliation(s)
- Xin Zhu
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Dan-Dan Ye
- Department of Radiology, Quanzhou Orthopedic-Traumatological Hospital, Quanzhou 362000, Fujian Province, China
| | - Jian-Hua Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Jing Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Shao-Wei Liu
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
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Alyami AS. The Role of Radiomics in Fibrosis Crohn's Disease: A Review. Diagnostics (Basel) 2023; 13:diagnostics13091623. [PMID: 37175014 PMCID: PMC10178496 DOI: 10.3390/diagnostics13091623] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a global health concern that has been on the rise in recent years. In addition, imaging is the established method of care for detecting, diagnosing, planning treatment, and monitoring the progression of IBD. While conventional imaging techniques are limited in their ability to provide comprehensive information, cross-sectional imaging plays a crucial role in the clinical management of IBD. However, accurately characterizing, detecting, and monitoring fibrosis in Crohn's disease remains a challenging task for clinicians. Recent advances in artificial intelligence technology, machine learning, computational power, and radiomic emergence have enabled the automated evaluation of medical images to generate prognostic biomarkers and quantitative diagnostics. Radiomics analysis can be achieved via deep learning algorithms or by extracting handcrafted radiomics features. As radiomic features capture pathophysiological and biological data, these quantitative radiomic features have been shown to offer accurate and rapid non-invasive tools for IBD diagnostics, treatment response monitoring, and prognosis. For these reasons, the present review aims to provide a comprehensive review of the emerging radiomics methods in intestinal fibrosis research that are highlighted and discussed in terms of challenges and advantages.
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Affiliation(s)
- Ali S Alyami
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
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Chirra P, Sharma A, Bera K, Cohn HM, Kurowski JA, Amann K, Rivero MJ, Madabhushi A, Lu C, Paspulati R, Stein SL, Katz JA, Viswanath SE, Dave M. Integrating Radiomics With Clinicoradiological Scoring Can Predict High-Risk Patients Who Need Surgery in Crohn's Disease: A Pilot Study. Inflamm Bowel Dis 2023; 29:349-358. [PMID: 36250776 PMCID: PMC9977224 DOI: 10.1093/ibd/izac211] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Indexed: 12/09/2022]
Abstract
BACKGROUND Early identification of Crohn's disease (CD) patients at risk for complications could enable targeted surgical referral, but routine magnetic resonance enterography (MRE) has not been definitively correlated with need for surgery. Our objective was to identify computer-extracted image (radiomic) features from MRE associated with risk of surgery in CD and combine them with clinical and radiological assessments to predict time to intervention. METHODS This was a retrospective single-center pilot study of CD patients who had an MRE within 3 months prior to initiating medical therapy. Radiomic features were extracted from annotated terminal ileum regions on MRE and combined with clinical variables and radiological assessment (via Simplified Magnetic Resonance Index of Activity scoring for wall thickening, edema, fat stranding, ulcers) in a random forest classifier. The primary endpoint was high- and low-risk groups based on need for surgery within 1 year of MRE. The secondary endpoint was time to surgery after treatment. RESULTS Eight radiomic features capturing localized texture heterogeneity within the terminal ileum were significantly associated with risk of surgery within 1 year of treatment (P < .05); yielding a discovery cohort area under the receiver-operating characteristic curve of 0.67 (n = 50) and validation cohort area under the receiver-operating characteristic curve of 0.74 (n = 23). Kaplan-Meier analysis of radiomic features together with clinical variables and Simplified Magnetic Resonance Index of Activity scores yielded the best hazard ratio of 4.13 (P = (7.6 × 10-6) and concordance index of 0.71 in predicting time to surgery after MRE. CONCLUSIONS Radiomic features on MRE may be associated with risk of surgery in CD, and in combination with clinicoradiological scoring can yield an accurate prognostic model for time to surgery.
