ArticleJapanese journal of radiology2025
Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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10 authors.
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Abstract
objectivesThis study evaluates the effectiveness of machine learning (ML) models that incorporate clinical and 2-deoxy-2-[ MATERIALS AND
methodsThe study analyzed 52 gallbladder cancer patients who underwent pre-treatment [
resultsTwo clinical variables (UICC stage, N stage) and three radiomic features (total lesion glycolysis, grey-level size-zone matrix_grey level non-uniformity and grey-level run-length matrix_run-length non-uniformity) were identified by the statistical feature selection method as significant for PFS prediction. The RSF model incorporating these features demonstrated strong predictive performance, with C-indices above 0.80 in both training and testing sets (training 0.81, testing 0.89). This model almost closely matched the actual and predicted progression timelines with a low mean absolute error of 1.435, a median absolute error of 0.082, and a root mean square error of 2.359.
conclusionThis study highlights the potential of using ML approaches with clinical and pre-treatment [
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