ViT-based quantification of intratumoral heterogeneity for predicting the early recurrence in HCC following multiple ablation

Ke Zhang; Jinyu Ru; Wenbo Wang; Min Xu; Lei Mu; Jinhua Pan; Jionghui Gu; Haoyan Zhang; Jie Tian; Wei Yang; Tianan Jiang; Kun Wang

doi:10.1111/liv.16051

ViT-based quantification of intratumoral heterogeneity for predicting the early recurrence in HCC following multiple ablation

Liver Int. 2024 Nov 11. doi: 10.1111/liv.16051. Online ahead of print.

Authors

Ke Zhang¹, Jinyu Ru^{2

3}, Wenbo Wang⁴, Min Xu¹, Lei Mu¹, Jinhua Pan¹, Jionghui Gu¹, Haoyan Zhang^{2

3}, Jie Tian^{2

5}, Wei Yang⁴, Tianan Jiang¹, Kun Wang^{2

3}

Affiliations

¹ Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
² CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
³ School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
⁴ Department of Ultrasound, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China.
⁵ School of Engineering Medicine, Beihang University, Beijing, China.

PMID: 39526488
DOI: 10.1111/liv.16051

Abstract

Objectives: This study aimed to develop a quantitative intratumoral heterogeneity (ITH) model for assessing the risk of early recurrence (ER) in pre-treatment multimodal imaging for hepatocellular carcinoma (HCC) patients undergoing ablation treatments.

Methods: This multi-centre study enrolled 633 HCC patients who underwent ultrasound-guided local ablation between January 2015 and September 2022. Among them, 422, 85, 57 and 69 patients underwent radiofrequency ablation (RFA), microwave ablation (MWA), laser ablation (LA) and irreversible electroporation (IRE) ablation, respectively. Vision-Transformer-based quantitative ITH (ViT-Q-ITH) features were extracted from the US and MRI sequences. Multivariable logistic regression analysis was used to identify variables associated with ER. A combined model integrated clinic-radiologic and ViT-Q-ITH scores. The prediction performance was evaluated concerning calibration, clinical usefulness and discrimination.

Results: The final training cohort and internal validation cohort included 318 patients and 83 patients, respectively, who underwent RFA and MWA. The three external testing cohorts comprised of 106 patients treated with RFA, 57 patients treated with LA and 69 patients who underwent IRE ablation. The combined model showed excellent predictive performance for ER in the training (AUC: .99, 95% CI: .99-1.00), internal validation (AUC: .86, 95% CI: .78-.94), external testing (AUC: .83, 95% CI: .73-.92), LA (AUC: .84, 95% CI: .73-.95) and IRE (AUC: .82, 95% CI: .72-.93) cohorts, respectively. Decision curve analysis further affirmed the clinical utility of the combined model.

Conclusions: The multimodal-based model, incorporating clinic-radiologic factors and ITH features, demonstrated superior performance in predicting ER among early-stage HCC patients undergoing different ablation modalities.

Keywords: Intratumoral heterogeneity; Vision‐Transformer; ablation treatments; early recurrence; hepatocellular carcinoma.