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Purpose <p>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.</p> Material and methods <p>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&#xa0;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,&#xa0;logistic regression, ROC analysis, the DeLong test, and the chi-square test.</p> Results <p>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 (<i>p</i> &gt; 0.05). In molecular subtype analyses, DDC_75% differed significantly between Luminal A and triple-negative subtypes (<i>p</i> = 0.035); Dt_50%, Dt_75%, and DDC_75% distinguished Luminal B from triple-negative subtypes (<i>p</i> = 0.016, 0.021, and 0.041, respectively); and ADC_kurtosis and DDC_kurtosis showed significant differences between HER2-positive and triple-negative subtypes (<i>p</i> = 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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年11月13日

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  • Yuan Yuan,
  • Manhua Huang,
  • Jie Peng,
  • Xiulan Zhang,
  • Xiaofang Lin,
  • Xiang Li,
  • Dewei Zeng

摘要

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.