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Zhu ZW, Wu J, Guo Y, Ren QY, Li DN, Li ZY, Han L. Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features. World J Gastrointest Oncol 2025; 17:104172. [DOI: 10.4251/wjgo.v17.i5.104172] [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: 12/27/2024] [Revised: 01/20/2025] [Accepted: 02/26/2025] [Indexed: 05/15/2025] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is one of the most common malignant tumours of the digestive system worldwide. The expression of Ki-67 is crucial for the diagnosis, treatment, and prognostic evaluation of HCC.
AIM To construct a machine learning model for the preoperative evaluation of Ki-67 expression in HCC and to assist in clinical decision-making.
METHODS This study included 164 pathologically confirmed HCC patients. Radiomic features were extracted from the computed tomography images reconstructed by superresolution of the intratumoral and peritumoral regions. Features were selected via the intraclass correlation coefficient, t tests, Pearson correlation coefficients and least absolute shrinkage and selection operator regression methods, and models were constructed via various machine learning methods. The best model was selected, and the radiomics score (Radscore) was calculated. A nomogram incorporating the Radscore and clinical risk factors was constructed. The predictive performance of each model was evaluated via receiver operating characteristic (ROC) curves and calibration curves, and decision curve analysis was used to assess the clinical benefits.
RESULTS In total, 164 HCC patients, namely, 104 patients with high Ki-67 expression and 60 with low Ki-67 expression, were included. Compared with the models in which only intratumoral or peritumoral features were used, the fusion model in which intratumoral and peritumoral features were combined demonstrated stronger predictive ability. Moreover, the clinical-radiomics model including the Radscore and clinical features had higher predictive performance than did the fusion model (area under the ROC curve = 0.848 vs 0.780 in the training group, area under the ROC curve = 0.830 vs 0.760 in the validation group). The calibration curve showed good consistency between the predicted probability and the actual probability, and the decision curve further confirmed its clinical benefit.
CONCLUSION A machine learning model based on the radiomic features of the intratumoral and peritumoral regions on superresolution computed tomography in conjunction with clinical factors can accurately evaluate Ki-67 expression. The model provides valuable assistance in selecting treatment strategies for HCC patients and contributes to research on neoadjuvant therapy for liver cancer.
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Affiliation(s)
- Zi-Wei Zhu
- China Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110000, Liaoning Province, China
| | - Jun Wu
- Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
| | - Yang Guo
- Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
| | - Qiong-Yuan Ren
- Dalian Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110000, Liaoning Province, China
| | - Dong-Ning Li
- Dalian Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110000, Liaoning Province, China
| | - Ze-Yu Li
- China Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110000, Liaoning Province, China
| | - Lei Han
- Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
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Zhang Y, Sheng R, Qian X, Wang H, Wu F, Dai H, Song M, Yang C, Zhou J, Zhang W, Zeng M. Deep learning empowered gadolinium-free contrast-enhanced abbreviated MRI for diagnosing hepatocellular carcinoma. JHEP Rep 2025; 7:101392. [PMID: 40337547 PMCID: PMC12056404 DOI: 10.1016/j.jhepr.2025.101392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2024] [Revised: 03/04/2025] [Accepted: 03/07/2025] [Indexed: 05/09/2025] Open
Abstract
Background & Aims By reducing some magnetic resonance imaging (MRI) sequences, abbreviated MRI (aMRI) has shown extensive promise for detecting hepatocellular carcinoma (HCC). We aim to develop deep learning (DL)-based gadolinium-free contrast-enhanced (CE) aMRI protocols (DL-aMRI) for detecting HCC. Methods In total, 1,769 patients (913 with HCC) were retrospectively included from three institutions for training, testing, and external validation. Stable diffusion-based DL models were trained to generate CE-MRI, including T1-weighted arterial, portal venous, transitional, and hepatobiliary phase images (AP-syn, VP-syn, TP-syn, and HBP-syn, respectively). Non-contrast-MRI (NC-MRI), including T2-weighted, diffusion-weighted, and pre-contrast T1-weighted (Pre) sequences, along with either actual or DL-synthesized CE-MRI (AP, VP, TP, and HBP or AP-syn, VP-syn, TP-syn, and HBP-syn), were used to create conventional complete MRI (cMRI) and DL-aMRI protocols. An inter-method comparison of image quality between DL-aMRI and cMRI was conducted using a non-inferiority test. The sensitivity and specificity of DL-aMRI and cMRI for detecting HCC were