{"doi":"10.48550/arxiv.2409.08331","title":"Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning","abstract":"Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023 [1]. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cross-sections sliced from 3D biopsy tissue specimens. This process impedes the analysis of complex tissue structures such as glands, which can vary depending on the tissue slice examined. We propose a novel digital pathology data source called a \"volumetric core,\" obtained via the extraction and co-alignment of serially sectioned tissue sections using a novel morphology-preserving alignment framework. We trained an attention-based multiple-instance learning (ABMIL) framework on deep features extracted from volumetric patches to automatically classify the Gleason Grade Group (GGG). To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning. We ran our morphology preserving alignment framework to construct 10,210 volumetric cores, leaving out 30% for pretraining. The rest of the dataset was used to train ABMIL, which resulted in a 0.958 macro-average AUC, 0.671 F1 score, 0.661 precision, and 0.695 recall averaged across all five GGG significantly outperforming the 2D baselines.","journal":"PubMed","year":2024,"id":503714,"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":0.9589,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1353990,"name":"Mara Pleasure","orcid":null,"position":1,"is_corresponding":false},{"id":613898,"name":"Zichen Wang","orcid":"0000-0002-9369-6611","position":2,"is_corresponding":false},{"id":633029,"name":"Anthony Sisk","orcid":"0000-0001-7262-3985","position":3,"is_corresponding":false},{"id":1353525,"name":"Yang Zong","orcid":"0009-0008-9950-8184","position":4,"is_corresponding":false},{"id":1353526,"name":"Kimberly Johana Cárdenas Flores","orcid":"0009-0005-1850-528X","position":5,"is_corresponding":false},{"id":241711,"name":"William Speier","orcid":"0000-0002-0890-8684","position":6,"is_corresponding":false},{"id":241712,"name":"Corey Arnold","orcid":"0000-0002-4119-8143","position":7,"is_corresponding":false},{"id":1049280,"name":"Ekaterina Redekop","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:10:31.826282Z","pmid":"39314499","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":[]}