NDLI: Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer

Content Provider	Springer Nature : BioMed Central
Author	Deng, Tingting Liang, Jianwen Yan, Cuiju Ni, Mengqian Xiang, Huiling Li, Chunyan Ou, Jinjing Lin, Qingguang Liu, Lixian Tang, Guoxue Luo, Rongzhen An, Xin Gao, Yi Lin, Xi
Abstract	Background Identifying breast cancer (BC) patients with germline breast cancer susceptibility gene (gBRCA) mutation is important. The current criteria for germline testing for BC remain controversial. This study aimed to develop a nomogram incorporating ultrasound radiomic features and clinicopathological factors to predict gBRCA mutations in patients with BC. Materials and methods In this retrospective study, 497 women with BC who underwent gBRCA genetic testing from March 2013 to May 2022 were included, including 348 for training (84 with and 264 without a gBRCA mutation) and 149 for validation(36 patients with and 113 without a gBRCA mutation). Factors associated with gBRCA mutations were identified to establish a clinicopathological model. Radiomics features were extracted from the intratumoral and peritumoral regions (3 mm and 5 mm) of each image. The least absolute shrinkage and selection operator regression algorithm was used to select the features and logistic regression analysis was used to construct three imaging models. Finally, a nomogram that combined clinicopathological and radiomics features was developed. The models were evaluated based on the area under the receiver operating characteristic curve (AUC), calibration, and clinical usefulness. Results Age at diagnosis, family history of BC, personal history of other BRCA-related cancers, and human epidermal growth factor receptor 2 status were independent predictors of the clinicopathological model. The AUC of the imaging radiomics model combining intratumoral and peritumoral 3 mm areas in the validation set was 0.783 (95% confidence interval [CI]: 0.702—0.862), which showed the best performance among three imaging models. The nomogram yielded better performance than the clinicopathological model in validation sets (AUC: 0.824 [0.755—0.894] versus 0.659 [0.563—0.755], p = 0.007). Conclusion The nomogram based on ultrasound images and clinicopathological factors performs well in predicting gBRCA mutations in BC patients and may help to improve clinical decisions about genetic testing.
Related Links	https://cancerimagingjournal.biomedcentral.com/counter/pdf/10.1186/s40644-024-00676-w.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14707330
DOI	10.1186/s40644-024-00676-w
Journal	Cancer Imaging
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-02-29
Access Restriction	Open
Subject Keyword	Oncology Cancer Research Imaging Radiology Nuclear Medicine BRCA Breast cancer Ultrasound Radiomics Nomogram
Content Type	Text
Resource Type	Article
Subject	Radiological and Ultrasound Technology Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.3/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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