{"id":"W4206194534","doi":"10.1002/sta4.450","title":"Sparse Bayesian predictive modelling of tumour response using radiomic features","year":2022,"lang":"en","type":"article","venue":"Stat","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Frequentist inference; Computer science; Bayesian probability; Feature selection; Bayesian inference; Artificial intelligence; Feature (linguistics); Inference; Machine learning; Model selection; Radiomics; Pattern recognition (psychology); Data mining","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":[0.001466705,0.0005152141,0.000910118,0.0006449476,0.0002055242,0.000756811,0.001499376,0.0008610828,0.001050382],"category_scores_gemma":[0.004075654,0.0004833196,0.0006898827,0.0007520352,0.0006382169,0.0007545725,0.0006283655,0.0007845038,0.0003340983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005026389,"about_ca_system_score_gemma":0.0007525929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005997521,"about_ca_topic_score_gemma":0.008021395,"domain_scores_codex":[0.9995909,0.0001613149,0.00001676095,0.00007489488,0.0001092507,0.00004684326],"domain_scores_gemma":[0.9986563,0.0008851076,0.0002113651,0.00008998097,0.0001274176,0.00002992921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006250669,0.00002652959,0.0007671056,0.00004826233,0.00002942327,0.00004859811,0.00003135558,0.9682344,0.001706985,0.007768993,0.0004682119,0.02080758],"study_design_scores_gemma":[0.000003171499,0.000009047167,0.0001779048,0.000002447148,0.000003994525,0.000007528608,0.000001441425,0.9966965,0.0001563415,0.002858717,0.00007886525,0.000004121652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02689005,0.0002233426,0.9714842,0.0002674101,0.0000148306,0.00002910006,0.0002888962,0.0002119245,0.0005900894],"genre_scores_gemma":[0.8932825,0.0006444256,0.1014741,0.0001945096,0.0001002469,0.0002128965,0.0008894489,0.00007106359,0.003130849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005997521,"threshold_uncertainty_score":0.01192522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02290875228337112,"score_gpt":0.2906381868352594,"score_spread":0.2677294345518882,"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."}}