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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. [PMID: 39140631 DOI: 10.2214/ajr.24.31675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Background: Tumors' growth processes result in spatial heterogeneity, with the development of tumor subregions (i.e., habitats) having unique biologic characteristics. Objective: To develop and validate a habitat model combining tumor and peritumoral radiomics features on chest CT for predicting invasiveness of lung adenocarcinoma. Methods: This retrospective study included 1156 patients (mean age, 57.5 years; 464 male, 692 female) 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, defined consistently across patients. Radiomic features were extracted from each habitat. After feature selection, a habitat model was developed for predicting invasiveness, using 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, solid). Results: Invasive cancer was diagnosed in 625/1156 patients. GMM identified four as the optimal number of voxel clusters and thus of per-patient tumor habitats. The habitat model had 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 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 0.836-0.969 and for the integrated model were 0.846-0.917. Conclusions: 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
- Bachelor's degree, Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Ying Zeng
- Master's degree, Radiology Department, Xiangtan Central Hospital, Hunan, China
| | - Shiwei Luo
- Master's degree, Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Yisong Wang
- Bachelor's degree, Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Jiaqi Yao
- Imaging Center, the Second Affiliated Hospital of Xinjiang Medical University, Urumuqi 830000, China
| | - Ming Li
- Doctor's degree, Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Xiaoying Li
- Master's degree, Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Xiaoyan Kui
- Doctor's degree, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Hao Wu
- High School Degree, School of Computer Science and Engineering, Central South University, Hunan, China
| | - Kangxu Fan
- High School Degree, School of Computer Science and Engineering, Central South University, Hunan, China
| | - Zhi-Cheng Li
- Doctor's degree, The Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Hairong Zheng
- Doctor's degree, Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Ge Li
- Master's degree, Department of Radiology, Xiangya Hospital, Central South University, No. 87 Xiangya Rd, Changsha, Hunan Province, 410008
| | - Jun Liu
- Doctor's degree, Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, 410011, China
| | - Wei Zhao
- Doctor's degree, Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, 410011, China
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, 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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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 2024. [PMID: 38712652 DOI: 10.1002/jmri.29428] [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: 02/17/2024] [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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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. [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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