aceComparative evaluation of feature-selection strategies for machine learning-based histological grading of breast cancer using tumor and fibroglandular-tissue DCE-MRI features
Nottingham histological grading is central to breast cancer prognosis and treatment planning, but conventional pathological assessment is labor-intensive and subject to inter-observer variability. Radiomics and machine learning may support noninvasive preoperative grade prediction. To compare mutual-information, chi-squared, and LASSO feature-selection strategies for binary Nottingham grade classification using tumor- and fibroglandular-tissue (FGT)-derived DCE-MRI features. This retrospective secondary analysis…
Chi-squared selection with a neural network achieved the highest mean accuracy (74.78%; AUC 0.77).
Evidence
- Peer-reviewedCancer Treatment and Research Communications2026-08-21
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Truvace Impact Record TRV-2026-0858, v1: “Comparative evaluation of feature-selection strategies for machine learning-based histological grading of breast cancer using tumor and fibroglandular-tissue DCE-MRI features.” Truvace, 2026-08-23. /record/TRV-2026-0858 (accessed at citation time). sha256 12ce3665992c81e6…
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