Predictive Model of Liver Toxicity to Aid the Personalized Selection of Proton Versus Photon Therapy in Hepatocellular Carcinoma

Authors

Ibrahim Chamseddine
Yejin Kim
Brian De
Issam El Naqa
Dan G Duda
John A Wolfgang
Jennifer Pursley
Jennifer Y Wo
Theodore S Hong
Harald Paganetti
Eugene J Koay
Clemens Grassberger

Publication Date

8-1-2023

Journal

International Journal of Radiation Oncology, Biology, Physics

DOI

10.1016/j.ijrobp.2023.01.055

PMID

36739920

PMCID

PMC12273456

PubMedCentral® Posted Date

7-18-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Purpose: Our objective was to develop an externally validated model for predicting liver toxicity after radiation therapy in patients with hepatocellular carcinoma (HCC) that can integrate both photon and proton dose distributions with patient-specific characteristics.

Methods and materials: Training data consisted of all patients with HCC treated between 2008 and 2019 at our institution (n = 117, 60%/40% photon/proton). We developed a shallow convolutional neural network (CNN) to predict posttreatment liver dysfunction from the differential dose-volume histogram (DVH) and baseline liver metrics. To reduce bias and improve robustness, we used ensemble learning (CNNE). After a preregistered study analysis plan, we evaluated stability using internal bootstrap resampling and generalizability using a data set from a different institution (n = 88). Finally, we implemented a class activation map method to characterize the critical DVH subregions and benchmarked the model against logistic regression and XGBoost. The models were evaluated using the area under the receiver operating characteristic curve and area under the precision-recall curve.

Results: The CNNE model showed similar internal performance and robustness compared with the benchmarks. CNNE exceeded the benchmark models in external validation, with an area under the receiver operating characteristic curve of 0.78 versus 0.55 to 0.70, and an area under the precision-recall curve of 0.6 versus 0.43 to 0.52. The model showed improved predictive power in the photon group, excellent specificity in both modalities, and high sensitivity in the photon high-risk group. Models built solely on DVHs confirm outperformance of the CNNE and indicate that the proposed structure efficiently abstracts features from both proton and photon dose distributions. The activation map method demonstrates the importance of the low-dose bath and its interaction with low liver function at baseline.

Conclusions: We developed and externally validated a patient-specific prediction model for hepatic toxicity based on the entire DVH and clinical factors that can integrate both photon and proton therapy cohorts. This model complements the new American Society for Radiation Oncology clinical practice guidelines and could support value-driven integration of proton therapy into the management of HCC.

Keywords

Humans, Carcinoma, Hepatocellular, Protons, Liver Neoplasms, Radiotherapy Dosage, Proton Therapy

Published Open-Access

yes

Recommended Citation

Chamseddine, Ibrahim; Kim, Yejin; De, Brian; et al., "Predictive Model of Liver Toxicity to Aid the Personalized Selection of Proton Versus Photon Therapy in Hepatocellular Carcinoma" (2023). Faculty, Staff and Student Publications. 4598.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/4598