ArticleBMC medical imaging2020
Breast MRI texture analysis for prediction of BRCA-associated genetic risk.
Article in BMC medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Radiogenomic profiling to determine BRCA alteration status-a systematic review and meta-analysis.The British journal of radiology · 2025Pooled it
- Prediction of germline BRCA mutation using clinicopathologic, MRI semantic, and radiomics features in high-risk breast cancer patients: a multicenter study.Frontiers in radiology · 2026Article
- Prediction of Germline BRCA Mutations in High-Risk Breast Cancer Patients Using Machine Learning with Multiparametric Breast MRI Features.Sensors (Basel, Switzerland) · 2025Article
- Bone texture by clinical magnetic resonance imaging is directly related to bone tissue maturity by Fourier-transform infrared spectroscopy.JBMR plus · 2025Article
- Predicting Pathogenic Variants of Breast Cancer Using Ultrasound-Derived Machine Learning Models.Cancers · 2025Article
- Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- Comparing Characteristics of Pelvic High-grade Serous Carcinomas with and without Breast Cancer Gene Variants on MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Article
- Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images.Journal of personalized medicine · 2023Article
- Expression of the Immunohistochemical Markers CK5, CD117, and EGFR in Molecular Subtypes of Breast Cancer Correlated with Prognosis.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence for multimodal data integration in oncology.Cancer cell · 2022Review
- Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundBRCA1/2 deleterious variants account for most of the hereditary breast and ovarian cancer cases. Prediction models and guidelines for the assessment of genetic risk rely heavily on criteria with high variability such as family cancer history. Here we investigated the efficacy of MRI (magnetic resonance imaging) texture features as a predictor for BRCA mutation status.
methodsA total of 41 female breast cancer individuals at high genetic risk, sixteen with a BRCA1/2 pathogenic variant and twenty five controls were included. From each MRI 4225 computer-extracted voxels were analyzed. Non-imaging features including clinical, family cancer history variables and triple negative receptor status (TNBC) were complementarily used. Lasso-principal component regression (L-PCR) analysis was implemented to compare the predictive performance, assessed as area under the curve (AUC), when imaging features were used, and lasso logistic regression or conventional logistic regression for the remaining analyses.
resultsLasso-selected imaging principal components showed the highest predictive value (AUC 0.86), surpassing family cancer history. Clinical variables comprising age at disease onset and bilateral breast cancer yielded a relatively poor AUC (~ 0.56). Combination of imaging with the non-imaging variables led to an improvement of predictive performance in all analyses, with TNBC along with the imaging components yielding the highest AUC (0.94). Replacing family history variables with imaging components yielded an improvement of classification performance of ~ 4%, suggesting that imaging compensates the predictive information arising from family cancer structure.
conclusionsThe L-PCR model uncovered evidence for the utility of MRI texture features in distinguishing between BRCA1/2 positive and negative high-risk breast cancer individuals, which may suggest value to diagnostic routine. Integration of computer-extracted texture analysis from MRI modalities in prediction models and inclusion criteria might play a role in reducing false positives or missed cases especially when established risk variables such as family history are missing.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.