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Beyond Pathology: MRI-derived Metrics Unlock Preoperative Prognostic Risk Stratification in Breast Cancer.

1/5 보강
Clinical breast cancer 📖 저널 OA 7.6% 2021: 0/2 OA 2022: 0/1 OA 2023: 0/1 OA 2024: 1/4 OA 2025: 0/5 OA 2026: 9/134 OA 2021~2026 2026
Retraction 확인
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PICO 자동 추출 (휴리스틱, conf 2/4)

유사 논문
P · Population 대상 환자/모집단
791 patients with invasive BC, we leveraged quantitative (tumor size, volume, and enhancement metrics) and categorical (presence or absence of axillary lymphadenopathy, multicentricity, contralateral disease, chest wall and skin involvement) MRI features to predict high-risk NPI grades (>3.
I · Intervention 중재 / 시술
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C · Comparison 대조 / 비교
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O · Outcome 결과 / 결론
[CONCLUSION] MRI serves as a powerful noninvasive proxy for pathological NPI, enabling risk-stratified preoperative counseling and treatment planning. Molecular subtype-tailored MRI interpretation offers a precision medicine pathway toward individualized breast cancer management before surgery.

Zare M, Mohebbi A, Mohammadi A, Tavangar SM

📝 환자 설명용 한 줄

[BACKGROUND] The Nottingham Prognostic Index (NPI) remains the gold-standard for breast cancer (BC) prognostic risk stratification, yet its reliance on postoperative pathology delays critical treatmen

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • Sensitivity 72.0%

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↓ .bib ↓ .ris
APA Zare M, Mohebbi A, et al. (2026). Beyond Pathology: MRI-derived Metrics Unlock Preoperative Prognostic Risk Stratification in Breast Cancer.. Clinical breast cancer. https://doi.org/10.1016/j.clbc.2026.03.017
MLA Zare M, et al.. "Beyond Pathology: MRI-derived Metrics Unlock Preoperative Prognostic Risk Stratification in Breast Cancer.." Clinical breast cancer, 2026.
PMID 42034540 ↗

Abstract

[BACKGROUND] The Nottingham Prognostic Index (NPI) remains the gold-standard for breast cancer (BC) prognostic risk stratification, yet its reliance on postoperative pathology delays critical treatment decisions. We hypothesized that MRI could unlock prognostic insights before surgery, enabling immediate preoperative risk classification without waiting for histology.

[METHODS] In a retrospective cohort of 791 patients with invasive BC, we leveraged quantitative (tumor size, volume, and enhancement metrics) and categorical (presence or absence of axillary lymphadenopathy, multicentricity, contralateral disease, chest wall and skin involvement) MRI features to predict high-risk NPI grades (>3.4). Discriminative performance was determined by receiver operating characteristic analysis and multivariable logistic regression, with stratification by molecular subtype, tumor size, and breast density.

[RESULTS] Quantitative MRI metrics demonstrated strong discriminatory power; tumor size and total enhancing volume achieved area under curve (AUC) of 0.855 and 0.867, while washout-enhancing volume provided vascular insights (AUC 0.828). Notably, performance varied by tumor biology; volumetric metrics dominated in HER2-enriched tumors (AUC 0.965) and washout kinetics excelled in triple-negative disease (AUC 0.932). Among categorical features, axillary lymphadenopathy and multicentricity contributed complementary prognostic value. Integration of 3 selected parameters of tumor size, lymphadenopathy, and multicentricity yielded a robust combined model (AUC 0.883) with 89.8% sensitivity and 72.0% specificity. Critically, MRI metrics remained robust across breast density categories.

[CONCLUSION] MRI serves as a powerful noninvasive proxy for pathological NPI, enabling risk-stratified preoperative counseling and treatment planning. Molecular subtype-tailored MRI interpretation offers a precision medicine pathway toward individualized breast cancer management before surgery.

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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반