ArticleTranslational oncology2025
Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study.
Article in Translational oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Transformer-Based Multitask Framework Integrating Habitat and Deep Learning for Predicting Early Disease Control and Survival in Immunotherapy-Treated Hepatocellular Carcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Development and validation of a radiomics-habitat model for preoperatively predicting poorly differentiated stage IA lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Heterogeneity Analyzed by CT-Based Habitat Analysis for Clinical Management of Cancers: A Narrative Review.The Kaohsiung journal of medical sciences · 2026Review
- Construction of Rheumatoid Arthritis-Associated Interstitial Lung Disease diagnostic model and identification of biomarkers based on a multi-omics integration strategy of machine learning.Clinics (Sao Paulo, Brazil) · 2026Article
- [Contrast-enhanced CT-based habitat radiomics for analyzing the predictive capability for oral squamous cell carcinoma].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2026Article
- Development and validation of a habitat-based computed tomography radiomics model for differentiating isolated lung cancer, isolated tuberculoma, and coexistence of tuberculosis with lung cancer: a dual-center retrospective study.Translational lung cancer research · 2026Article
- Temporal-spatial evolution of tumor habitat analysis: a bibliometric study on research hotspots and trends in medical imaging (2014-2025).Translational cancer research · 2026Article
- Integrating deep learning with multimodal MRI habitat radiomics: toward personalized prediction of risk stratification and androgen deprivation therapy outcomes in prostate cancer.Insights into imaging · 2026Article
- Dynamic Vascular Spatiotemporal Heterogeneity on Multiphase CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- HepaKineticNet: Deep Learning of CT-Derived Intratumoral Heterogeneity for Overall Survival Prediction in BCLC Stage B-C Hepatocellular Carcinoma Treated with Locoregional Therapy Plus First-Line TKI and Anti-PD-1 Therapy.Journal of hepatocellular carcinoma · 2026Article
- Radiomics based on habitat analysis in predicting parametrial invasion of early stage cervical cancer.Frontiers in oncology · 2026Article
- Habitat Radiomics Analysis Based on Non-contrast CT in Differentiation of Parotid Pleomorphic Adenoma and AdenolymphomaCurrent medical imaging · 2026Article
- Habitat Model Based on Lung CT for Predicting Brain Metastasis in Patients with Non-Small Cell Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- New perspectives in biology-driven treatment of cholangiocarcinoma: from oncological resectability to genetic breakthroughs and AI-powered imaging.Hepatobiliary surgery and nutrition · 2025Article
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6 authors.
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Abstract
BACKGROUND AND
objectiveThough several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC).
methodsA total of 232 HCC patients were retrospectively included, including a training/validation cohort and two external testing cohorts from 4 centers. For habitat radiomics, intratumoral habitat partitioning based on CT images was first performed by using Otsu thresholding method. Second, a total of 350 habitat radiomic models were constructed to select the optimal model. Then, both ROC curve analyses and Kaplan-Meier survival curve analyses were applied to assess the predictive performances. Ultimately, an immune status profiling was conducted based on bioinformatic analyses and multiplex immunohistochemistry (mIHC) assays to reveal the potential mechanisms.
resultsA total of 4 habitats were segmented, and the corresponding habitat radiomic models were constructed based on each habitat and an integration of all the four habitats. Generally, habitat radiomic models outperformed traditional radiomic models in stratifying prognosis for HCC. The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUC
conclusionsThe habitat radiomic model based on CT images was potentially predictive of overall survival for HCC, with a superiority over the traditional radiomic model. The prognostic power of the habitat radiomic model was partly attributed to the distinct immune status captured in the CT images.
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