statistically compared using the non-inferiority test and generalized estimating equations models. Results DL-aMRI showed a remarkable reduction in acquisition time compared with cMRI (4.1 vs. 28.1 min). The image quality of DL-synthesized CE-MRI was not inferior to that of actual CE-MRI (p <0.001). There was an excellent inter-method agreement between the HCC sizes measured by the two protocols (R2 = 0.9436-0.9683). The pooled sensitivity and specificity of cMRI and DL-aMRI were 0.899 and 0.925 and 0.866 and 0.922, respectively. No significant differences were found between the sensitivity and specificity of the two protocols. Conclusions The proposed DL-aMRI could facilitate precise HCC diagnosis with no need for contrast agents, a substantial reduction in acquisition time, and preservation of both NC-MRI and CE-MRI data. DL-aMRI may serve as a valuable tool for HCC diagnosing. Impact and implications In this multi-center study involving 1,769 participants, we developed a generative deep learning-based abbreviated MRI (DL-aMRI) strategy that provides an efficient, contrast-agent-free alternative for detecting HCC with accuracy comparable to that of conventional complete MRI, significantly reducing acquisition time from 28.1 min to just 4.1 min. This strategy is valuable for clinicians who face significant workloads resulting from long MRI scanning times and the potential adverse effects of contrast agents, as well as for researchers focused on developing cost-effective and accessible diagnostic tools for HCC detection. The proposed DL-aMRI protocol has practical implications for clinical settings, enhancing diagnostic efficiency while maintaining high image quality, eliminating the need for contrast agents and ultimately benefiting patients and healthcare providers.
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Affiliation(s)
- Yunfei Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
| | - Ruofan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xianling Qian
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Heqing Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Fei Wu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Haoran Dai
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mingyue Song
- Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Weiguo Zhang
- Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
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Chen S, Zhang Y, Su Y, Tian J, Chen Y, Tang W, Fan Y, Jin C, He Y, Xu Y, Hu H, Guo Y, Li J. Habitat Radiomics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Assessing Axillary Lymph Node Burden in Clinical T1-T2 Stage Breast Cancer: A Multicenter and Interpretable Study. J Magn Reson Imaging 2025. [PMID: 40256826 DOI: 10.1002/jmri.29796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2025] [Revised: 04/02/2025] [Accepted: 04/03/2025] [Indexed: 04/22/2025] Open
Abstract
BACKGROUND Axillary lymph node burden(ALNB) is a critical factor in determining treatment strategies for clinical T1-T2 (cT1-T2) stage breast cancer. However, as ALNB assessment relies on invasive procedures, exploring non-invasive methods is essential. PURPOSE To develop and validate a habitat radiomics model for assessing ALNB in cT1-T2 breast cancer, incorporating radiogenomic data to improve interpretability. STUDY TYPE Retrospective. POPULATION 468 patients with cT1-T2 stage breast cancer from two institutions and The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) were included. The cohort was divided into training (n = 173), internal validation (n = 58), external validation (n = 130), and TCGA-BRCA sets (n = 107). Patients were categorized into high nodal burden (HNB; > 3 positive lymph nodes) and non-HNB (≤ 3 positive lymph nodes) groups. FIELD STRENGTH/SEQUENCE 1.5-T MRI and 3.0-T MRI, and three-dimensional dynamic contrast-enhanced T1-weighted gradient-echo sequences. ASSESSMENT Two logistic regression models were developed using habitat-based and clinical features. Model performance was evaluated using the AUC. SHapley Additive exPlanations (SHAP) analysis was employed to identify key features. Radiogenomic analysis, including gene set enrichment and drug sensitivity assessments, was conducted using transcriptomic data from the TCGA-BRCA set. STATISTICAL TESTS Pearson correlation, Mann-Whitney U, genetic algorithm, logistic regression, AUC analysis, delong test, and SHAP analysis. A p-value < 0.05 was considered statistically significant. RESULTS The Habitat model outperformed the Clinical model (AUCs: 0.840-0.932 vs. 0.558-0.673). The SHAP analysis was used to rank feature importance, with subregion 3 showing the highest average SHAP value. Radiogenomic analysis indicated upregulation of the KEGG ribosome pathway in the HNB group and identified differential drug sensitivity profiles among risk groups. DATA CONCLUSION The Habitat model has the potential to assess ALNB in cT1-T2 breast cancer and assist radiologists in axillary diagnosis, which may help reduce the need for unnecessary ALN dissection. EVIDENCE LEVEL 3. Technical Efficacy: Stage 2.
