{"doi":"10.3390/cancers13246273","title":"Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results","abstract":"The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1- patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31-81), 27 had PD-L1- tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.","journal":"Cancers","year":2021,"id":168260,"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":31,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9575,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":236121,"name":"Hannah Y. Wen","orcid":"0000-0001-6794-5234","position":1,"is_corresponding":false},{"id":452315,"name":"Jeffrey S. Reiner","orcid":null,"position":2,"is_corresponding":false},{"id":696955,"name":"Raza S. Hoda","orcid":"0000-0002-6316-1819","position":3,"is_corresponding":false},{"id":353291,"name":"Varadan Sevilimedu","orcid":"0000-0002-0938-9396","position":4,"is_corresponding":false},{"id":282408,"name":"Danny F. Martinez","orcid":"0000-0002-8564-9049","position":5,"is_corresponding":false},{"id":254405,"name":"Sunitha B. Thakur","orcid":"0000-0001-8090-3696","position":6,"is_corresponding":false},{"id":254406,"name":"Maxine S. Jochelson","orcid":"0000-0002-4012-2470","position":7,"is_corresponding":false},{"id":254404,"name":"Peter Gibbs","orcid":"0000-0002-5754-7352","position":8,"is_corresponding":false},{"id":250549,"name":"Katja Pinker","orcid":"0000-0002-2722-7331","position":9,"is_corresponding":false},{"id":250546,"name":"Roberto Lo Gullo","orcid":"0000-0001-6887-292X","position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":null,"created_at":"2026-07-18T23:46:02.603031Z","pmid":"34944898","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":[]}