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TRUVACE RECORD VERSION
record: TRV-2026-0858
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-23T06:03:20.132219Z
status: published
lens: p_space
sector: health
headline: Comparative evaluation of feature-selection strategies for machine learning-based histological grading of breast cancer using tumor and fibroglandular-tissue DCE-MRI features
dek: 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…
gain_title: (none)
problem_title: Chi-squared selection with a neural network achieved the highest mean accuracy (74.78%; AUC 0.77).
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Chi-squared selection with a neural network achieved the highest mean accuracy (74.78%; AUC 0.77).
problem_evidence: (none)
quick_read: 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.

This retrospective secondary analysis included 576 patients from the Duke-Breast-Cancer-MRI collection in The Cancer Imaging Archive, comprising 288 non-high-grade and 288 high-grade tumors. Chi-squared selection with a neural network achieved the highest mean accuracy (74.78%; AUC 0.77).
limitation: 
tag: Evidence-backed problem
key_points: 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.
rundown: 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 included 576 patients from the Duke-Breast-Cancer-MRI collection in The Cancer Imaging Archive, comprising 288 non-high-grade and 288 high-grade tumors.
sources:
- peer_reviewed | Cancer Treatment and Research Communications | https://doi.org/10.1016/j.ctarc.2026.101382 | 2026-08-21
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