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Affiliation(s)
- Siyi Chen
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yue Zhang
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yuqi Su
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jie Tian
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yongxin Chen
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
| | - Wenjie Tang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
| | - Yaheng Fan
- Shukun Technology Co., Ltd, Beijing, China
| | - Chen Jin
- College of Computer Science and Technology, Zhejiang University of Technology, Zhejiang, China
| | - Yangcheng He
- Department of Ultrasound, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
| | | | - Hong Hu
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
| | - Junping Li
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
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Huang Z, Huang W, Jiang L, Zheng Y, Pan Y, Yan C, Ye R, Weng S, Li Y. Decision Fusion Model for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Multi-MR Habitat Imaging and Machine-Learning Classifiers. Acad Radiol 2025; 32:1971-1980. [PMID: 39472207 DOI: 10.1016/j.acra.2024.10.007] [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: 08/25/2024] [Revised: 09/30/2024] [Accepted: 10/10/2024] [Indexed: 11/20/2024]
Abstract
RATIONALE AND OBJECTIVES Accurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep-learning radiomics, feature fusion, and decision fusion models based on multi-region MR habitat imaging using six machine-learning classifiers. MATERIALS AND METHODS We retrospectively included 300 HCC patients. The intratumoral and peritumoral regions were segmented into distinct habitats, from which radiomics and deep-learning features were extracted using arterial phase MR images. To reduce feature dimensionality, we applied intra-class correlation coefficient (ICC) analysis, Pearson correlation coefficient (PCC) filtering, and recursive feature elimination (RFE). Based on the selected optimal features, prediction models were constructed using decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost (XGB) classifiers. Additionally, fusion models were developed utilizing both feature fusion and decision fusion strategies. The performance of these models was validated using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis. RESULTS The decision fusion model (VOI-Peri10-1) using LR and integrating clinicoradiologic, radiomics, and deep-learning features achieved the highest AUC of 0.808 (95% confidence interval [CI]: 0.807-0.912) in the test cohort, with good calibration (Hosmer-Lemeshow test, P > 0.050) and clinical net benefit. CONCLUSION The LR-based decision fusion model integrating clinicoradiologic, radiomics, and deep-learning features shows promise for preoperative prediction of MVI in HCC, aiding in patient outcome predictions and personalized treatment planning.
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Affiliation(s)
- Zhenhuan Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.); Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian 364000, China (Z.H.)
| | - Wanrong Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Lu Jiang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Yao Zheng
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Yifan Pan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Chuan Yan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Rongping Ye
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.)
| | - Shuping Weng
- Department of Radiology, Fujian Maternity and Child Health Hospital, Fujian Medical University, Fuzhou, Fujian 350001, China (S.W.)
| | - Yueming Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., W.H., L.J., Y.Z., Y.P., C.Y., R.Y., Y.L.); Department of Radiology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350212, China (Y.L.); Key Laboratory of Radiation Biology of Fujian higher education institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350005, China (Y.L.).
