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August 1, 2024Journal of Hepatocellular CarcinomaOpen Access

A Machine Learning Model Based on Counterfactual Theory for Treatment Decision of Hepatocellular Carcinoma Patients

Xiaoqin Wei Fang Wang Ying Liu Zeyong Li Zhong Xue Mingyue Tang Xiaowen Chen

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Abstract

To predict the efficacy of patients treated with hepatectomy and transarterial chemoembolization (TACE) based on machine learning models using clinical and radiomics features.

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Cite This Study

Wei et al. (2024) studied this question.

synapsesocial.com/papers/68e5dfdcb6db6435875746cf https://doi.org/10.2147/jhc.s470550

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Enhancing prognostic prediction in hepatocellular carcinoma post-TACE: a machine learning approach integrating radiomics and clinical features2024 · 11 citations
2Using Machine Learning to Predict Advanced-Stage Progression of Intermediate-Stage Hepatocellular Carcinoma after Transarterial Chemoembolization2025 · 3 citations
3Survival Prediction and Treatment Decisions in Hepatocellular Carcinoma: A Deep Learning-Based Radiomics ApproachPrognostic Study · 2026
4Construction of a Preoperative Prediction Model for TACE Resistance in Primary Hepatocellular Carcinoma Based on Machine Learning AlgorithmsRetrospective Study · 2026
5A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation2024 · 22 citations

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Preoperative Prediction of TACE Refractoriness in Hepatocellular Carcinoma Using CT-Based Radiomics Model2026
Development of a predictive model for distant metastasis in HCC patients post-TACE using clinical data, radiomics, and deep learning2025
Advancing predictive oncology: Integrating clinical and radiomic models to optimize transarterial chemoembolization outcomes in hepatocellular carcinoma2025

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