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Dong M, Li C, Zhang L, Zhou J, Xiao Y, Zhang T, Jin X, Fang Z, Zhang L, Han Y, Guan J, Weng Z, Cheng N, Wang J. Intertumoral Heterogeneity Based on MRI Radiomic Features Estimates Recurrence in Hepatocellular Carcinoma. J Magn Reson Imaging 2025; 61:168-181. [PMID: 38712652 DOI: 10.1002/jmri.29428] [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: 02/17/2024] [Revised: 04/16/2024] [Accepted: 04/16/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) heterogeneity impacts prognosis, and imaging is a potential indicator. PURPOSE To characterize HCC image subtypes in MRI and correlate subtypes with recurrence. STUDY TYPE Retrospective. POPULATION A total of 440 patients (training cohort = 213, internal test cohort = 140, external test cohort = 87) from three centers. FIELD STRENGTH/SEQUENCE 1.5-T/3.0-T, fast/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, contrast-enhanced three-dimensional gradient-recalled-echo T1-weighted with extracellular agents (Gd-DTPA, Gd-DTPA-BMA, and Gd-BOPTA). ASSESSMENT Three-dimensional volume-of-interest of HCC was contoured on portal venous phase, then coregistered with precontrast and late arterial phases. Subtypes were identified using non-negative matrix factorization by analyzing radiomics features from volume-of-interests, and correlated with recurrence. Clinical (demographic and laboratory data), pathological, and radiologic features were compared across subtypes. Among clinical, radiologic features and subtypes, variables with variance inflation factor above 10 were excluded. Variables (P < 0.10) in univariate Cox regression were included in stepwise multivariate analysis. Three recurrence estimation models were built: clinical-radiologic model, subtype model, hybrid model integrating clinical-radiologic characteristics, and subtypes. STATISTICAL TESTS Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, Fisher's exact test, Kaplan-Meier curves, log-rank test, concordance index (C-index). Significance level: P < 0.05. RESULTS Two subtypes were identified across three cohorts (subtype 1:subtype 2 of 86:127, 60:80, and 36:51, respectively). Subtype 1 showed higher microvascular invasion (MVI)-positive rates (53%-57% vs. 26%-31%), and worse recurrence-free survival. Hazard ratio (HR) for the subtype is 6.10 in subtype model. Clinical-radiologic model included alpha-fetoprotein (HR: 3.01), macrovascular invasion (HR: 2.32), nonsmooth tumor margin (HR: 1.81), rim enhancement (HR: 3.13), and intratumoral artery (HR: 2.21). Hybrid model included alpha-fetoprotein (HR: 2.70), nonsmooth tumor margin (HR: 1.51), rim enhancement (HR: 3.25), and subtypes (HR: 5.34). Subtype model was comparable to clinical-radiologic model (C-index: 0.71-0.73 vs. 0.71-0.73), but hybrid model outperformed both (C-index: 0.77-0.79). CONCLUSION MRI radiomics-based clustering identified two HCC subtypes with distinct MVI status and recurrence-free survival. Hybrid model showed superior capability to estimate recurrence. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Mengshi Dong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Lina Zhang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jinhui Zhou
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuanqiang Xiao
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Tianhui Zhang
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
| | - Xin Jin
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zebin Fang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Linqi Zhang
- Department of Radiology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Yu Han
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jiexia Guan
- Department of Pathology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zijin Weng
- Department of Pathology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Na Cheng
- Department of Pathology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jin Wang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
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Sheng R, Zheng B, Zhang Y, Sun W, Yang C, Han J, Zeng M, Zhou J. MRI-based microvascular invasion prediction in mass-forming intrahepatic cholangiocarcinoma: survival and therapeutic benefit. Eur Radiol 2024:10.1007/s00330-024-11296-0. [PMID: 39699676 DOI: 10.1007/s00330-024-11296-0] [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: 09/12/2024] [Revised: 10/23/2024] [Accepted: 11/16/2024] [Indexed: 12/20/2024]
Abstract
