Frontiers in oncology · 2026
MRI-based clinical-radiomics-habitat model for predicting prognosis of hepatocellular carcinoma patients treated with HAIC.
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Cao Jiaojiao, Zhu Tianyi, Sun Qichen, Liu Yiting, Yu Haiyang, Guo Xiaoxia, Liu Qin, Ding Xiaoyi, Wang Jian, Wu Zhiyuan, Zhou Yu
Аннотация
BACKGROUND: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignant tumor with generally poor prognosis. Hepatic arterial infusion chemotherapy (HAIC) serves as a crucial treatment modality for intermediate to advanced HCC, but its efficacy is significantly influenced by tumor heterogeneity and individual variability. This study aimed to develop an MRI-based Clinical-Radiomics-Habitat model for non-invasive prediction of early response to HAIC treatment.
METHODS: 105 HCC patients who received HAIC treatment across two institutions were retrospectively analyzed. Tumor subregions were segmented on four preoperative MRI sequences, including T1-weighted imaging (T1WI) and contrast-enhanced T1WI (arterial late phase, portal venous phase, and delayed phase), from which image features were extracted. Clinical data, habitat analysis features, and radiomics features were collected to construct three distinct predictive models. Each model was internally validated using the bootstrap method, evaluated using multiple performance metrics, and employed to explore prognostic information.
RESULTS: Among the 105 patients, treatment responses included complete response (n=9), partial response (n=48), stable disease (n=34), and progressive disease (n=14), yielding responder and non-responder rates of 54.3% and 45.7%, respectively. With an AUC of 0.771 (95% CI 0.682-0.860), the Clinical-Radiomics-Habitat model performed better than both the Clinical model (0.633) and the Clinical-Radiomics model (0.747). Habitat imaging exhibits significant potential in analyzing tumor heterogeneity and predicting treatment early response in HCC patients.
CONCLUSION: We established a multiparametric MRI-based Clinical-Radiomics-Habitat model for preoperative early response prediction in HAIC-treated HCC patients. This model may assist clinicians in optimizing personalized treatment decisions.
METHODS: 105 HCC patients who received HAIC treatment across two institutions were retrospectively analyzed. Tumor subregions were segmented on four preoperative MRI sequences, including T1-weighted imaging (T1WI) and contrast-enhanced T1WI (arterial late phase, portal venous phase, and delayed phase), from which image features were extracted. Clinical data, habitat analysis features, and radiomics features were collected to construct three distinct predictive models. Each model was internally validated using the bootstrap method, evaluated using multiple performance metrics, and employed to explore prognostic information.
RESULTS: Among the 105 patients, treatment responses included complete response (n=9), partial response (n=48), stable disease (n=34), and progressive disease (n=14), yielding responder and non-responder rates of 54.3% and 45.7%, respectively. With an AUC of 0.771 (95% CI 0.682-0.860), the Clinical-Radiomics-Habitat model performed better than both the Clinical model (0.633) and the Clinical-Radiomics model (0.747). Habitat imaging exhibits significant potential in analyzing tumor heterogeneity and predicting treatment early response in HCC patients.
CONCLUSION: We established a multiparametric MRI-based Clinical-Radiomics-Habitat model for preoperative early response prediction in HAIC-treated HCC patients. This model may assist clinicians in optimizing personalized treatment decisions.