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Affiliation(s)
- Prathyush Chirra
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Anamay Sharma
- Division of Gastroenterology, Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - H Matthew Cohn
- Long Island Digestive Disease Consultants, Northwell Health Physician Partners, Setauket, NY, USA
| | - Jacob A Kurowski
- Department of Pediatric Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, OH, USA
| | - Katelin Amann
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Marco-Jose Rivero
- Division of Gastroenterology, Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Research Health Scientist, Atlanta Veterans Administration Medical Center, Atlanta, GA, USA
| | - Cheng Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Rajmohan Paspulati
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Sharon L Stein
- Department of General Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USAand
| | - Jeffrey A Katz
- Division of Gastroenterology, Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Satish E Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Maneesh Dave
- Division of Gastroenterology, Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, UC Davis Medical Center, UC Davis School of Medicine, Sacramento, CA, USA
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Ding H, Li J, Jiang K, Gao C, Lu L, Zhang H, Chen H, Gao X, Zhou K, Sun Z. Assessing the inflammatory severity of the terminal ileum in Crohn disease using radiomics based on MRI. BMC Med Imaging 2022; 22:118. [PMID: 35787255 PMCID: PMC9254684 DOI: 10.1186/s12880-022-00844-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 06/21/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Evaluating inflammatory severity using imaging is essential for Crohn's disease, but it is limited by potential interobserver variation and subjectivity. We compared the efficiency of magnetic resonance index of activity (MaRIA) collected by radiologists and a radiomics model in assessing the inflammatory severity of terminal ileum (TI). METHODS 121 patients were collected from two centers. Patients were divided into ulcerative group and mucosal remission group based on the TI Crohn's disease Endoscopic Severity Index. The consistency of bowel wall thickness (BWT), relative contrast enhancement (RCE), edema, ulcer, MaRIA and features of the region of interest between radiologists were described by weighted Kappa test and intraclass correlation coefficient (ICC), and developed receiver operating curve of MaRIA. The radiomics model was established using reproducible features of logistic regression based on arterial staging of T1WI sequences. Delong test was used to compare radiomics with MaRIA. RESULTS The consistency between radiologists were moderate in BWT (ICC = 0.638), fair in edema (κ = 0.541), RCE (ICC = 0.461), MaRIA (ICC = 0.579) and poor in ulcer (κ = 0.271). Radiomics model was developed by 6 reproducible features (ICC = 0.93-0.96) and equivalent to MaRIA which evaluated by the senior radiologist (0.872 vs 0.883 in training group, 0.824 vs 0.783 in validation group, P = 0.847, 0.471), both of which were significantly higher than MaRIA evaluated by junior radiologist (AUC: 0.621 in training group, 0.557 in validation group, all, P < 0.05). CONCLUSION The evaluation of inflammatory severity could be performed by radiomics objectively and reproducibly, and was comparable to MaRIA evaluated by the senior radiologist. Radiomics may be an important method to assist junior radiologists to assess the severity of inflammation objectively and accurately.
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Affiliation(s)
- Honglei Ding
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Jiaying Li
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Kefang Jiang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Liangji Lu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Huani Zhang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Haibo Chen
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Xuning Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Kefeng Zhou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.
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Li T, Liu Y, Guo J, Wang Y. Prediction of the activity of Crohn's disease based on CT radiomics combined with machine learning models. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:1155-1168. [PMID: 35988261 DOI: 10.3233/xst-221224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PURPOSE To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase. METHODS CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance.
