{"doi":"10.1007/s00330-020-06991-7","title":"Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers","abstract":"OBJECTIVES: To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. METHODS: In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. RESULTS: Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). CONCLUSIONS: Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. KEY POINTS: • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone.","journal":"European Radiology","year":2020,"id":93346,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":40,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8279,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":250547,"name":"Isaac Daimiel Naranjo","orcid":"0000-0002-4878-1710","position":1,"is_corresponding":false},{"id":256910,"name":"Carolina Rossi Saccarelli","orcid":null,"position":2,"is_corresponding":false},{"id":254403,"name":"Almir Galvão Vieira Bitencourt","orcid":"0000-0003-0192-9885","position":3,"is_corresponding":false},{"id":254404,"name":"Peter Gibbs","orcid":"0000-0002-5754-7352","position":4,"is_corresponding":false},{"id":256911,"name":"Michael J. Fox","orcid":null,"position":5,"is_corresponding":false},{"id":254405,"name":"Sunitha B. Thakur","orcid":"0000-0001-8090-3696","position":6,"is_corresponding":false},{"id":282408,"name":"Danny F. Martinez","orcid":"0000-0002-8564-9049","position":7,"is_corresponding":false},{"id":254406,"name":"Maxine S. Jochelson","orcid":"0000-0002-4012-2470","position":8,"is_corresponding":false},{"id":250548,"name":"Elizabeth A. Morris","orcid":"0000-0001-5069-0992","position":9,"is_corresponding":false},{"id":250549,"name":"Katja Pinker","orcid":"0000-0002-2722-7331","position":10,"is_corresponding":false},{"id":250546,"name":"Roberto Lo Gullo","orcid":"0000-0001-6887-292X","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-18T22:30:54.208860Z","pmid":"32594207","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}