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Shen J, Li Q, Li L, Lu T, Han J, Xie Z, Wang P, Cao Z, Zeng M, Zhou J, Yu T, Xu Y, Sun H. Contrast-enhanced MRI-based intratumoral heterogeneity assessment for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma. Insights Imaging 2025; 16:76. [PMID: 40159327 PMCID: PMC11955437 DOI: 10.1186/s13244-025-01956-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2025] [Accepted: 03/17/2025] [Indexed: 04/02/2025] Open
Abstract
OBJECTIVES To develop and validate a contrast-enhanced MRI-based intratumoral heterogeneity (ITH) model for predicting lymph node (LN) metastasis in resectable pancreatic ductal adenocarcinoma (PDAC). METHODS Lesions were encoded into different habitats based on enhancement ratios at arterial, venous, and delayed phases of contrast-enhanced MRI. Habitat models on enhanced ratio mapping and single sequences, radiomic models, and clinical models were developed for evaluating LN metastasis. The performance of the models was evaluated via different metrics. Additionally, patients were stratified into high-risk and low-risk groups based on an ensembled model to assess prognosis after adjuvant therapy. RESULTS We developed an ensembled radiomics-habitat-clinical (RHC) model that integrates radiomics, habitat, and clinical data for precise prediction of LN metastasis in PDAC. The RHC model showed strong predictive performance, with area under the curve (AUC) values of 0.805, 0.779, and 0.615 in the derivation, internal validation, and external validation cohorts, respectively. Using an optimal threshold of 0.46, the model effectively stratified patients, revealing significant differences in recurrence-free survival and overall survival (OS) (p = 0.004 and p < 0.001). Adjuvant therapy improved OS in the high-risk group (p = 0.004), but no significant benefit was observed in the low-risk group (p = 0.069). CONCLUSION We developed an MRI-based ITH model that provides reliable estimates of LN metastasis for resectable PDAC and may offer additional value in guiding clinical decision-making. CRITICAL RELEVANCE STATEMENT This ensemble RHC model facilitates preoperative prediction of LN metastasis in resectable PDAC using contrast-enhanced MRI. This offers a foundation for enhanced prognostic assessment and supports the management of personalized adjuvant treatment strategies. KEY POINTS MRI-based habitat models can predict LN metastasis in PDAC. Both the radiomics model and clinical characteristics were useful for predicting LN metastasis in PDAC. The RHC models have the potential to enhance predictive accuracy and inform personalized therapeutic decisions.
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Affiliation(s)
- Junjian Shen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
| | - Qing Li
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
| | - Lei Li
- Department of Radiology, Fengyang County People's Hospital, Chuzhou, China
| | - Tianyu Lu
- Department of Radiology, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jun Han
- Department of Radiology, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Zongyu Xie
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Peng Wang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, P.R. China
| | - Zirui Cao
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen Municipal Clinical Research Center for Medical Imaging, Fujian Province Key Clinical Specialty for Medical Imaging, Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence, Xiamen, China
| | - Tianzhu Yu
- Department of Interventional Radiology, Zhongshan Hospital, Shanghai, China
| | - Yaolin Xu
- Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Haitao Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China.
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Wang X, Deng C, Kong R, Gong Z, Dai H, Song Y, Wu Y, Bi G, Ai C, Bi Q. Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma. Acad Radiol 2025; 32:1476-1487. [PMID: 39368914 DOI: 10.1016/j.acra.2024.09.039] [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: 08/16/2024] [Revised: 09/13/2024] [Accepted: 09/16/2024] [Indexed: 10/07/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the validity of multiparametric MRI-based intratumoral and peritumoral habitat imaging for predicting cervical stromal invasion (CSI) in patients with early-stage endometrial carcinoma (EC) and to compare the performance of structural and functional habitats. MATERIALS AND METHODS The preoperative MRI and clinical data of 680 patients with early-stage EC from three centers were retrospectively analyzed. Based on cohort-level, gaussian mixture model (GMM) algorithm was used for habitat clustering of MRI images. Structural habitats were clustered using T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI), and functional habitats were clustered using apparent diffusion coefficient (ADC) mapping and CE-T1WI. Habitat parameters were extracted from four volumes of interest (VOIs): intratumoral regions (ROI), peritumoral loops of 3 mm dilation (L3), intratumoral regions + peritumoral loops of 3 mm dilation (R3), and peritumoral loops of 3 mm dilation + peritumoral loops of 3 mm erosion (DE3). Clinical-habitat models were constructed by combining clinical independent predictors and optimal habitat models. The model performance was evaluated by the area under the curve (AUC). RESULTS Deep myometrial invasion (DMI) was an independent predictor. L3 models showed the best performance for both structural and functional habitats, and the L3 functional habitat model had the highest average AUC (0.807) in external test groups, and the average AUC increased to 0.815 when combing with the clinical independent predictor. CONCLUSION Multiparametric MRI-based intratumoral and peritumoral habitat imaging provides a noninvasive approach to predict CSI in EC patients. The combination of the clinical predictor with the L3 functional habitat model improved predictive performance.