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Affiliation(s)
- Tingting Li
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yu Liu
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School ofMedicine, Shanghai 200011, China
| | - Jiuhong Guo
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School ofMedicine, Shanghai 200011, China
| | - Yuanjun Wang
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Kepp FH, Huber FA, Wurnig MC, Mannil M, Kaniewska M, Guglielmi R, Del Grande F, Guggenberger R. Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis. Eur J Radiol 2021; 140:109755. [PMID: 33989966 DOI: 10.1016/j.ejrad.2021.109755] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/09/2021] [Accepted: 05/03/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE To compare the diagnostic performance of texture analysis (TA) against visual qualitative assessment in the differentiation of spondyloarthritis (SpA) from degenerative changes in the sacroiliac joints (SIJ). METHOD Ninety patients referred for suspected inflammatory lower back pain from the rheumatology department were retrospectively included at our university hospital institution. MRI at 3 T of the lumbar spine and SIJ was performed with oblique coronal T1-weighted (w), fluid-sensitive fat-saturated (fs) TIRM and fsT1w intravenously contrast-enhanced (CE) images. Subjects were divided into three age- and gender-matched groups (30 each) based on definite clinical diagnosis serving as clinical reference standard with either degenerative, inflammatory (SpA) or no changes of the SIJ. SIJ were rated qualitatively by two independent radiologists and quantitatively by region-of-interest-based TA with 304 features subjected to machine learning logistic regression with randomized ten-fold selection of training and validation data. Qualitative and quantitative results were evaluated for diagnostic performance and compared against clinical reference standard. RESULTS Agreement of radiologist's diagnose with clinical reference was fair for both readers (κ = 0.32 and 0.44). ROC statistics revealed significant outperformance of TA compared to qualitative ratings for differentiation of SpA from remainder (AUC = 0.89 vs. 0.75), SpA from degenerative (AUC = 0.91 vs. 0.67) and TIRM-positive SpA (i.e. with bone marrow edema) from remainder cases (AUC = 0.95 vs. 0.76). T1w-CE images were the most important discriminator for detection of SpA. CONCLUSIONS TA is superior to qualitative assessment for the differentiation of inflammatory from degenerative changes of the SIJ. Intravenous CE-images increase diagnostic yield in quantitative TA.
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Affiliation(s)
- Felix H Kepp
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; Faculty of Medicine, University of Zurich, Switzerland
| | - Florian A Huber
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; Faculty of Medicine, University of Zurich, Switzerland.
| | - Moritz C Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; Faculty of Medicine, University of Zurich, Switzerland
| | - Manoj Mannil
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; Faculty of Medicine, University of Zurich, Switzerland
| | - Malwina Kaniewska
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; Faculty of Medicine, University of Zurich, Switzerland
| | - Riccardo Guglielmi
- Institute of Radiology, Spital Thurgau AG, Cantonal Hospital Münsterlingen, Spitalcampus 1, 8596 Münsterlingen, Switzerland
| | - Filippo Del Grande
- Istituto di imaging della Svizzera Italiana, Regional Hospital of Lugano, Via Tesserete 46, 6900 Lugano, Switzerland
| | - Roman Guggenberger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; Faculty of Medicine, University of Zurich, Switzerland
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11
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Abstract
PURPOSE OF REVIEW Recent advances in computed tomography (CT), ultrasound (US), magnetic resonance imaging (MRI), and nuclear radiology have improved the diagnosis and characterization of small bowel pathology. Our purpose is to highlight the current status and recent advances in multimodality noninvasive imaging of the small bowel. RECENT FINDINGS CT and MR enterography are established techniques for small bowel evaluation. Dual-energy CT is a novel technique that has shown promise for the mesenteric ischemia and small bowel bleeding. Advanced US techniques and MRI sequences are being investigated to improve assessment of bowel inflammation, treatment response assessment, motility, and mural fibrosis. Novel radiotracers and scanner technologies have made molecular imaging the new reference standard for small bowel neuroendocrine tumors. Computational image analysis and artificial intelligence (AI) have the potential to augment physician expertise, reduce errors and variability in assessment of the small bowel on imaging. SUMMARY Advances in translational imaging research coupled with progress in imaging technology have led to a wider adoption of cross-sectional imaging for the evaluation and management of small bowel entities. Ongoing developments in image acquisition and postprocessing techniques, molecular imaging and AI have the strongest potential to transform the care and outcomes of patients with small bowel diseases.
