Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study

作者：Kun Chen, Chunxiao Sui, Ziyang Wang, Zifan Liu, Lisha Qi, Xiaofeng Li · 发表于：Translational Oncology · 年份：2025 · DOI：10.1016/j.tranon.2024.102260 · 被引用次数：18 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Hepatocellular Carcinoma Treatment and Prognosis、Cholangiocarcinoma and Gallbladder Cancer Studies

• A total of 4 intratumoral habitats were segmented based on CT images using otsu clustering for HCC. • Habitat radiomics outperformed traditional radiomics in stratifying prognosis for HCC. • Distinct immune status in TME contributed to the prognostic power of the habitat radiomic model. Though 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). A 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. A total of 4 habitats were segmented, and the corresponding habitat radiomic models were...