OBJECTIVES To establish an MRI-based model for microvascular invasion (MVI) prediction in mass-forming intrahepatic cholangiocarcinoma (MF-iCCA) and further evaluate its potential survival and therapeutic benefit. METHODS One hundred and fifty-six pathologically confirmed MF-iCCAs with traditional surgery (121 in training and 35 in validation cohorts), 33 with neoadjuvant treatment and 57 with first-line systemic therapy were retrospectively included. Univariate and multivariate regression analyses were performed to identify the independent predictors for MVI in the traditional surgery group, and an MVI-predictive model was constructed. Survival analyses were conducted and compared between MRI-predicted MVI-positive and MVI-negative MF-iCCAs in different treatment groups. RESULTS Tumor multinodularity (odds ratio = 4.498, p < 0.001) and peri-tumor diffusion-weighted hyperintensity (odds ratio = 4.163, p < 0.001) were independently significant variables associated with MVI. AUC values for the predictive model were 0.760 [95% CI 0.674, 0.833] in the training cohort and 0.757 [95% CI 0.583, 0.885] in the validation cohort. Recurrence-free survival or progression-free survival of the MRI-predicted MVI-positive patients was significantly shorter than the MVI-negative patients in all three treatment groups (log-rank p < 0.001 to 0.046). The use of neoadjuvant therapy was not associated with improved postoperative recurrence-free survival for high-risk MF-iCCA patients in both MRI-predicted MVI-positive and MVI-negative groups (log-rank p = 0.79 and 0.27). Advanced MF-iCCA patients of the MRI-predicted MVI-positive group had significantly worse objective response rate than the MVI-negative group with systemic therapy (40.91% vs 76.92%, χ2 = 5.208, p = 0.022). CONCLUSION The MRI-based MVI-predictive model could be a potential biomarker for personalized risk stratification and survival prediction in MF-iCCA patients with varied therapies and may aid in candidate selection for systemic therapy. KEY POINTS Question Identifying intrahepatic cholangiocarcinoma (iCCA) patients at high risk for microvascular invasion (MVI) may inform prognostic risk stratification and guide clinical treatment decision. Findings We established an MRI-based predictive model for MVI in mass-forming-iCCA, integrating imaging features of tumor multinodularity and peri-tumor diffusion-weighted hyperintensity. Clinical relevance The MRI-based MVI-predictive model could be a potential biomarker for personalized risk stratification and survival prediction across varied therapies and may aid in therapeutic candidate selection for systemic therapy.
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Affiliation(s)
- Ruofan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Beixuan Zheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yunfei Zhang
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Jing Han
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
- Shanghai Institute of Medical Imaging, Shanghai, China.
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Fujian, China
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Sheng R, Zheng B, Zhang Y, Sun W, Yang C, Zeng M. A preliminary study of developing an MRI-based model for postoperative recurrence prediction and treatment direction of intrahepatic cholangiocarcinoma. LA RADIOLOGIA MEDICA 2024; 129:1766-1777. [PMID: 39487376 DOI: 10.1007/s11547-024-01910-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 10/23/2024] [Indexed: 11/04/2024]
Abstract
PURPOSE To establish an MRI-based predictive model for postoperative recurrence in intrahepatic cholangiocarcinoma (iCCA) and further to evaluate the model utility in treatment direction for neoadjuvant and adjuvant therapies. MATERIALS AND METHODS Totally 114 iCCA patients with curative surgery were retrospectively included, including 38 patients in the neoadjuvant treatment, traditional surgery, and adjuvant treatment groups for each. Predictive variables associated with postoperative recurrence were identified using univariate and multivariate Cox regression analyses, and a prognostic model was formulated. Recurrence-free survival (RFS) curves were compared using log-rank test between MRI-predicted high-risk and low-risk iCCAs stratified by the optimal threshold. RESULTS Tumor multiplicity (hazard ratio (HR) = 1.671 [95%CI 1.036, 2.695], P = 0.035), hemorrhage (HR = 2.391 [95%CI 1.189, 4.810], P = 0.015), peri-tumor diffusion-weighted hyperintensity (HR = 1.723 [95%CI 1.085, 2.734], P = 0.021), and positive regional lymph node (HR = 2.175 [95%CI 1.295, 3.653], P = 0.003) were independently associated with postoperative recurrence; treatment group was not significantly related to recurrence (P > 0.05). Independent variables above were incorporated for the recurrence prediction model, the 1-year and 2-year time-dependent area under the curve values were 0.723 (95%CI 0.631, 0.815) and 0.725 (95%CI 0.634, 0.816), respectively. After risk stratification, the MRI-predicted high-risk iCCA patients had higher cumulative incidences of recurrence and worse RFS than the low-risk patients (P < 0.001 for both). In the MRI-predicted high-risk patients, neoadjuvant therapy was associated with improved all-stage RFS (P = 0.034), and adjuvant therapy was associated with improved RFS after 4 months (P = 0.014). CONCLUSIONS The MRI-based iCCA recurrence predictive model may serve as a decision-making tool for both personalized prognostication and therapy selection.
