{"id":"W4414208331","doi":"10.3390/cancers17182991","title":"A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole Slide Images","year":2025,"lang":"en","type":"article","venue":"Cancers","topic":"AI in cancer detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; Royal Jubilee Hospital; University of British Columbia","funders":"BC Cancer Foundation","keywords":"Grading (engineering); Neuroendocrine tumors; Survival analysis; Deep learning; Tumor grade; Multivariate analysis; Overall survival; Multivariate statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001179755,0.0008871306,0.0007023002,0.001091834,0.0004082439,0.0009782028,0.0018947,0.001428251,0.002216971],"category_scores_gemma":[0.002006265,0.0003622133,0.000958437,0.0007009315,0.0003830958,0.0006750444,0.0009162644,0.001572179,0.001013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001528647,"about_ca_system_score_gemma":0.001491098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02106595,"about_ca_topic_score_gemma":0.01896136,"domain_scores_codex":[0.9995291,0.00009303759,0.00002732852,0.000142157,0.0001149503,0.00009348185],"domain_scores_gemma":[0.9993667,0.0002027106,0.00006086697,0.0000532197,0.0002683769,0.00004806578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003699129,0.0003703093,0.006661767,0.000142143,0.0001802471,0.0002571862,0.0001037762,0.4229835,0.01005395,0.002886562,0.01509599,0.5408946],"study_design_scores_gemma":[0.000006147473,0.00003072588,0.0004117536,0.000008803876,0.000008052461,0.00002087903,0.000008646728,0.9967775,0.001028156,0.001058307,0.0006360731,0.000004935815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1222163,0.002321204,0.8621902,0.001524735,0.0002704581,0.0002660988,0.002063055,0.005814624,0.003333254],"genre_scores_gemma":[0.7126633,0.0008702141,0.2680106,0.0009294432,0.0002144178,0.0004600663,0.006244881,0.0002007951,0.0104063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02106595,"threshold_uncertainty_score":0.04188669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01579119497896978,"score_gpt":0.2891659667071627,"score_spread":0.2733747717281929,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}