{"id":"W4361267463","doi":"10.3390/cancers15072056","title":"Combination of FDG PET/CT Radiomics and Clinical Parameters for Outcome Prediction in Patients with Hodgkin’s Lymphoma","year":2023,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; William Osler Health System; Princess Margaret Cancer Centre; Women's College Hospital; University of Toronto; University Health Network; Mount Sinai Hospital","funders":"","keywords":"Medicine; Logistic regression; Radiomics; Confidence interval; Nuclear medicine; Univariate analysis; Hazard ratio; Univariate; Proportional hazards model; Internal medicine; Radiology; Multivariate analysis; Multivariate statistics; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001158403,0.0005058417,0.0005250323,0.001060823,0.0002017628,0.0006275567,0.0002124959,0.0003369859,0.001130779],"category_scores_gemma":[0.002221899,0.0001748087,0.0003687604,0.0005564709,0.0002486197,0.0004704662,0.0004626807,0.0003501901,0.0003444527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001690068,"about_ca_system_score_gemma":0.0001786325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002674188,"about_ca_topic_score_gemma":0.0005095391,"domain_scores_codex":[0.9996386,0.0001536182,0.00004110214,0.00004857133,0.00007447167,0.00004366567],"domain_scores_gemma":[0.9989809,0.0003675127,0.0002970679,0.00007209703,0.00009737811,0.0001848931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007006994,0.00003907106,0.9916797,0.00001821896,0.00006766254,0.00009723113,0.00001742367,0.000185019,0.0004859672,0.00000730871,0.00006866582,0.006633092],"study_design_scores_gemma":[0.00003666402,0.0006069478,0.9950542,0.00001888608,0.0001768487,0.0009902413,0.0001013989,0.002087933,0.0004685478,0.00008888214,0.000361086,0.000008303136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981871,0.001011931,0.0002662073,0.00005156467,0.000009275983,0.000007461199,0.00009294973,0.000009704,0.0003637972],"genre_scores_gemma":[0.9995316,0.0001060349,0.0001264505,0.000009704958,0.00002494567,0.000004244034,0.000118954,0.000001598363,0.00007652514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001158403,"threshold_uncertainty_score":0.006126285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162835097208362,"score_gpt":0.3281461251854436,"score_spread":0.30651777421336,"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."}}