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Affiliation(s)
- Xianhong Wang
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.); Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B)
| | - Cheng Deng
- Department of Radiology, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650101, China (C.D.)
| | - Ruize Kong
- Department of Vascular Surgery, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (R.K.); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Zhimei Gong
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Hongying Dai
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Yang Song
- MR Research Collaboration, Siemens Healthineers, Shanghai 201318, China (Y.S.)
| | - Yunzhu Wu
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China (Y.W.)
| | - Guoli Bi
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Conghui Ai
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China (C.A.)
| | - Qiu Bi
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.).
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Michelotti FC. Editorial for "The Association Between Tumor Radiomic Analysis and Peritumor Habitat-Derived Radiomic Analysis on Gadoxetate Disodium-Enhanced MRI With Microvascular Invasion in Hepatocellular Carcinoma". J Magn Reson Imaging 2025; 61:1440-1441. [PMID: 39082845 DOI: 10.1002/jmri.29551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 06/04/2024] [Indexed: 02/08/2025] Open
Affiliation(s)
- Filippo C Michelotti
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
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Huang Z, Pan Y, Huang W, Pan F, Wang H, Yan C, Ye R, Weng S, Cai J, Li Y. Predicting Microvascular Invasion and Early Recurrence in Hepatocellular Carcinoma Using DeepLab V3+ Segmentation of Multiregional MR Habitat Images. Acad Radiol 2025:S1076-6332(25)00109-6. [PMID: 40011096 DOI: 10.1016/j.acra.2025.02.006] [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: 01/10/2025] [Revised: 02/05/2025] [Accepted: 02/05/2025] [Indexed: 02/28/2025]
Abstract
RATIONALE AND OBJECTIVES Accurate identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for treatment and prognosis. Single-modality and feature fusion models using manual segmentation fail to provide insights into MVI. This study aims to develop a DeepLab V3+ model for automated segmentation of HCC magnetic resonance (MR) images and a decision fusion model to predict MVI and early recurrence (ER). MATERIALS AND METHODS This retrospective study included 209 HCC patients (146 in the training and 63 in the test cohorts). The performance of DeepLab V3+ for HCC MR image segmentation was evaluated using Dice Loss and F1 score. Intraclass correlation coefficients (ICCs) assessed feature extraction reliability. Spearman's correlation analyzed the relationship between tumor volumes from automated and manual segmentation, with agreement evaluated using Bland-Altman plots. Model performance was assessed using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis. A nomogram predicted ER of HCC after surgery, with Kaplan-Meier analysis for 2-year recurrence-free survival (RFS). RESULTS The DeepLab V3+ model demonstrated high segmentation accuracy, with strong agreement in feature extraction (ICC: 0.802-0.999). The decision fusion model achieved AUCs of 0.968 and 0.878 for MVI prediction, and the nomogram for predicting ER yielded AUCs of 0.782 and 0.690 in the training and test cohorts, respectively, with significant RFS differences between the risk groups. CONCLUSION The DeepLab V3+ model accurately segmented HCC. The decision fusion model significantly improved MVI prediction, and the nomogram offered valuable insights into recurrence risk for clinical decision-making. AVAILABILITY OF DATA AND MATERIALS The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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Affiliation(s)
- Zhenhuan Huang
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian 364000, China (Z.H.); Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Yifan Pan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Wanrong Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Feng Pan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Huifang Wang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Chuan Yan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Rongping Ye
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.)
| | - Shuping Weng
- Department of Radiology, Fujian Maternity and Child Health Hospital, Fujian Medical University, Fuzhou, Fujian 350001, China (S.W.)
| | - Jingyi Cai
- School of Medical Imaging, Fujian Medical University, Fuzhou, Fujian 350001, China (J.C.)
| | - Yueming Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China (Z.H., Y.P., W.H., F.P., H.W., C.Y., R.Y., Y.L.); Department of Radiology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350212, China (Y.L.); Key Laboratory of Radiation Biology of Fujian higher education institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350005, China (Y.L.).
