Comparative analysis of apparent diffusion coefficient (ADC) metrics for the differential diagnosis of breast mass lesions - research paper

Sep 23, 2026

Breast cancer’s diagnostic difficulty increases with tumor heterogeneity, making precise characterization essential for selecting optimal management and improving prognosis. Diffusion-Weighted Imaging (DWI) enables noninvasive evaluation through Apparent Diffusion Coefficient (ADC) metrics. A retrospective cohort used MRI from 125 pathologically confirmed tumors, measuring lesion and reference sites, then comparing ADC metrics and age via ROC and logistic regression. Advanced ADC features, especially a combined model, improved discrimination between benign and malignant lesions, supporting precision medicine.

LanguageEnglish

CategoryHealthcare

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Pages11

Reading Time28 min

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Abstract

Background

Methods

Results

Conclusions

Yang et al. BMC Medical Imaging (2025) 25:117 [https://doi.org/10.1186/s12880-025-01654-9](https://doi.org/10.1186/s12880-025-01654-9) BMC Medical Imaging RESEARCH Open Access Comparative analysis of apparent diffusion coefficient (ADC) metrics for the differential diagnosis of breast mass lesions Yangping Yang 1,2†, Jiong Liu 1,3† and Jian Shu 1,3* Abstract Background Breast cancer’s diagnostic challenge is amplified by its heterogeneity. Diffusion-Weighted Imaging (DWI) offers promising avenues for precise tumor characterization through Apparent Diffusion Coefficient (ADC) metrics. Purpose To investigate the diagnostic utility of advanced ADC metrics in distinguishing breast lesions using Magnetic Resonance Imaging (MRI) . Methods A retrospective cohort analysis of MRI data from 125 pathologically confirmed breast tumors was conducted. ADC values were independently measured by two physicians at the lesion sites and reference points (contralateral normal breast parenchyma, pectoral is major, and interventricular septum), from which advanced ADC metrics were calculated. Statistical analyses were applied to differentiate ADC metrics between malignant and benign groups. ROC curves assessed the diagnostic efficacy of individual ADC metrics. A binary logistic regression model incorporating ADC metrics and age was developed, with its diagnostic superiority evaluated through multidimensional comparisons. Results Of the 125 lesions, 77 were malignant and 48 benign. Significant differences in ADC metrics were found between malignant and benign tumors. Diagnostic analysis showed minimum ADC value (ADC_min) as the most effective single indicator, while the combined model, including age and average ADC value (ADC_avg), outperformed individual ADC metrics, demonstrating superior diagnostic accuracy (area under the curve (AUC) = 0 . 964) . The combined model nomogram also showed improved clinical utility and a significant increase in diagnostic performance. Conclusions Advanced ADC metrics significantly enhance the diagnostic accuracy for differentiating between benign and malignant breast lesions. The development of a combined model further refines breast cancer diagnostics, supporting the advancement towards precision medicine. Keywords Breast tumor, Magnetic resonance imaging, Apparent diffusion coefficient, Differential diagnosis †Yangping Yang and Jiong Liu contributed equally to this work. *Correspondence: Jian Shu [shujiannc@163.com](shujiannc@163.com) 1Department of Radiology, The Affiliated Hospital of Southwest Medical University, No 25 Taiping St, Jiangyang District, Luzhou, Sichuan, PR China 2Department of Radiology, Chongzhou People’s Hospital, Chengdu, China 3Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, No 25 Taiping St, Jiangyang District, Luzhou, Sichuan, PR China © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/. Yang et al. BMC Medical Imaging (2025)

FAQs

What was the purpose of the study regarding ADC metrics?

To evaluate the diagnostic utility of advanced ADC metrics for distinguishing breast lesions using MRI.

How were ADC values measured and analyzed?

ADC values were measured at lesion sites and multiple reference points by two physicians, then advanced ADC metrics were calculated and compared between malignant and benign groups using ROC analysis and logistic regression with age.

Which ADC-related approach performed best for diagnosis?

The combined model including age and average ADC value (ADC_avg) outperformed individual ADC metrics, and the ADC_min value showed the strongest single-indicator performance.

Sep 23, 2026

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