{"id":"W4402558328","doi":"10.1016/j.annonc.2024.08.1417","title":"1362P Evaluation of imaging-based prognostication (IPRO) for advanced non-small cell lung cancer (aNSCLC) using deep learning applied to computed tomography (CT)","year":2024,"lang":"en","type":"article","venue":"Annals of Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Glycemic Index Laboratories; Princess Margaret Cancer Centre; Systems, Applications & Products in Data Processing (Canada); University of Calgary","funders":"","keywords":"Medicine; Lung cancer; Computed tomography; Radiology; Nuclear medicine; Oncology","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":[0.001346919,0.000608956,0.0004226797,0.0006798838,0.0001934294,0.0005908483,0.0004417081,0.0006034525,0.002757651],"category_scores_gemma":[0.003089687,0.0001075038,0.0004531429,0.0002472053,0.0001724824,0.0003669422,0.0006217358,0.0003667587,0.0006704844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004124933,"about_ca_system_score_gemma":0.0008142851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004388212,"about_ca_topic_score_gemma":0.005513743,"domain_scores_codex":[0.9996964,0.00009159818,0.00001708389,0.00005306653,0.00009455397,0.0000474254],"domain_scores_gemma":[0.9991747,0.0003451611,0.00005507908,0.00007066051,0.0002559919,0.00009850571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003827859,0.001227351,0.3384911,0.0003114523,0.0006748913,0.0005539483,0.00006335671,0.1067026,0.01431448,0.001350565,0.03152028,0.5009621],"study_design_scores_gemma":[0.0002218644,0.001574226,0.06046937,0.00005052429,0.0002645842,0.0003954644,0.00007546038,0.9168369,0.01316622,0.0014173,0.005497223,0.00003092466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9518793,0.002130267,0.02957176,0.001851457,0.0003011227,0.0002107219,0.005295977,0.00145119,0.007308159],"genre_scores_gemma":[0.984218,0.0002331089,0.00843523,0.0001360044,0.00005560062,0.00005194601,0.004521316,0.00003800531,0.002310731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004388212,"threshold_uncertainty_score":0.009225249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0417545052990924,"score_gpt":0.406830213404072,"score_spread":0.3650757081049796,"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."}}