Histogram analysis of relaxometry from synthetic MRI for differentiation of breast BI-RADS 4 non-mass enhancement lesions

To extract the histogram features of quantitative parameters from synthetic MRI (SyMRI) and evaluate their diagnostic performance in differentiating benign and malignant BI-RADS 4 non-mass enhancement (NME) lesions. This single-center retrospective study enrolled consecutive patients who underwent dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and SyMRI between October 2019 and May 2023. Conventional imaging characteristics and the time-signal intensity curve (TIC) were evaluated on DCE-MRI. Apparent diffusion coefficient (ADC) values and histogram features were independently measured by two radiologists, with inter-observer agreement evaluated. Multivariate logistic regression was employed to construct three diagnostic models: conventional model (based on DCE-MRI features and ADC values), SyMRI model(based on histogram features) and combined model(integrating festures from both). The diagnostic performance of these models were subsequently evaluated and compared using receiver operating characteristic (ROC) curve analysis. A total of 108 patients (mean age, 48 years ± 11 [standard deviation]) were finally included. Univariate analysis indicated significant differences in distribution, internal enhancement pattern, ADC-min, ADC-mean, TIC, and several histogram characteristics between benign and malignant NME groups. Multivariable logistic regression models demonstrated that the SyMRI model exhibited significantly higher sensitivity (98.6% vs. 73.9%), negative predictive value(NPV) (95.4% vs. 53.7%), positive predictive value(PPV) (83.7% vs. 80.6%), and area under the curve (AUC) (0.821, 95% CI: 0.728–0.915 vs. 0.739, 95% CI: 0.636–0.842) compared to the conventional model The combined model achieved the best performance for differentiating BI-RADS 4 NME lesions, with an AUC of 0.851 (95% CI: 0.764–0.937). Incorporating histogram features of quantitative parameters from synthetic MRI into conventional MRI improved the diagnostic performence in differentiating benign and malignant BI-RADS 4 NME lesions.

Authors

Institutions

Publication Details

Journal
BMC Medical Imaging
Published
2025-10-13
DOI
https://doi.org/10.1186/s12880-025-01945-1
Primary Topic
MRI in cancer diagnosis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Histogram analysis of relaxometry from synthetic MRI for differentiation of breast BI-RADS 4 non-mass enhancement lesions

Zhaolong Zheng, Qingliang Niu, Pu‐Yeh Wu, Qin Li et al.
BMC Medical Imaging
MRI in cancer diagnosis
article

Histogram analysis of relaxometry from synthetic MRI for differentiation of breast BI-RADS 4 non-mass enhancement lesions

Zhaolong Zheng, Qingliang Niu, Pu‐Yeh Wu, Qin Li, Xiaomei Jiang, Haitong Yu, Fucai Xie
article en

Abstract

To extract the histogram features of quantitative parameters from synthetic MRI (SyMRI) and evaluate their diagnostic performance in differentiating benign and malignant BI-RADS 4 non-mass enhancement (NME) lesions. This single-center retrospective study enrolled consecutive patients who underwent dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and SyMRI between October 2019 and May 2023. Conventional imaging characteristics and the time-signal intensity curve (TIC) were evaluated on DCE-MRI. Apparent diffusion coefficient (ADC) values and histogram features were independently measured by two radiologists, with inter-observer agreement evaluated. Multivariate logistic regression was employed to construct three diagnostic models: conventional model (based on DCE-MRI features and ADC values), SyMRI model(based on histogram features) and combined model(integrating festures from both). The diagnostic performance of these models were subsequently evaluated and compared using receiver operating characteristic (ROC) curve analysis. A total of 108 patients (mean age, 48 years ± 11 [standard deviation]) were finally included. Univariate analysis indicated significant differences in distribution, internal enhancement pattern, ADC-min, ADC-mean, TIC, and several histogram characteristics between benign and malignant NME groups. Multivariable logistic regression models demonstrated that the SyMRI model exhibited significantly higher sensitivity (98.6% vs. 73.9%), negative predictive value(NPV) (95.4% vs. 53.7%), positive predictive value(PPV) (83.7% vs. 80.6%), and area under the curve (AUC) (0.821, 95% CI: 0.728–0.915 vs. 0.739, 95% CI: 0.636–0.842) compared to the conventional model The combined model achieved the best performance for differentiating BI-RADS 4 NME lesions, with an AUC of 0.851 (95% CI: 0.764–0.937). Incorporating histogram features of quantitative parameters from synthetic MRI into conventional MRI improved the diagnostic performence in differentiating benign and malignant BI-RADS 4 NME lesions.

BMC Medical ImagingVol. 25(1)
Chinese PLA General Hospital (CN), Weifang People's Hospital (CN), Weifang Chinese Medicine Hospital (CN)
Natural Science Foundation of Shandong Province
Openalex Percentile: Top 57%
MRI in cancer diagnosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.