Journal Article

18F-FDG PET/CT-based intratumoral and peritumoral radiomics combining ensemble learning for prognosis prediction in hepatocellular carcinoma: a multi-center study

Tan, Rui, Sui, Chunxiao, Shen, Jie, Xu, Wengui, Li, Xiaofeng, Li, Yue, Chen, Kun, Ding, Enci

2025

Overview

Background Radiomic models combining intratumoral with peritumoral features are potentially beneficial to enhance the predictive performance. This study aimed to identify the optimal 18 F-FDG PET/CT-derived radiomic models for prediction of prognosis in hepatocellular carcinoma (HCC). Methods A total of 135 HCC patients from two institutions were retrospectively included. Four peritumoral regions were defined by dilating tumor region with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm, respectively. Based on segmentation of intratumoral, peritumoral and integrated volume of interest (VOI), corresponding radiomic features were extracted respectively. After feature selection, a total of 15 intratumoral radiomic models were constructed based on five ensemble learning algorithms and radiomic features from three image modalities. Then, the optimal combination of ensemble learning algorithms and image modality in the intratumoral models was selected to develop subsequent peritumoral radiomic models and integrated radiomic models. Finally, a nomogram was developed incorporating the optimal radiomic model with clinical independent predictors to achieve an intuitive representation of the prediction model. Results Among the intratumoral radiomic models, the one which combined PET/CT-based radiomic features with SVM classifier outperformed other models. With the addition of peritumoral information, the integrated model based on an integration of intratumoral and 2 mm-peritumoral VOI, was finally approved as the optimal radiomic model with a mean AUC of 0.831 in the internal validation, and a highest AUC of 0.839 (95%CI:0.718–0.960) in the external test. Furthermore, a nomogram incorporating the optimal radiomic model with HBV infection and TNM status, was able to predict the prognosis for HCC with an AUC of 0.889 (95%CI: 0.799–0.979). Conclusions The integrated intratumoral and peritumoral radiomic model, especially for a 2 mm peritumoral region, was verified as the optimal radiomic model to predict the overall survival of HCC. Furthermore, combination of integrated radiomic model with significant clinical parameter contributed to further enhance the prediction efficacy. Trial registration This study was a retrospective study, so it was free from registration.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: 18F-FDG PET/CT / Algorithms / Analysis / Biomedical and Life Sciences / Biomedicine / Cancer Research / Care and treatment / Clinical outcomes / Computed tomography / Data mining / Development and progression / Feature selection / HCC / Health aspects / Health Promotion and Disease Prevention / Hepatocellular carcinoma / Hepatoma / Learning algorithms / Liver cancer / Machine learning / Medical prognosis / Medicine/Public Health / Metabolism / Methods / Oncology / Peritumoral / PET imaging / Positron emission tomography / Prediction models / Prognosis / Radiomics / Surgical Oncology / Survival analysis / Tomography