{"id":"W4224981952","doi":"10.3174/ajnr.a7488","title":"Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma","year":2022,"lang":"en","type":"article","venue":"American Journal of Neuroradiology","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"National Cancer Institute; National Institutes of Health; Cure Brain Cancer Foundation; National Institute of Biomedical Imaging and Bioengineering; American Society of Neuroradiology; Ludwig Institute for Cancer Research; American Roentgen Ray Society; Radiological Society of North America; Cancer Research Institute; Canadian Institute for Advanced Research; AstraZeneca","keywords":"Medicine; Concordance; Immunotherapy; Glioblastoma; Progression-free survival; Oncology; Radiomics; Survival analysis; Post-hoc analysis; Internal medicine; Tumor progression; Overall survival; Radiology; Cancer; Cancer research","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":[0.009949107,0.0008627192,0.0009979645,0.0004360099,0.0002297762,0.0006341349,0.00061723,0.000609084,0.0004561997],"category_scores_gemma":[0.006900141,0.0002999991,0.0009396878,0.0002153856,0.0003686197,0.000495395,0.0004316689,0.001331591,0.0001581555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008855931,"about_ca_system_score_gemma":0.001132276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001169319,"about_ca_topic_score_gemma":0.001011768,"domain_scores_codex":[0.9979628,0.001637402,0.00006075226,0.0001487507,0.0001050245,0.00008517406],"domain_scores_gemma":[0.9979913,0.0009082892,0.0003767985,0.0001888233,0.0002344797,0.0003002704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"nonrandomized_trial","study_design_scores_codex":[0.2078802,0.02739549,0.4226298,0.000360371,0.004076226,0.0002277506,0.0004778308,0.1105566,0.01003048,0.0004986494,0.004730988,0.2111357],"study_design_scores_gemma":[0.02650518,0.1631314,0.1925065,0.00008311815,0.002162426,0.0003227719,0.0001848823,0.6038411,0.007569072,0.001232197,0.002327169,0.0001341163],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972624,0.0003849545,0.001426356,0.0002710607,0.00002829351,0.0002447755,0.0001665081,0.00004108747,0.0001744796],"genre_scores_gemma":[0.9972662,0.0001291298,0.001608267,0.00009468679,0.00002556476,0.0002647369,0.0004436939,0.000007285456,0.0001605252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009949107,"threshold_uncertainty_score":0.05261654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02576246174904804,"score_gpt":0.328026552400316,"score_spread":0.302264090651268,"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."}}