A habitat radiomics model based on contrast-enhanced MRI for predicting early treatment response to hepatic arterial infusion chemotherapy in patients with unresectable hepatocellular carcinoma

To develop a contrast-enhanced Magnetic Resonance Imaging (CEMRI)-based habitat radiomics model for predicting early treatment response to hepatic artery infusion chemotherapy with fluorouracil, leucovorin, and oxaliplatin (HAIC-FOLFOX) in patients with unresectable hepatocellular carcinoma (HCC) and elucidate the underlying biological mechanisms. Among 120 HCC patients who underwent HAIC treatment, habitat features were extracted by applying clustering algorithms to preoperative CEMRI to delineate intratumoral subregions with distinct enhancement characteristics. Least absolute shrinkage and selection operator (LASSO) and logistic regression were employed for feature selection to construct habitat, conventional radiomics, clinical, and combined models. Internal validation was performed using 1000 bootstrap resamples. In a separate cohort of 107 surgically resected HCC patients, the habitat model was applied for risk stratification, and correlations between habitat features and pathomorphological characteristics, as well as immunohistochemical (IHC) markers, were investigated. MRI images were categorized into three distinct habitats, and a predictive model was built from their proportional distribution. The habitat radiomics model achieved an area under the curve (AUC) of 0.868 (95% confidence interval (CI): 0.748–0.976), outperforming both conventional radiomics (AUC 0.849, 95% CI: 0.719–0.954) and clinical models (AUC 0.653, 95% CI: 0.497–0.802). The combined clinical-habitat model reached the highest AUC of 0.901 (95% CI: 0.795–0.989, P < 0.05). In the surgical cohort, low-risk habitat patients exhibited increased tumor necrosis/stromal components (elevated IntensityMin) and better differentiation (reduced CurvMean) (P < 0.05). Immunohistochemistry revealed higher microvessel density (CD34) and lower cancer stem cell marker expression (CK19, Glypican-3) in the low-risk group (P < 0.05). The CEMRI habitat radiomics model accurately predicts early HAIC treatment response, with risk stratification significantly correlated with pathomorphological and molecular characteristics.

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Publication Details

Journal
Cellular Oncology
Published
2026-03-23
DOI
https://doi.org/10.1007/s13402-026-01196-4
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
Type
article
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article

A habitat radiomics model based on contrast-enhanced MRI for predicting early treatment response to hepatic arterial infusion chemotherapy in patients with unresectable hepatocellular carcinoma

Chang Li, Nan You, Zhexuan Ye, Guanhui Li et al.
Cellular Oncology
Hepatocellular Carcinoma Treatment and Prognosis
article

A habitat radiomics model based on contrast-enhanced MRI for predicting early treatment response to hepatic arterial infusion chemotherapy in patients with unresectable hepatocellular carcinoma

Chang Li, Nan You, Zhexuan Ye, Guanhui Li, Yuqin He, Changxu Cai, Jie Long, Yanfang Li, Hao Zhong, Xinyu Zhao, Yuxin Tang, Liangzhi Wen, Shujie Lai, Lili Rao, Xiang Lan, Hao Wu, Jie Li, Jun Wang
article en

Abstract

To develop a contrast-enhanced Magnetic Resonance Imaging (CEMRI)-based habitat radiomics model for predicting early treatment response to hepatic artery infusion chemotherapy with fluorouracil, leucovorin, and oxaliplatin (HAIC-FOLFOX) in patients with unresectable hepatocellular carcinoma (HCC) and elucidate the underlying biological mechanisms. Among 120 HCC patients who underwent HAIC treatment, habitat features were extracted by applying clustering algorithms to preoperative CEMRI to delineate intratumoral subregions with distinct enhancement characteristics. Least absolute shrinkage and selection operator (LASSO) and logistic regression were employed for feature selection to construct habitat, conventional radiomics, clinical, and combined models. Internal validation was performed using 1000 bootstrap resamples. In a separate cohort of 107 surgically resected HCC patients, the habitat model was applied for risk stratification, and correlations between habitat features and pathomorphological characteristics, as well as immunohistochemical (IHC) markers, were investigated. MRI images were categorized into three distinct habitats, and a predictive model was built from their proportional distribution. The habitat radiomics model achieved an area under the curve (AUC) of 0.868 (95% confidence interval (CI): 0.748–0.976), outperforming both conventional radiomics (AUC 0.849, 95% CI: 0.719–0.954) and clinical models (AUC 0.653, 95% CI: 0.497–0.802). The combined clinical-habitat model reached the highest AUC of 0.901 (95% CI: 0.795–0.989, P < 0.05). In the surgical cohort, low-risk habitat patients exhibited increased tumor necrosis/stromal components (elevated IntensityMin) and better differentiation (reduced CurvMean) (P < 0.05). Immunohistochemistry revealed higher microvessel density (CD34) and lower cancer stem cell marker expression (CK19, Glypican-3) in the low-risk group (P < 0.05). The CEMRI habitat radiomics model accurately predicts early HAIC treatment response, with risk stratification significantly correlated with pathomorphological and molecular characteristics.

Cellular OncologyVol. 49(2)
Army Medical University (CN), Daping Hospital (CN), Xinqiao Hospital (CN), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Chongqing Medical University (CN)
Life in Land
Openalex Percentile: Top 14%
Hepatocellular Carcinoma Treatment and Prognosis
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