{"id":"W4399603768","doi":"10.3390/cancers16122215","title":"Relating Macroscopic PET Radiomics Features to Microscopic Tumor Phenotypes Using a Stochastic Mathematical Model of Cellular Metabolism and Proliferation","year":2024,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Phenotype; Positron emission tomography; Cellular metabolism; Tumor microenvironment; In vivo; Computational biology; Pathology; Biology; Computer science; Tumor cells; Cancer research; Metabolism; Medicine; Gene; Genetics; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001981202,0.000133655,0.0003200391,0.0001252708,0.00006650234,0.00005242638,0.00005018564,0.00003471986,0.00001127578],"category_scores_gemma":[0.0002364422,0.0001137594,0.00004649333,0.0001509709,0.00009427957,0.00006043047,0.00003278307,0.0002951872,0.000002094536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002369162,"about_ca_system_score_gemma":0.0004183045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004450829,"about_ca_topic_score_gemma":0.000001145576,"domain_scores_codex":[0.9991107,0.00001950199,0.0002442825,0.0002508278,0.0001762163,0.0001984959],"domain_scores_gemma":[0.9995799,0.00005722664,0.00004682456,0.0001304161,0.00003725338,0.0001483932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006390985,0.00001158352,0.0001038139,0.001363892,0.00009848693,0.00004013889,0.003243376,0.1322994,0.855616,0.005155922,0.0002485393,0.001754913],"study_design_scores_gemma":[0.000379298,0.00003855499,0.00004860744,0.0009295066,0.0002119729,0.0001011476,0.0001012533,0.9882429,0.008551587,0.001254132,0.00003791854,0.0001031048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8095022,0.002466409,0.187016,0.0003618375,0.0001748204,0.0003070218,0.000007246565,0.00004169699,0.0001227874],"genre_scores_gemma":[0.9535624,0.00001461375,0.04558605,0.0003590362,0.0001718601,0.00001362021,0.000005238969,0.00003601294,0.0002511248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8559435,"threshold_uncertainty_score":0.4638975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392619257876467,"score_gpt":0.296932975312569,"score_spread":0.2830067827338044,"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."}}