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Affiliation(s)
- Ruofan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
| | - Beixuan Zheng
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yunfei Zhang
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
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Zhu Y, Liu T, Chen J, Wen L, Zhang J, Zheng D. Prediction of therapeutic response to transarterial chemoembolization plus systemic therapy regimen in hepatocellular carcinoma using pretreatment contrast-enhanced MRI based habitat analysis and Crossformer model. Abdom Radiol (NY) 2024:10.1007/s00261-024-04709-7. [PMID: 39586897 DOI: 10.1007/s00261-024-04709-7] [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: 09/04/2024] [Revised: 11/14/2024] [Accepted: 11/15/2024] [Indexed: 11/27/2024]
Abstract
PURPOSE To develop habitat and deep learning (DL) models from multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) habitat images categorized using the K-means clustering algorithm. Additionally, we aim to assess the predictive value of identified regions for early evaluation of the responsiveness of hepatocellular carcinoma (HCC) patients to treatment with transarterial chemoembolization (TACE) plus molecular targeted therapies (MTT) and anti-PD-(L)1. METHODS A total of 102 patients with HCC from two institutions (A, n = 63 and B, n = 39) who received TACE plus systemic therapy were enrolled from September 2020 to January 2024. Multiple CE-MRI sequences were used to outline 3D volumes of interest (VOI) of the lesion. Subsequently, K-means clustering was applied to categorize intratumoral voxels into three distinct subgroups, based on signal intensity values of images. Using data from institution A, the habitat model was built with the ExtraTrees classifier after extracting radiomics features from intratumoral habitats. Similarly, the Crossformer model and ResNet50 model were trained on multi-channel data in institution A, and a DL model with Transformer-based aggregation was constructed to predict the response. Finally, all models underwent validation at institution B. RESULTS The Crossformer model and the habitat model both showed high area under the receiver operating characteristic curves (AUCs) of 0.869 and 0.877 (training cohort). In validation, AUC was 0.762 for the Crossformer model and 0.721 for the habitat model. CONCLUSION The habitat model and DL model based on CE-MRI possesses the capability to non-invasively predict the efficacy of TACE plus systemic therapy in HCC patients, which is critical for precision treatment and patient outcomes.
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Affiliation(s)
- Yuemin Zhu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Tao Liu
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Cancer Institute, and Chongqing Cancer Hospital, Chongqing, China
| | - Jianwei Chen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Liting Wen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Cancer Institute, and Chongqing Cancer Hospital, Chongqing, China.
