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Machine learning predicts MSH2 expression and provides prognostic information in gastric cancer, suggesting clinical utility.
The machine learning-derived pathomics signature shows potential in predicting MSH2 expression. It can serve as a complementary research tool and provide clinically meaningful prognostic information for GC.
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Zhang et al. (2026) studied this question.
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