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12
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Li Q, Wang T, Huang Y, Li Q, Liu P, Grimm R, Fu C, Zhang Y, Gu Y. Whole-Tumor Histogram and Texture Imaging Features on Magnetic Resonance Imaging Combined With Epstein-Barr Virus Status to Predict Disease Progression in Patients With Nasopharyngeal Carcinoma. Front Oncol 2021; 11:610804. [PMID: 33767984 PMCID: PMC7986723 DOI: 10.3389/fonc.2021.610804] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 02/05/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: We aimed to investigate whether Epstein–Barr virus (EBV) could produce differences on MRI by examining the histogram and texture imaging features. We also sought to determine the predictive value of pretreatment MRI texture analyses incorporating with EBV status for disease progression (PD) in patients with primary nasopharyngeal carcinoma (NPC). Materials and Methods: Eighty-one patients with primary T2-T4 NPC and known EBV status who underwent contrast-enhanced MRI were included in this retrospective study. Whole-tumor-based histogram and texture features were extracted from pretreatment T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced (CE)-T1WI images. Mann–Whitney U-tests were performed to identify the differences in histogram and texture parameters between EBV DNA-positive and EBV DNA-negative NPC images. The effects of clinical variables as well as histogram and texture features were estimated by using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curve analysis was used to predict the EBV status and PD. Finally, an integrated model with the best performance was built. Results: Of the 81 patients included, 54 had EBV DNA-positive NPC, and 27 had EBV DNA-negative NPC. Patients who were tested EBV DNA-positive had higher overall stage (P = 0.016), more lymphatic metastases (p < 0.0001), and easier distant metastases (P = 0.026) than the patients who were tested EBV DNA-negative. Tumor volume, T1WISkewness and T2WIKurtosis showed significant differences between the two groups. The combination of the three features achieved an AUC of 0.783 [95% confidence interval (CI) 0.678–0.888] with a sensitivity and specificity of 70.4 and 74.1%, respectively, in differentiating EBV DNA-positive tumors from EBV DNA-negative tumors. The combination of overall stage and tumor volume of T2WIKurtosis and EBV status was the most effective model for predicting PD in patients with primary NPC. The overall accuracy was 84.6%, with a sensitivity and specificity of 93.8 and 66.2%, respectively (AUC, 0.800; 95% CI 0.700–0.900). Conclusion: This study demonstrates that MRI-based radiological features and EBV status can be used as an aid tool for the evaluation of PD, in order to develop tailored treatment targeting specific characteristics of individual patients.
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Affiliation(s)
- Qiao Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - TingTing Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Huang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - PeiYao Liu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Robert Grimm
- Magnetic Resonance Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - CaiXia Fu
- Magnetic Resonance Applications Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - YunYan Zhang
- Department of Radiology, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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13
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Thomas JV, Abou Elkassem AM, Ganeshan B, Smith AD. MR Imaging Texture Analysis in the Abdomen and Pelvis. Magn Reson Imaging Clin N Am 2020; 28:447-456. [PMID: 32624161 DOI: 10.1016/j.mric.2020.03.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Add "which is a" before "distribution"? Texture analysis (TA) is a form of radiomics that refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MR images of the abdomen and pelvis, with the main strength quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MRTA. Despite these limitations, there is a growing body of literature supporting MRTA. This review discusses application of MRTA to the abdomen and pelvis.
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Affiliation(s)
- John V Thomas
- Body Imaging Section, Department of Radiology, University of Alabama at Birmingham, N355 Jefferson Tower, 619 19th Street South, Birmingham, AL 35249-6830, USA.
| | - Asser M Abou Elkassem
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL 35249-6830, USA
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College of London, 5th Floor, Tower, 235 Euston Road, London NW1 2BU, UK
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL 35249-6830, USA
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14
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Zhong YK, Lu BL, Huang SY, Chen YJ, Li ZP, Rimola J, Li XH. Cross-sectional imaging for assessing intestinal fibrosis in Crohn's disease. J Dig Dis 2020; 21:342-350. [PMID: 32418328 DOI: 10.1111/1751-2980.12881] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
More than 30% of patients with Crohn's disease (CD) develop fibrotic strictures in the bowel as the disease progresses. Excessive deposition of extracellular matrix components in the submucosa and smooth muscle hypertrophy or hyperplasia are the main features of fibrosis in CD. Cross-sectional imaging technology provides a wealth of information on the anatomy, histological composition, and physiological function of the bowel, allowing for a non-invasive and complete evaluation of associated abnormalities. This review summarizes recent advances in and the potential technologies of cross-sectional imaging for assessing intestinal fibrosis in CD, including ultrasound imaging, computed tomography, and magnetic resonance imaging.
