{"doi":"10.1101/2025.05.23.25328257","title":"Rapid Discrimination of High-Grade Prostate Cancer Using Label-Free Fluorescence Lifetime Measurements","abstract":"Purpose: Histologic evaluation of prostatic needle biopsies is essential for prostate cancer (PCa) diagnosis and treatment planning, yet tissue targeting remains suboptimal despite MRI-guided Bx procedures. This pilot study investigates the use of label-free Fluorescence Lifetime Imaging (FLIm) for real-time biopsy guidance. Using ex vivo specimens, we assess FLIm's preliminary efficacy in discriminating malignant from benign prostate tissue. Materials and Methods: Twenty patients undergoing prostate biopsy were enrolled. FLIm measurements were performed immediately after sample collection using a custom fiber-optic probe. Optical parameters from 4 spectral bands associated with distinct endogenous fluorophores including structural proteins and metabolic cofactors (e.g. NADH, FAD) were extracted and labeled based on histological annotation. Data were analyzed to characterize tissue-type differences and train and evaluate a classifier to distinguish malignancy. Results: Separation between benign tissue and Gleason grade ≥4 PCa was achieved using just 2 of 56 FLIm-derived parameters. A Support Vector Machine classifier using all parameters achieved a ROC of 0.88 in identifying grade 4 PCa. A reduced lifetime value in the NADH-associate band, likely due to increased free NADH from upregulated glycolysis, supports the biochemical basis for optical differentiation. Conclusions: FLIm shows significant potential for high-grade PCa identification. The single-fiber approach requires minimal modification for integration into current biopsy tools, supporting its feasibility for clinical translation.","journal":"medRxiv","year":2025,"id":566713,"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":0.9559,"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":659704,"name":"Xiangnan Zhou","orcid":"0000-0002-6715-5176","position":1,"is_corresponding":false},{"id":1471016,"name":"Yash Tipirneni","orcid":null,"position":2,"is_corresponding":false},{"id":1470717,"name":"Shuai Chen","orcid":"0009-0002-7992-4507","position":3,"is_corresponding":false},{"id":256279,"name":"Jinyi Qi","orcid":"0000-0002-5428-0322","position":4,"is_corresponding":false},{"id":388115,"name":"Kenneth A. Iczkowski","orcid":"0000-0002-8909-8811","position":5,"is_corresponding":false},{"id":642437,"name":"Marc Dall’Era","orcid":"0000-0002-2301-2683","position":6,"is_corresponding":false},{"id":289213,"name":"Laura Marcu","orcid":"0000-0003-2369-0748","position":7,"is_corresponding":false},{"id":289206,"name":"Julien Bec","orcid":"0000-0003-1222-4071","position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":null,"created_at":"2026-07-19T02:56:40.491968Z","pmid":"40661262","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":[]}