{"id":"W3214593633","doi":"10.1200/cci.21.00096","title":"End-to-End Non–Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography","year":2021,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre; Toronto General Hospital; University of Toronto","funders":"National Cancer Institute; Bayer","keywords":"Medicine; Stage (stratigraphy); Concordance; Lung cancer; TNM staging system; Computed tomography; Hazard ratio; Cancer; Internal medicine; Retrospective cohort study; Radiology; Cancer staging; Proportional hazards model; Oncology; Confidence interval; Neoplasm staging","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":[0.001853715,0.0008981344,0.0006521636,0.0008371007,0.0002476646,0.0006244444,0.0005700423,0.0005114399,0.0006479899],"category_scores_gemma":[0.004315059,0.0001660021,0.0007021255,0.0003745901,0.0002782537,0.0005021651,0.0007620113,0.0007883984,0.0002434196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007023448,"about_ca_system_score_gemma":0.0007174863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004698283,"about_ca_topic_score_gemma":0.00604531,"domain_scores_codex":[0.9994453,0.000168232,0.00004393645,0.0001718371,0.00008605736,0.00008471969],"domain_scores_gemma":[0.9985777,0.0006381304,0.000261968,0.0001355905,0.0002761772,0.0001103638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001283724,0.0006275984,0.6291935,0.00009902096,0.0006550745,0.0002612935,0.00008330611,0.2218566,0.00489659,0.0004437665,0.002966623,0.1376328],"study_design_scores_gemma":[0.00003292766,0.0002673679,0.06359943,0.00002056996,0.00008349018,0.0001245422,0.00002535337,0.9308872,0.003364303,0.001203476,0.0003700343,0.00002127214],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9483058,0.0005707018,0.04804474,0.0003603303,0.00003385521,0.00007012976,0.00142253,0.0004315992,0.0007603883],"genre_scores_gemma":[0.9920827,0.00005515955,0.00593116,0.00004589949,0.00001369615,0.0000261497,0.001554469,0.000009043051,0.0002817278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004698283,"threshold_uncertainty_score":0.009803534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04213011039090193,"score_gpt":0.3763934208854455,"score_spread":0.3342633104945436,"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."}}