June 26, 2024Scientific ReportsOpen Access

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation

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KBKrzysztof BartnikWarsaw University of Technology MKMateusz KrzyzińskiWarsaw University of Technology TBTomasz BartczakSilesian University of Technology

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Machine learning analysis demonstrates superior progression-free survival prediction in hepatocellular carcinoma patients, highlighting the value of automated multi-organ radiomics.

Key Points

Radiomics random survival forest models outperformed clinical models in predicting progression-free survival across 252 hepatocellular carcinoma patients treated with chemoembolization.
Deep learning multi-organ segmentation automatically extracted volumetric radiomics features from pre-treatment CT scans, eliminating the need for manual radiologist contouring.
Cumulative feature importance from non-tumoral organs exceeded tumor regions, highlighting multi-organ radiomics potential to predict transarterial chemoembolization outcomes.

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Bartnik et al. (2024) studied this question.

synapsesocial.com/papers/68e6327cb6db6435875c427d https://doi.org/10.1038/s41598-024-65630-z

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Machine learning analysis demonstrates superior progression-free survival prediction in hepatocellular carcinoma patients, highlighting the value of automated multi-organ radiomics.

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation

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Implication

Key Points

Cite This Study

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation

Discussion

Member takes

Implication

Key Points

Cite This Study

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