Interpretable CT-Based Radiomics for Prediction of High-Affinity Neoantigens Associated Immunogenic States in Advanced Hepatocellular Carcinoma Treated with TACE Plus Targeted Therapy and Immunotherapy.
- 1Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
- 2Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
- 3Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou 510095, China.
Abstract
Objectives: To develop and validate a CT radiomics-based machine learning model for the noninvasive prediction of HANS status in advanced HCC and to investigate the prognostic and genomic characteristics associated with HANS status. Materials and Methods: This retrospective study included patients with advanced HCC treated with TACE-based triplet therapy. Patients were followed until death or last follow-up. Tumor tissue underwent whole-exome sequencing for HANS calculation, and arterial-phase CT images were used for radiomic feature extraction. A radiomics model (HANS-Rad), a clinical model (HANS-Clin), and a combined model (HANS-RC) were developed using least absolute shrinkage and selection operator regression. Model performance was evaluated using receiver operating characteristic curve analysis. Calibration and decision curve analyses were performed to assess model reliability and clinical utility. Shapley Additive Explanations (SHAP) were used for model interpretation. Genomic analyses were conducted to explore differences between predicted HANS subgroups. Results: A total of 129 patients were included (median age, 54 years; IQR, 44-62 years; 90% men). The HANS-High group demonstrated longer overall survival compared with the HANS-Low group (median, 23.0 vs. 10.0 months; hazard ratio, 0.60; p = 0.026). The combined HANS-RC model achieved the best performance, with an area under the curve (AUC) of 0.964 in the training set and 0.869 (95% CI, 0.716-1.000) in the test set, outperforming the radiomics-only and clinical models. The model also showed favorable calibration and net benefit. Genomic analyses revealed that HANS-High tumors exhibited higher tumor mutational burden and enrichment of immune-related and oncogenic pathways, whereas HANS-Low tumors were associated with distinct mutational patterns and pathway alterations. Conclusions: A radiomics-based machine learning model using CT images enables the noninvasive prediction of HANS status in advanced HCC, with strong prognostic relevance and biologic interpretability supported by genomic analyses.
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BibTeX
@article{bian2026interpretable,
title = {{Interpretable CT-Based Radiomics for Prediction of High-Affinity Neoantigens Associated Immunogenic States in Advanced Hepatocellular Carcinoma Treated with TACE Plus Targeted Therapy and Immunotherapy.}},
author = {Chaofan Bian and Yanjin Qin and Hongliang Zou and Wenzhe Fan and Jiaping Li},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18193218},
url = {https://europepmc.org/article/MED/42882912},
note = {CC BY},
}
RIS
TY - JOUR
TI - Interpretable CT-Based Radiomics for Prediction of High-Affinity Neoantigens Associated Immunogenic States in Advanced Hepatocellular Carcinoma Treated with TACE Plus Targeted Therapy and Immunotherapy.
AU - Chaofan Bian
AU - Yanjin Qin
AU - Hongliang Zou
AU - Wenzhe Fan
AU - Jiaping Li
PY - 2026
DA - 2026/10/06
JO - Cancers
DO - 10.3390/cancers18193218
UR - https://europepmc.org/article/MED/42882912
AB - Objectives: To develop and validate a CT radiomics-based machine learning model for the noninvasive prediction of HANS status in advanced HCC and to investigate the prognostic and genomic characteristics associated with HANS status. Materials and Methods: This retrospective study included patients with advanced HCC treated with TACE-based triplet therapy. Patients were followed until death or last follow-up. Tumor tissue underwent whole-exome sequencing for HANS calculation, and arterial-phase CT images were used for radiomic feature extraction. A radiomics model (HANS-Rad), a clinical model (HANS-Clin), and a combined model (HANS-RC) were developed using least absolute shrinkage and selection operator regression. Model performance was evaluated using receiver operating characteristic curve analysis. Calibration and decision curve analyses were performed to assess model reliability and clinical utility. Shapley Additive Explanations (SHAP) were used for model interpretation. Genomic analyses were conducted to explore differences between predicted HANS subgroups. Results: A total of 129 patients were included (median age, 54 years; IQR, 44-62 years; 90% men). The HANS-High group demonstrated longer overall survival compared with the HANS-Low group (median, 23.0 vs. 10.0 months; hazard ratio, 0.60; p = 0.026). The combined HANS-RC model achieved the best performance, with an area under the curve (AUC) of 0.964 in the training set and 0.869 (95% CI, 0.716-1.000) in the test set, outperforming the radiomics-only and clinical models. The model also showed favorable calibration and net benefit. Genomic analyses revealed that HANS-High tumors exhibited higher tumor mutational burden and enrichment of immune-related and oncogenic pathways, whereas HANS-Low tumors were associated with distinct mutational patterns and pathway alterations. Conclusions: A radiomics-based machine learning model using CT images enables the noninvasive prediction of HANS status in advanced HCC, with strong prognostic relevance and biologic interpretability supported by genomic analyses.
SN - 2072-6694
ER -