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Zang Y, Zheng F, Feng L, Shi X, Chen X. Preoperatively Predicting PIT1 Expression in Pituitary Adenomas Using Habitat, Intra-tumoral and Peri-tumoral Radiomics Based on MRI. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025:10.1007/s10278-024-01376-4. [PMID: 39904941 DOI: 10.1007/s10278-024-01376-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 11/10/2024] [Accepted: 12/08/2024] [Indexed: 02/06/2025]
Abstract
The study aimed to predict expression of pituitary transcription factor 1 (PIT1) in pituitary adenomas using habitat, intra-tumoral and peri-tumoral radiomics models. A total of 129 patients with pituitary adenoma (training set, n = 103; test set, n = 26) were retrospectively enrolled. A total of 12, 18, 14, 13, and 14 radiomics features were selected from the ROIintra, ROIintra+peri (ROIintra+2mm, ROIintra+4mm, ROIintra+6mm), and ROIhabitat, respectively. Then, three machine learning algorithms were employed to develop radiomic models, including logistic regression (LR), support vector machines (SVM), and multilayer perceptron (MLP). The performances of the intra-tumoral, combined intra-tumoral and peri-tumoral, and habitat models were evaluated. The peritumoral region (ROI2mm, ROI4mm, ROI6mm) of the combined model with the highest performance was individually selected for further peritumoral analysis. Moreover, a deep learning radiomics nomogram (DLRN) was constructed incorporating clinical characteristics and the peri-tumoral and habitat models for individual prediction. The combined modelintra+2mm based on ROIintra+2mm achieved a better performance (AUC, 0.800) than that of the intra-tumoral model alone (AUC, 0.731). And the habitat model showed a higher performance (AUC, 0.806) than that of the intra-tumoral model. In addition, the performance of the peri-tumoral model based on ROI2mm was 0.694 in the testing set. Furthermore, the DLRN achieved the highest performance of 0.900 in the test set. The DLRN showed the best performance for PIT1 expression in pituitary adenomas, followed by the habitat, combined modelintra+2mm, intra-tumoral model, and peri-tumoral model based on ROI2mm, respectively. These different models are helpful for the model choice in clinical work.
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Affiliation(s)
- Yuying Zang
- Department of Radiology, The Affiliated Children's Hospital, Capital Institute of Pediatrics, Beijing, China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fei Zheng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Limei Feng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xinyao Shi
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuzhu Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Lanza C, Ascenti V, Amato GV, Pellegrino G, Triggiani S, Tintori J, Intrieri C, Angileri SA, Biondetti P, Carriero S, Torcia P, Ierardi AM, Carrafiello G. All You Need to Know About TACE: A Comprehensive Review of Indications, Techniques, Efficacy, Limits, and Technical Advancement. J Clin Med 2025; 14:314. [PMID: 39860320 PMCID: PMC11766109 DOI: 10.3390/jcm14020314] [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: 11/10/2024] [Revised: 12/17/2024] [Accepted: 12/28/2024] [Indexed: 01/27/2025] Open
Abstract
Transcatheter arterial chemoembolization (TACE) is a proven and widely accepted treatment option for hepatocellular carcinoma and it is recommended as first-line non-curative therapy for BCLC B/intermediate HCC (preserved liver function, multifocal, no cancer-related symptoms) in patients without vascular involvement. Different types of TACE are available nowadays, including TAE, c-TACE, DEB-TACE, and DSM-TACE, but at present there is insufficient evidence to recommend one TACE technique over another and the choice is left to the operator. This review then aims to provide a comprehensive overview of the current literature on indications, types of procedures, safety, and efficacy of different TACE treatments.
