{"id":"W4407764510","doi":"10.1016/j.semcancer.2025.02.009","title":"Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer","year":2025,"lang":"en","type":"review","venue":"Seminars in Cancer Biology","topic":"AI in cancer detection","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pancreas Centre (Canada)","funders":"","keywords":"Biomarker; Pancreatic ductal adenocarcinoma; Pancreatic cancer; Omics; Biomarker discovery; Medicine; Disease; Population; Cancer; Bioinformatics; Internal medicine; Biology; Proteomics; Environmental health; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000472235,0.0003561948,0.001248345,0.0005814134,0.0000705256,0.00003135132,0.0006922832,0.0005694775,0.000006719309],"category_scores_gemma":[0.0001180984,0.0003347992,0.0001933939,0.00106671,0.000219168,0.0001061725,0.0002591331,0.0002904894,0.000001093552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006266021,"about_ca_system_score_gemma":0.0006834094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001509395,"about_ca_topic_score_gemma":0.001162658,"domain_scores_codex":[0.9976604,0.0001898584,0.0008407053,0.0008439728,0.00008722683,0.0003778607],"domain_scores_gemma":[0.997993,0.0008229134,0.0005598943,0.000475307,0.0001015452,0.00004737623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002734265,0.00001354743,0.00004362739,0.005616194,0.0001001108,6.390251e-7,0.00008841509,0.000005602072,0.00007493098,0.0006016053,0.0000378207,0.9933901],"study_design_scores_gemma":[0.0003630678,0.001485555,0.00006333245,0.05461852,0.001202996,0.00004029789,0.00007364352,0.006178258,0.008429342,0.03417162,0.8915239,0.001849496],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006311988,0.8705752,0.1258321,0.00005811921,0.002242948,0.001010677,0.0001437225,0.00003808286,0.00003593257],"genre_scores_gemma":[0.0004288686,0.9939455,0.003700258,0.00002365701,0.0001705704,0.001635573,0.000009643079,0.00002127412,0.0000646964],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9915407,"threshold_uncertainty_score":0.9999104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05149028492997009,"score_gpt":0.3745916745409362,"score_spread":0.3231013896109661,"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."}}