{"doi":"10.1093/jncics/pkaf037","title":"Deep learning analysis of hematoxylin and eosin-stained benign breast biopsies to predict future invasive breast cancer","abstract":"BACKGROUND: Benign breast disease is an important risk factor for breast cancer development. In this study, we analyzed hematoxylin and eosin-stained whole-slide images from diagnostic benign breast disease biopsies using different deep learning approaches to predict which individuals would subsequently developed breast cancer (cases) or would not (controls). METHODS: We randomly divided cases and controls from a nested case-control study of 946 women with benign breast disease into training (331 cases, 331 control individuals) and test (142 cases, 142 control individuals) groups. We employed customized VGG-16 and AutoML machine learning models for image-only classification using whole-slide images, logistic regression for classification using only clinicopathological characteristics, and a multimodal network combining whole-slide images and clinicopathological characteristics for classification. RESULTS: Both image-only (area under the receiver operating characteristic curve [AUROC] = 0.83 [SE = 0.001] and 0.78 [SE = 0.001] for customized VGG-16 and AutoML models, respectively) and multimodal (AUROC = 0.89 [SE = 0.03]) networks had high discriminatory accuracy for breast cancer. The clinicopathological-characteristics-only model had the lowest AUROC (0.54 [SE = 0.03]). In addition, compared with the customized VGG-16 model, which performed better than the AutoML model, the multimodal network had improved accuracy (AUROC = 0.89 [SE = 0.03] vs 0.83 [SE = 0.02]), sensitivity (AUROC = 0.93 [SE = 0.04] vs 0.83 [SE = 0.003]), and specificity (AUROC = 0.86 [SE = 0.03] vs 0.84 [SE = 0.003]). CONCLUSION: This study opens promising avenues for breast cancer risk assessment in women with benign breast disease. Integrating whole-slide images and clinicopathological characteristics through a multimodal approach substantially improved predictive model performance. Future research will explore deep learning techniques to understand benign breast disease progression to invasive breast cancer.","journal":"JNCI Cancer Spectrum","year":2025,"id":563855,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8003,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":463253,"name":"Mustapha Abubakar","orcid":"0000-0003-3728-6535","position":1,"is_corresponding":false},{"id":282490,"name":"Ruth M. Pfeiffer","orcid":"0000-0001-7791-2698","position":2,"is_corresponding":false},{"id":243124,"name":"Thomas E. Rohan","orcid":"0000-0001-6897-4045","position":3,"is_corresponding":false},{"id":322729,"name":"Máire A. Duggan","orcid":"0000-0001-6625-7683","position":4,"is_corresponding":false},{"id":1388448,"name":"Kathryn Richert-Boe","orcid":null,"position":5,"is_corresponding":false},{"id":272280,"name":"Jonas S. Almeida","orcid":"0000-0002-7883-7922","position":6,"is_corresponding":false},{"id":302470,"name":"Gretchen L. Gierach","orcid":"0000-0002-0165-5522","position":7,"is_corresponding":false},{"id":341710,"name":"Monjoy Saha","orcid":"0000-0002-6470-3544","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":null,"created_at":"2026-07-19T02:56:17.117043Z","pmid":"40193520","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":[]}