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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Survival Tree

Survival Tree

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Updated: Jan 31, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Survival Prediction and Treatment Decisions in Hepatocellular Carcinoma: A Deep Learning-Based Radiomics Approach.

Xiaoqin Wei¹, Jun Xiao¹, Ying Liu²

¹School of Medical Imaging, North Sichuan Medical College, 637000 Nanchong, Sichuan, China.

British Journal of Hospital Medicine (London, England : 2005)

|January 29, 2026

Summary

Deep learning radiomics combined with clinical data can predict outcomes for hepatocellular carcinoma (HCC) patients undergoing hepatectomy or transarterial chemoembolization (TACE). These models effectively assess survival risk, aiding treatment selection.

Keywords:

TACE deep learning hepatectomy hepatocellular carcinoma prognosis

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A "Patient-Like" Orthotopic Syngeneic Mouse Model of Hepatocellular Carcinoma Metastasis

A "Patient-Like" Orthotopic Syngeneic Mouse Model of Hepatocellular Carcinoma Metastasis

Published on: October 24, 2015

Area of Science:

Radiology
Oncology
Machine Learning

Background:

Deep learning radiomics (DLRadiomics) extracts detailed tumor characteristics.
These features offer insights into tumor biology, disease status, and patient prognosis.
Hepatocellular carcinoma (HCC) treatment effectiveness requires robust predictive models.

Purpose of the Study:

To integrate DLRadiomics features with clinical data for a machine learning survival model.
To assess the comparative effectiveness of hepatectomy versus transarterial chemoembolization (TACE) in HCC patients.
To develop prognostic models for predicting survival risk and aiding treatment decisions.

Main Methods:

Utilized deep learning algorithms (ResNet50, ResNet18, DenseNet121) on contrast-enhanced CT images.
Extracted DLRadiomics features and combined them with clinical data.
Developed and validated machine learning survival models using ROC curves and C-indices.
Constructed nomograms for predicting prognosis and evaluating survival risk via Kaplan-Meier analysis.

Main Results:

Included 409 HCC patients (278 hepatectomy, 131 TACE).
Combined models demonstrated superior discriminative performance, with high C-indices for both hepatectomy (0.836 training, 0.861 testing) and TACE (0.840 training, 0.834 testing).
Developed nomograms to assist clinicians in treatment selection based on predicted outcomes.

Conclusions:

Machine learning models integrating DLRadiomics can predict differential outcomes between hepatectomy and TACE.
Prognostic models effectively predict survival risk in HCC patients.
These models support personalized treatment strategies for HCC.