Development and internal validation of a pretreatment magnetic resonance imaging-based radiomics prediction model for 1-year overall survival in treatment-naïve patients with hepatocellular carcinoma receiving transarterial chemoembolization combined with targeted therapy and immune checkpoint inhibitors.
- 1Department of Interventional Radiology, Gaoxin Branch, Jilin Cancer Hospital, Changchun, China.
- 2Department of Radiology, Jilin Cancer Hospital, Changchun, China.
Abstract
Reliable pretreatment tools to identify treatment-naïve patients with intermediate-to-advanced hepatocellular carcinoma (HCC) who may benefit from this combination are lacking. We developed and internally validated a clinical-radiomics model based on pretreatment T2-weighted fat-suppressed magnetic resonance imaging (MRI) for predicting 1-year overall survival (OS). This retrospective, single-center cohort study included 85 treatment-naïve patients with Barcelona Clinic Liver Cancer (BCLC) stage B/C HCC treated with transarterial chemoembolization (TACE) plus targeted therapy and immune checkpoint inhibitors between December 2021 and April 2024. The last follow-up was April 1, 2026. The primary endpoint was 1-year OS status (death within 12 months vs. survival ≥12 months). Patients were randomly divided into a training set (70%) and a held-out test set (30%) using stratified sampling. Three-dimensional tumor volumes were manually segmented on pretreatment axial T2 spectral attenuated inversion recovery images. Features were first filtered for intra- and interobserver reproducibility, then selected using univariable screening and least absolute shrinkage and selection operator (LASSO) regression before being entered into the machine-learning models. The optimal model was selected via fivefold cross-validation in the training set and evaluated in a held-out test set through use of discrimination, calibration, decision curve analysis, and Shapley additive explanations. A total of 2,525 radiomics features were extracted, and four final predictors were retained: maximum tumor diameter and three filtered radiomics features. Gradient boosting machine (GBM) achieved the highest mean cross-validation area under the receiver operating characteristic curve (AUC) (0.818±0.079). In the held-out test set, the combined GBM model yielded an AUC of 0.91 [95% confidence interval (CI): 0.87-0.93], accuracy of 84.6% (95% CI: 79.3-88.9%), sensitivity of 81.8% (95% CI: 72.2-89.0%), and specificity of 86.7% (95% CI: 79.1-92.1%); the Brier score was 0.04. Decision curve analysis suggested a net clinical benefit across a broad range of threshold probabilities. A pretreatment T2-weighted fat-suppressed MRI-based clinical-radiomics model demonstrated promising performance for predicting 1-year OS in treatment-naïve patients with BCLC stage B/C HCC receiving TACE plus targeted therapy and immune checkpoint inhibitors. However, this single-center finding requires external validation before clinical implementation.
Related
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- Enhanced Efficacy of Thalidomide‐Based Cocktail With Transarterial Chemoembolization in the Management of Unresectable Hepatocellular Carcinoma: A Multi‐Center Cohort Study
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BibTeX
@article{jiang2026development,
title = {{Development and internal validation of a pretreatment magnetic resonance imaging-based radiomics prediction model for 1-year overall survival in treatment-naïve patients with hepatocellular carcinoma receiving transarterial chemoembolization combined with targeted therapy and immune checkpoint inhibitors.}},
author = {Chengyi Jiang and Shuai Zhang and Beiguang Zhang and Mingguang Xin and Xiaoyu Chen and Jiale Cheng and Dingli Ye and Zhiqiang Sun and Shuangyan Sun},
journal = {Journal of gastrointestinal oncology},
year = {2026},
month = oct,
doi = {10.21037/jgo-2026-1080},
url = {https://europepmc.org/article/MED/42996585},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Development and internal validation of a pretreatment magnetic resonance imaging-based radiomics prediction model for 1-year overall survival in treatment-naïve patients with hepatocellular carcinoma receiving transarterial chemoembolization combined with targeted therapy and immune checkpoint inhibitors.
AU - Chengyi Jiang
AU - Shuai Zhang
AU - Beiguang Zhang
AU - Mingguang Xin
AU - Xiaoyu Chen
AU - Jiale Cheng
AU - Dingli Ye
AU - Zhiqiang Sun
AU - Shuangyan Sun
PY - 2026
DA - 2026/10/26
JO - Journal of gastrointestinal oncology
DO - 10.21037/jgo-2026-1080
UR - https://europepmc.org/article/MED/42996585
AB - Reliable pretreatment tools to identify treatment-naïve patients with intermediate-to-advanced hepatocellular carcinoma (HCC) who may benefit from this combination are lacking. We developed and internally validated a clinical-radiomics model based on pretreatment T2-weighted fat-suppressed magnetic resonance imaging (MRI) for predicting 1-year overall survival (OS). This retrospective, single-center cohort study included 85 treatment-naïve patients with Barcelona Clinic Liver Cancer (BCLC) stage B/C HCC treated with transarterial chemoembolization (TACE) plus targeted therapy and immune checkpoint inhibitors between December 2021 and April 2024. The last follow-up was April 1, 2026. The primary endpoint was 1-year OS status (death within 12 months vs. survival ≥12 months). Patients were randomly divided into a training set (70%) and a held-out test set (30%) using stratified sampling. Three-dimensional tumor volumes were manually segmented on pretreatment axial T2 spectral attenuated inversion recovery images. Features were first filtered for intra- and interobserver reproducibility, then selected using univariable screening and least absolute shrinkage and selection operator (LASSO) regression before being entered into the machine-learning models. The optimal model was selected via fivefold cross-validation in the training set and evaluated in a held-out test set through use of discrimination, calibration, decision curve analysis, and Shapley additive explanations. A total of 2,525 radiomics features were extracted, and four final predictors were retained: maximum tumor diameter and three filtered radiomics features. Gradient boosting machine (GBM) achieved the highest mean cross-validation area under the receiver operating characteristic curve (AUC) (0.818±0.079). In the held-out test set, the combined GBM model yielded an AUC of 0.91 [95% confidence interval (CI): 0.87-0.93], accuracy of 84.6% (95% CI: 79.3-88.9%), sensitivity of 81.8% (95% CI: 72.2-89.0%), and specificity of 86.7% (95% CI: 79.1-92.1%); the Brier score was 0.04. Decision curve analysis suggested a net clinical benefit across a broad range of threshold probabilities. A pretreatment T2-weighted fat-suppressed MRI-based clinical-radiomics model demonstrated promising performance for predicting 1-year OS in treatment-naïve patients with BCLC stage B/C HCC receiving TACE plus targeted therapy and immune checkpoint inhibitors. However, this single-center finding requires external validation before clinical implementation.
SN - 2078-6891
SN - 2219-679X
ER -