An interpretable machine learning model based on MRI radiomics and GAME score for predicting early recurrence after thermal ablation in colorectal liver metastases

The combined model incorporating preoperative MRI-derived radiomics features and clinical parameters serves as a valuable tool for predicting ER risk in patients with CRLM undergoing TA therapy.

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Publication Details

Journal
International Journal of Colorectal Disease
Published
2026-01-09
DOI
https://doi.org/10.1007/s00384-025-05079-2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

An interpretable machine learning model based on MRI radiomics and GAME score for predicting early recurrence after thermal ablation in colorectal liver metastases

Tang Fl, X L Yue, Lu Wan, Yaqing Kong et al.
International Journal of Colorectal Disease
Radiomics and Machine Learning in Medical Imaging
article

An interpretable machine learning model based on MRI radiomics and GAME score for predicting early recurrence after thermal ablation in colorectal liver metastases

Tang Fl, X L Yue, Lu Wan, Yaqing Kong, Xiang Zhou
article en

Abstract

The combined model incorporating preoperative MRI-derived radiomics features and clinical parameters serves as a valuable tool for predicting ER risk in patients with CRLM undergoing TA therapy.

International Journal of Colorectal DiseaseVol. 41(1)
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), National Cancer Center (US)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 93%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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