{"id":"W3199368218","doi":"10.1016/j.cpet.2021.06.007","title":"Radiomics in PET Imaging","year":2021,"lang":"en","type":"review","venue":"PET Clinics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Radiomics; Medicine; Medical physics; Pipeline (software); Harmonization; Checklist; Pet imaging; Artificial intelligence; Nuclear medicine; Positron emission tomography; Computer science; Radiology; Psychology","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001574307,0.0004665432,0.003163346,0.0003770807,0.00005184553,0.00007223906,0.0003092119,0.0001306428,0.0002091583],"category_scores_gemma":[0.002943444,0.000406008,0.0008894964,0.0005533883,0.0001248736,0.00005033313,0.0001903273,0.002778485,0.0001132672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002506283,"about_ca_system_score_gemma":0.001129135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002286132,"about_ca_topic_score_gemma":0.000003501828,"domain_scores_codex":[0.9967097,0.0002828546,0.001402278,0.0007203345,0.0003651811,0.0005196716],"domain_scores_gemma":[0.9975521,0.0008973192,0.0004280413,0.0007587397,0.00007555259,0.0002882291],"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":[0.000003615382,0.0001142518,0.000459008,0.007262887,0.00009814287,0.006580946,0.00003036554,0.000002270811,5.811111e-8,0.0001718224,0.00303157,0.9822451],"study_design_scores_gemma":[0.0007170672,0.0000208696,0.00001665427,0.02334004,0.0008628453,0.005391356,0.00002579398,0.003748841,2.110323e-8,0.00004899098,0.9654847,0.0003427978],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00003746918,0.9946461,0.0001716285,0.0006936281,0.001239096,0.0004371518,0.00001542519,0.00007969088,0.002679806],"genre_scores_gemma":[0.000006690614,0.9902713,0.005755345,0.001092382,0.0008681055,0.00003346441,0.0005086691,0.0001497584,0.001314279],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9819022,"threshold_uncertainty_score":0.9998392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04016412162525951,"score_gpt":0.4178017834804213,"score_spread":0.3776376618551618,"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."}}