{"doi":"10.1126/sciadv.adz1820","title":"Rapid cancer diagnosis using deep learning–powered label-free subcellular-resolution photoacoustic histology","abstract":"Traditional hematoxylin and eosin staining in formalin-fixed paraffin-embedded sections, while essential for diagnostic pathology, is time-consuming, labor intensive, and prone to artifacts that can obscure critical histological details. Label-free ultraviolet photoacoustic microscopy (UV-PAM) has emerged as a promising alternative, offering fast histology-like images without the need for traditional staining and excessive tissue preparation. However, current UV-PAM systems face challenges in achieving the high spatial resolution required for detailed histological analysis and diagnosis. To address this, we developed a subcellular-resolution UV-PAM (SRUV-PAM) system with a 240-nanometer resolution, enabled by the integration of a high numerical aperture (NA) objective lens (NA = 0.64) and the precise piezo actuators for fine scanning control. This configuration allows visualization of detailed nuclear structures. In addition, we demonstrated virtual staining of SRUV-PAM images via cycle-consistent generative adversarial networks and diagnosis of malignant and benign tumors in liver tissues via densely connected convolutional networks DenseNet-121, achieving an area under the receiver operating characteristic curve of 0.902.","journal":"Science Advances","year":2025,"id":524894,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.949,"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":473756,"name":"Rui Cao","orcid":"0000-0003-4444-7528","position":1,"is_corresponding":false},{"id":668181,"name":"Yilin Luo","orcid":"0000-0002-4611-3049","position":2,"is_corresponding":false},{"id":1373231,"name":"Cindy Liu","orcid":"0000-0002-1075-1436","position":3,"is_corresponding":false},{"id":845639,"name":"Yushun Zeng","orcid":"0000-0001-5995-4085","position":4,"is_corresponding":false},{"id":585749,"name":"Yide Zhang","orcid":"0000-0002-9463-3970","position":5,"is_corresponding":false},{"id":266837,"name":"Qifa Zhou","orcid":"0000-0003-1527-3020","position":6,"is_corresponding":false},{"id":350913,"name":"Samuel Davis","orcid":"0000-0002-0871-8880","position":7,"is_corresponding":false},{"id":775854,"name":"Massimo D’Apuzzo","orcid":"0000-0001-8146-0997","position":8,"is_corresponding":false},{"id":253575,"name":"Lihong V. Wang","orcid":"0000-0001-9783-4383","position":9,"is_corresponding":false},{"id":1399045,"name":"Byullee Park","orcid":"0000-0002-2949-1419","position":0,"is_corresponding":true}],"reference_count":36,"raw_metadata":null,"created_at":"2026-07-19T02:50:16.562292Z","pmid":"41270177","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":[]}