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Affiliation(s)
- Carolina Lanza
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Velio Ascenti
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Gaetano Valerio Amato
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Giuseppe Pellegrino
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Sonia Triggiani
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Jacopo Tintori
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Cristina Intrieri
- Postgraduate School in Diangostic Imaging, Università degli Studi di Siena, 20122 Milan, Italy;
| | - Salvatore Alessio Angileri
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Pierpaolo Biondetti
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Serena Carriero
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Pierluca Torcia
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Anna Maria Ierardi
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Gianpaolo Carrafiello
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
- Faculty of Health Science, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
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11
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Li B, Yu Y, Xia T. Editorial for "Intertumoral Heterogeneity Based on MRI Radiomic Features Estimates Recurrence in Hepatocellular Carcinoma". J Magn Reson Imaging 2025; 61:182-183. [PMID: 38712658 DOI: 10.1002/jmri.29433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 04/01/2024] [Indexed: 05/08/2024] Open
Affiliation(s)
- Binrong Li
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yaoyao Yu
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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12
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Zhu Y, Zheng D, Xu S, Chen J, Wen L, Zhang Z, Ruan H. Intratumoral habitat radiomics based on magnetic resonance imaging for preoperative prediction treatment response to neoadjuvant chemotherapy in nasopharyngeal carcinoma. Jpn J Radiol 2024; 42:1413-1424. [PMID: 39162780 DOI: 10.1007/s11604-024-01639-8] [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: 06/06/2024] [Accepted: 07/27/2024] [Indexed: 08/21/2024]
Abstract
PURPOSE The aim of this study is to determine intratumoral habitat regions from multi-sequences magnetic resonance imaging (MRI) and to assess the value of those regions for prediction of patient response to neoadjuvant chemotherapy (NAC) in nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS Two hundred and ninety seven patients with NPC were enrolled. Multi-sequences MRI data were used to outline three-dimensional volumes of interest (VOI) of the whole tumor. The original imaging data were divided into two groups, which were resampled to an isotropic resolution of 1 × 1 × 1 mm3 (group_1mm) and 3 × 3 × 3 mm3 (group_3mm). Nineteen radiomics features were computed for each voxel of three sequences in group_3mm, within the tumor region to extract local information. Then, k-means clustering was implemented to segment the whole tumor regions in two groups. After radiomics features were extracted and dimension reduction, habitat models were built using Multi-Layer Perceptron (MLP) algorithm. RESULTS Only T stage was included as the clinical model. The habitat3mm model, which included 10 radiomics features, achieved AUCs of 0.752 and 0.724 in the training and validation cohorts, respectively. Given the slightly better outcome of habitat3mm model, nomogram was developed in combination with habitat3mm model and T stage with the AUC of 0.749 and 0.738 in the training and validation cohorts. The decision curve analysis provides further evidence of the nomogram's clinical practicality. CONCLUSIONS A nomogram based on intratumoral habitat predicts the efficacy of NAC in NPC patients, offering the potential to improve both the treatment plan and patient outcomes.
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Affiliation(s)
- Yuemin Zhu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Dechun Zheng
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China.
| | - Shugui Xu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Jianwei Chen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Liting Wen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Zhichao Zhang
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Huiping Ruan
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
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13
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Tang Z, Wang W, Gao B, Liu X, Liu X, Zhuo Y, Du J, Ai F, Yang X, Gu H. Unveiling Tim-3 immune checkpoint expression in hepatocellular carcinoma through abdominal contrast-enhanced CT habitat radiomics. Front Oncol 2024; 14:1456748. [PMID: 39582537 PMCID: PMC11581969 DOI: 10.3389/fonc.2024.1456748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2024] [Accepted: 10/11/2024] [Indexed: 11/26/2024] Open
Abstract
Introduction Immune checkpoint inhibitors (ICIs) are important systemic therapeutic agents for hepatocellular carcinoma (HCC), among which T-cell immunoglobulin and mucin-domain containing protein 3 (Tim-3) is considered an emerging target for ICI therapy. This study aims to evaluate the prognostic value of Tim-3 expression and develop a predictive model for Tim-3 infiltration in HCC. Methods We collected data from 424 HCC patients in The Cancer Genome Atlas (TCGA) and data from 102 pathologically confirmed HCC patients from our center for prognostic analysis. Multivariate Cox regression analyses were performed on both datasets to determine the prognostic significance of Tim-3 expression. In radiomics analysis, we used the K-means algorithm to cluster regions of interest in arterial phase enhancement and venous phase enhancement images from patients at our center. Radiomic features were extracted from three subregions as well as the entire tumor using pyradiomics. Five machine learning methods were employed to construct Habitat models based on habitat features and Rad models based on traditional radiomic features. The predictive performance of the models was compared using ROC curves, DCA curves, and calibration curves. Results Multivariate Cox analyses from both our center and the TCGA database indicated that high Tim-3 expression is an independent risk factor for poor prognosis in HCC patients. Higher levels of Tim-3 expression were significantly associated with worse prognosis. Among the ten models evaluated, the Habitat model constructed using the LightGBM algorithm showed the best performance in predicting Tim-3 expression status (training set vs. test set AUC 0.866 vs. 0.824). Discussion This study confirmed the importance of Tim-3 as a prognostic marker in HCC. The habitat radiomics model we developed effectively predicted intratumoral Tim-3 infiltration, providing valuable insights for the evaluation of ICI therapy in HCC patients.