| | - Dechun Zheng
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
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Wyatt CR. Editorial for "Evaluate the Microvascular Invasion of Hepatocellular Carcinoma (≤5 cm) and Recurrence Free Survival with Gadoxetate Disodium-Enhanced MRI-Based Habitat Imaging". J Magn Reson Imaging 2024; 60:1676-1677. [PMID: 38299235 DOI: 10.1002/jmri.29271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/02/2024] Open
Affiliation(s)
- Cory R Wyatt
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon, USA
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Shang Y, Zeng Y, Luo S, Wang Y, Yao J, Li M, Li X, Kui X, Wu H, Fan K, Li ZC, Zheng H, Li G, Liu J, Zhao W. Habitat Imaging With Tumoral and Peritumoral Radiomics for Prediction of Lung Adenocarcinoma Invasiveness on Preoperative Chest CT: A Multicenter Study. AJR Am J Roentgenol 2024; 223:e2431675. [PMID: 39140631 DOI: 10.2214/ajr.24.31675] [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: 08/15/2024]
Abstract
BACKGROUND. Tumor growth processes result in spatial heterogeneity, with the development of tumor subregions (i.e., habitats) having unique biologic characteristics. OBJECTIVE. The purpose of our study was to develop and validate a habitat model combining tumor and peritumoral radiomic features on chest CT for predicting invasiveness of lung adenocarcinoma. METHODS. This retrospective study included 1156 patients (mean age, 57.5 years; 464 men, 692 women), from three centers and a public dataset, who underwent chest CT before lung adenocarcinoma resection (variable date ranges across datasets). Patients from one center formed training (n = 500) and validation (n = 215) sets; patients from the other sources formed three external test sets (n = 249, 113, 79). For each patient, a single nodule was manually segmented on chest CT. The nodule segmentation was combined with an automatically generated 4-mm peritumoral region into a whole-volume volume of interest (VOI). A gaussian mixture model (GMM) identified voxel clusters with similar first-order energy across patients. GMM results were used to divide each patient's whole-volume VOI into multiple habitats, which were defined consistently across patients. Radiomic features were extracted from each habitat. After feature selection, a habitat model was developed for predicting invasiveness, with the use of pathologic assessment as a reference. An integrated model was constructed, combining features extracted from habitats and whole-volume VOIs. Model performance was evaluated, including in subgroups based on nodule density (pure ground-glass, part-solid, and solid). The code for habitat imaging and model construction is publicly available (https://github.com/Shangyoulan/Habitat/). RESULTS. Invasive cancer was diagnosed in 626 of 1156 patients. GMM identified four as the optimal number of voxel clusters and thus of per-patient tumor habitats. The habitat model had an AUC of 0.932 in the validation set and 0.881, 0.880, and 0.764 in the three external test sets. The integrated model had an AUC of 0.947 in the validation set and 0.936, 0.908, and 0.800 in the three external test sets. In the three external test sets combined, across nodule densities, AUCs for the habitat model were 0.836-0.869 and for the integrated model were 0.846-0.917. CONCLUSION. Habitat imaging combining tumoral and peritumoral radiomic features could help predict lung adenocarcinoma invasiveness. Prediction is improved when combining information on tumor subregions and the tumor overall. CLINICAL IMPACT. The findings may aid personalized preoperative assessments to guide clinical decision-making in lung adenocarcinoma.
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Affiliation(s)
- Youlan Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, N o. 139 Middle Remin Rd, Changsha 410011, China
| | - Ying Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan City, China
| | - Shiwei Luo
- Department of Radiology, The Second Xiangya Hospital, Central South University, N o. 139 Middle Remin Rd, Changsha 410011, China
| | - Yisong Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, N o. 139 Middle Remin Rd, Changsha 410011, China
| | - Jiaqi Yao
- Imaging Center, The Second Affiliated Hospital of Xinjiang Medical University, Urumuqi, China
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Xiaoying Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, N o. 139 Middle Remin Rd, Changsha 410011, China
| | - Xiaoyan Kui
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hao Wu
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Kangxu Fan
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi-Cheng Li
- The Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ge Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, N o. 139 Middle Remin Rd, Changsha 410011, China
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, N o. 139 Middle Remin Rd, Changsha 410011, China
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Wang C, Wu F, Wang F, Chong HH, Sun H, Huang P, Xiao Y, Yang C, Zeng M. 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 2024. [PMID: 38997242 DOI: 10.1002/jmri.29523] [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: 04/17/2024] [Revised: 06/14/2024] [Accepted: 06/17/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment. PURPOSE To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis. STUDY TYPE Retrospective. SUBJECTS Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94). FIELD STRENGTH/SEQUENCE 1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence. ASSESSMENT Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews. STATISTICAL TESTS Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance. RESULTS Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival. DATA CONCLUSION The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Cheng Wang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fei Wu
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Huan-Huan Chong
- Department of Radiology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China
| | - Haitao Sun
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Peng Huang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuyao Xiao
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
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