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Whole-lesion histogram analysis of multi-model diffusion-weighted imaging for characterization and molecular classification of breast lesions.

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La Radiologia medica 📖 저널 OA 29.6% 2022: 0/1 OA 2023: 0/1 OA 2024: 0/1 OA 2025: 5/13 OA 2026: 11/35 OA 2022~2026 2026 Vol.131(3) p. 395-405
Retraction 확인
출처

PICO 자동 추출 (휴리스틱, conf 2/4)

유사 논문
P · Population 대상 환자/모집단
112 patients with 90 malignant lesions (17 Luminal A, 39 Luminal B, 18 HER2-positive, 10 triple-negative, and 6 undetermined) and 22 benign lesions, all examined with 1.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[CONCLUSION] Whole-lesion histogram analysis based on multi-model DWI shows potential for characterizing breast lesions. These exploratory findings, derived from an imbalanced single-center cohort, require further validation in larger prospective studies before clinical application.

Yuan Y, Huang M, Peng J, Zhang X, Lin X, Li X, Zeng D

📝 환자 설명용 한 줄

[PURPOSE] To evaluate the value of whole-lesion histogram analysis derived from mono-exponential, bi-exponential, and stretched-exponential DWI models in differentiating benign from malignant breast l

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • p-value p = 0.035
  • p-value p = 0.016

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↓ .bib ↓ .ris
APA Yuan Y, Huang M, et al. (2026). Whole-lesion histogram analysis of multi-model diffusion-weighted imaging for characterization and molecular classification of breast lesions.. La Radiologia medica, 131(3), 395-405. https://doi.org/10.1007/s11547-025-02156-y
MLA Yuan Y, et al.. "Whole-lesion histogram analysis of multi-model diffusion-weighted imaging for characterization and molecular classification of breast lesions.." La Radiologia medica, vol. 131, no. 3, 2026, pp. 395-405.
PMID 41231330 ↗

Abstract

[PURPOSE] To evaluate the value of whole-lesion histogram analysis derived from mono-exponential, bi-exponential, and stretched-exponential DWI models in differentiating benign from malignant breast lesions and exploring molecular subtypes.

[MATERIAL AND METHODS] This retrospective study included 112 patients with 90 malignant lesions (17 Luminal A, 39 Luminal B, 18 HER2-positive, 10 triple-negative, and 6 undetermined) and 22 benign lesions, all examined with 1.5 T MRI. Histogram parameters-apparent diffusion coefficient (ADC), true diffusion (Dt), pseudo-diffusion (Dp), perfusion fraction (f), distributed diffusion coefficient (DDC), and heterogeneity index (alpha)-were analyzed using the Mann-Whitney U test, Kruskal-Wallis test, logistic regression, ROC analysis, the DeLong test, and the chi-square test.

[RESULTS] Histogram parameters from all models showed significant differences between benign and malignant lesions, with high diagnostic performance (AUC range: 0.898-0.938). However, combining the models did not significantly improve the AUC (p > 0.05). In molecular subtype analyses, DDC_75% differed significantly between Luminal A and triple-negative subtypes (p = 0.035); Dt_50%, Dt_75%, and DDC_75% distinguished Luminal B from triple-negative subtypes (p = 0.016, 0.021, and 0.041, respectively); and ADC_kurtosis and DDC_kurtosis showed significant differences between HER2-positive and triple-negative subtypes (p = 0.021 and 0.029, respectively). ROC analysis indicated variable diagnostic efficacy among parameters across molecular subtypes, and model combinations did not enhance AUC values.

[CONCLUSION] Whole-lesion histogram analysis based on multi-model DWI shows potential for characterizing breast lesions. These exploratory findings, derived from an imbalanced single-center cohort, require further validation in larger prospective studies before clinical application.

🏷️ 키워드 / MeSH 📖 같은 키워드 OA만

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