{"doi":"10.1101/2020.12.17.423077","title":"Deep Learning Identification of Stiffness Markers in Breast Cancer","abstract":"Abstract While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo , is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Global breast tissue mechanics correlate weakly with the percentage of blood vessels and fibrotic tissue, and non-significantly with the percentage of fat, ducts, tumor cells, and wavy collagen in tissue. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":124278,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9567,"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":568796,"name":"Joo Ho Kim","orcid":"0000-0003-2904-8780","position":1,"is_corresponding":false},{"id":478622,"name":"Ashley Kiemen","orcid":"0000-0002-6281-2616","position":2,"is_corresponding":false},{"id":478623,"name":"Pei‐Hsun Wu","orcid":"0000-0002-1556-4357","position":3,"is_corresponding":false},{"id":247991,"name":"Mehran Habibi","orcid":"0000-0003-2944-4388","position":4,"is_corresponding":false},{"id":253568,"name":"Marissa J. White","orcid":"0000-0001-8821-051X","position":5,"is_corresponding":false},{"id":237413,"name":"Jude M. Phillip","orcid":"0000-0002-2999-5254","position":6,"is_corresponding":false},{"id":461172,"name":"Luo Gu","orcid":"0000-0002-9813-7202","position":7,"is_corresponding":false},{"id":288413,"name":"Denis Wirtz","orcid":"0000-0001-6147-3045","position":8,"is_corresponding":false},{"id":568795,"name":"Alexandra Sneider","orcid":"0000-0001-8575-3084","position":0,"is_corresponding":true}],"reference_count":95,"raw_metadata":null,"created_at":"2026-07-18T23:15:07.789881Z","pmid":null,"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":[]}