NDLI: Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: a multicentre study

Content Provider	Springer Nature : BioMed Central
Author	Hu, Yalan Cai, Zhenhai Aierken, Nijiati Liu, Yueqi Shao, Nan Shi, Yawei Zhang, Mengmeng Hu, Yangling Zhang, Xiaoling Lin, Ying
Abstract	Background and purpose The study aimed to create a radiomics model based on breast intra- and peri-tumoral regions in dynamic contrast-enhanced (DCE) MRI to distinguish benign from malignant breast lesions of Breast Imaging Reporting and Data System (BI-RADS) 4. Materials and methods A total of 516 patients from Hospital 1 were assigned to the training cohort. Then, 146 and 52 patients were enrolled from Hospital 2 and 3, respectively, as the internal and external test cohort. Seven classification models were built, using features extracted from the intra- and peri-tumoral regions. Diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis and compared by the DeLong test. Subgroup analysis was performed after stratifying all lesions by enhancement pattern and the subdivision of BI-RADS 4. Results The Comb2 model, built with features from peri-tumoral 2 mm and intra-tumoral region, demonstrated the best performance with AUCs of 0.828 and 0.844 in the internal and external test cohort, respectively. The Comb2 model was robust in both mass and non-mass enhancement (NME) lesions. At the three exploratory cutoff values on the ROC curve, the model identified 9.1% (sensitivity of C1 ≥ 98%), 27.3% (sensitivity of C2 ≥ 95%) and 36.4% (sensitivity of C3 ≥ 90%) of the benign lesions in the external test cohort. Applying the identified cutoff values in the external test cohort showed the potential to lower the number of unnecessary biopsies of benign lesions. Conclusion An MRI-based radiomics model built with features extracted from the intra-tumoral region combined with the peri-tumoral 2 mm showed the best potential to reduce false-positive diagnoses and may avoid unnecessary biopsies with a low underestimate risk.
Related Links	https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-025-02605-y.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s13014-025-02605-y
Journal	Radiation Oncology
Issue Number	1
Volume Number	20
Language	English
Publisher	BioMed Central
Publisher Date	2025-02-28
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Radiotherapy Imaging Radiology DCE-MRI BI-RADS Breast cancer Radiomics Machine learning
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.6/2023

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