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Affiliation(s)
- Ying Kui Zhong
- Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Bao Lan Lu
- Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Si Yun Huang
- Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Yu Jun Chen
- Department of Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Zi Ping Li
- Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Jordi Rimola
- Department of Radiology, Hospital Clínic of Barcelona, University of Barcelona, Barcelona, Spain
| | - Xue Hua Li
- Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
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15
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Deepak P, Fowler KJ, Fletcher JG, Bruining DH. Novel Imaging Approaches in Inflammatory Bowel Diseases. Inflamm Bowel Dis 2019; 25:248-260. [PMID: 30010908 DOI: 10.1093/ibd/izy239] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Indexed: 12/12/2022]
Abstract
Inflammatory bowel diseases are chronic autoimmune conditions of the gastrointestinal tract, mainly grouped into ulcerative colitis or Crohn's disease. Traditionally, symptoms have been used to guide IBD management, but this approach is fatally flawed, as symptoms don't correlate with disease activity and often fail to predict disease complications, especially with Crohn's disease. Hence, there is increasing recognition of the need for treatment algorithms based on objective measures of bowel inflammation. In this review, we will focus on advancements in the endoscopic and radiological imaging armamentarium that allow detailed assessments from intestinal mucosa to mesentery.
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Affiliation(s)
- Parakkal Deepak
- Division of Gastroenterology, John T. Milliken Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Kathryn J Fowler
- Department of Radiology, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Joel G Fletcher
- Division of Abdominal Imaging, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - David H Bruining
- Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine, Rochester, Minnesota
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16
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Penzias G, Singanamalli A, Elliott R, Gollamudi J, Shih N, Feldman M, Stricker PD, Delprado W, Tiwari S, Böhm M, Haynes AM, Ponsky L, Fu P, Tiwari P, Viswanath S, Madabhushi A. Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS One 2018; 13:e0200730. [PMID: 30169514 PMCID: PMC6118356 DOI: 10.1371/journal.pone.0200730] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 07/02/2018] [Indexed: 12/29/2022] Open
Abstract
Translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. In the context of prostate cancer (PCa), some studies have correlated gross histological measurements of gland lumen, epithelium, and nuclei with disease appearance on MRI. Quantitative histomorphometry (QH), like radiomics for radiologic images, is the computer based extraction of features for describing tumor morphology on digitized tissue images. In this work, we attempt to establish the histomorphometric basis for radiomic features for prostate cancer by (1) identifying the radiomic features from T2w MRI most discriminating of low vs. intermediate/high Gleason score, (2) identifying QH features correlated with the most discriminating radiomic features previously identified, and (3) evaluating the discriminative ability of QH features found to be correlated with spatially co-localized radiomic features. On a cohort of 36 patients (23 for training, 13 for validation), Gabor texture features were identified as being most predictive of Gleason grade on MRI (AUC of 0.69) and gland lumen shape features were identified as the most predictive QH features (AUC = 0.75). Our results suggest that the PCa grade discriminability of Gabor features is a consequence of variations in gland shape and morphology at the tissue level.
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Affiliation(s)
- Gregory Penzias
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Asha Singanamalli
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Robin Elliott
- University Hospitals, Cleveland, OH, United States of America
| | - Jay Gollamudi
- University Hospitals, Cleveland, OH, United States of America
| | - Natalie Shih
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Michael Feldman
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, United States of America
| | | | - Warick Delprado
- Douglass Hanly Moir Pathology, Macquarie Park, NSW, Australia
| | - Sarita Tiwari
- Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Maret Böhm
- Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Anne-Maree Haynes
- Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Lee Ponsky
- University Hospitals, Cleveland, OH, United States of America
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Satish Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
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