{"doi":"10.1109/tbme.2025.3634989","title":"Ex-Vivo Prostate Evaluation of Fused-Data TREIT Using Only Biopsy-Probe Electrodes","abstract":"This study evaluates a fused-data transrectal electrical impedance tomography (TREIT) method for prostate cancer imaging on a set of 22 ex vivo prostates. A previously optimized TREIT algorithm is utilized, and novel validation and fusion approaches leveraging pathology information are considered. Overall, the aim was to increase the sensed volume of a standard 12-core prostate biopsy by adding TREIT imaging. Two TREIT approaches were considered: 1. including prostate boundary information (EIT-P) and 2. including prostate and tumor boundary information (EIT-P+T). Both simple electrical impedance spectroscopy (EIS) metrics and the two imaging approaches (EIT-P and EIT-P+T) were evaluated with respect to biopsy core, 3D (EIT-P) image, and tumor-grade data. Best AUCs of 0.85, 0.84, and 0.83 were found when considering increasing volumes of tissue (0.8%, 2.7%, and 15% of the prostate). The largest measurement volume (15% ), which utilized EIT-P, sensed significantly more prostate tissue than the standard biopsy only approach (<1% ). These represent large improvement compared to prior clinical EIS biopsy and TREIT studies. Tumor-grade analysis (via EIT-P+T) appears to show promise but more data is required to confirm this. Overall, the study made important strides in developing the TREIT technique and further investigation, likely in an in vivo study, appears merited.","journal":"IEEE Transactions on Biomedical Engineering","year":2025,"id":581950,"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":0.9538,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"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":534939,"name":"Xiaotian Wu","orcid":"0000-0003-3178-6256","position":1,"is_corresponding":false},{"id":534940,"name":"Alicia Everitt","orcid":"0000-0001-5427-4566","position":2,"is_corresponding":false},{"id":1493456,"name":"Lawrence M. Dagrosa","orcid":"0000-0002-0677-0116","position":3,"is_corresponding":false},{"id":477048,"name":"Jason R. Pettus","orcid":"0000-0003-2751-0417","position":4,"is_corresponding":false},{"id":534941,"name":"Ryan J. Halter","orcid":"0000-0003-0222-3948","position":5,"is_corresponding":false},{"id":534938,"name":"Ethan K. Murphy","orcid":"0000-0002-4635-3384","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:58:51.328454Z","pmid":"41264459","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":[]}