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Affiliation(s)
- Zhishen Tang
- Department of Pediatric Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Wei Wang
- Department of Pediatric Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Bo Gao
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xuyang Liu
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Xiangyu Liu
- Department of Pediatric Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Yingquan Zhuo
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Jun Du
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Fujun Ai
- Department of Pathology and Pathophysiology, Guizhou Medical University, Guiyang, China
| | - Xianwu Yang
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Huajian Gu
- Department of Pediatric Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China
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Huang Y, Qian H. Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis. J Hepatocell Carcinoma 2024; 11:2159-2168. [PMID: 39525830 PMCID: PMC11546143 DOI: 10.2147/jhc.s493227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2024] [Accepted: 10/25/2024] [Indexed: 11/16/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary liver cancer and is associated with high mortality rates due to late detection and aggressive progression. Peritumoral radiomics, an emerging technique that quantitatively analyzes the tissue surrounding the tumor, has shown significant potential in enhancing the management of HCC. This paper examines the role of peritumoral radiomics in improving diagnostic accuracy, guiding personalized treatment strategies, and refining prognostic assessments. By offering unique insights into the tumor microenvironment, peritumoral radiomics enables more precise patient stratification and informs clinical decision-making. However, the integration of peritumoral radiomics into routine clinical practice faces several challenges. Addressing these challenges through continued research and innovation is crucial for the successful implementation of peritumoral radiomics in HCC management, ultimately leading to improved patient outcomes.
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Affiliation(s)
- Yanhua Huang
- Department of Ultrasound, Shaoxing People’s Hospital, Shaoxing, People’s Republic of China
| | - Hongwei Qian
- Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People’s Hospital, Shaoxing, People’s Republic of China
- Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, People’s Republic of China
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Liu Y, Liu Z, Li X, Zhou W, Lin L, Chen X. Non-invasive assessment of response to transcatheter arterial chemoembolization for hepatocellular carcinoma with the deep neural networks-based radiomics nomogram. Acta Radiol 2024; 65:535-545. [PMID: 38489805 DOI: 10.1177/02841851241229185] [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] [Indexed: 03/17/2024]
Abstract
BACKGROUND Transcatheter arterial chemoembolization (TACE) is a mainstay treatment for intermediate and advanced hepatocellular carcinoma (HCC), with the potential to enhance patient survival. Preoperative prediction of postoperative response to TACE in patients with HCC is crucial. PURPOSE To develop a deep neural network (DNN)-based nomogram for the non-invasive and precise prediction of TACE response in patients with HCC. MATERIAL AND METHODS We retrospectively collected clinical and imaging data from 110 patients with HCC who underwent TACE surgery. Radiomics features were extracted from specific imaging methods. We employed conventional machine-learning algorithms and a DNN-based model to construct predictive probabilities (RScore). Logistic regression helped identify independent clinical risk factors, which were integrated with RScore to create a nomogram. We evaluated diagnostic performance using various metrics. RESULTS Among the radiomics models, the DNN_LASSO-based one demonstrated the highest predictive accuracy (area under the curve [AUC] = 0.847, sensitivity = 0.892, specificity = 0.791). Peritumoral enhancement and alkaline phosphatase were identified as independent risk factors. Combining RScore with these clinical factors, a DNN-based nomogram exhibited superior predictive performance (AUC = 0.871, sensitivity = 0.844, specificity = 0.873). CONCLUSION In this study, we successfully developed a deep learning-based nomogram that can noninvasively and accurately predict TACE response in patients with HCC, offering significant potential for improving the clinical management of HCC.
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Affiliation(s)
- Yushuang Liu
- Guangdong Medical University, Zhanjiang, PR China
- Department of Radiology and Imaging, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, PR China
| | - Zilin Liu
- Guangdong Medical University, Zhanjiang, PR China
- Department of Radiology and Imaging, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, PR China
| | - Xinhua Li
- Guangdong Medical University, Zhanjiang, PR China
- Department of Radiology, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, PR China
| | - Weiwen Zhou
- Guangdong Medical University, Zhanjiang, PR China
- Department of Radiology and Imaging, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, PR China
| | - Lifu Lin
- Guangdong Medical University, Zhanjiang, PR China
- Department of Radiology and Imaging, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, PR China
| | - Xiaodong Chen
- Guangdong Medical University, Zhanjiang, PR China
- Department of Radiology, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, PR China
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