{"id":"W3093315369","doi":"10.1016/j.tranon.2020.100906","title":"Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics","year":2020,"lang":"en","type":"article","venue":"Translational Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"National Center for Advancing Translational Sciences; Fonds de Recherche du Québec - Santé","keywords":"Medicine; Radiomics; Interquartile range; Oncology; Radiation therapy; Lymph node; Radiology; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006564591,0.0001823888,0.0008750048,0.0005184968,0.00005341778,0.000004092947,0.0001065735,0.00006145884,0.0003017011],"category_scores_gemma":[0.0002609151,0.0001706471,0.0002734752,0.001034842,0.0001601728,0.00007114752,0.00001315985,0.0005990965,5.26303e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001747673,"about_ca_system_score_gemma":0.0004786647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002945733,"about_ca_topic_score_gemma":0.00003474531,"domain_scores_codex":[0.9977905,0.0003744274,0.0008241364,0.0003235857,0.0004565786,0.000230765],"domain_scores_gemma":[0.998631,0.0004783398,0.0004235024,0.00009860733,0.0001982226,0.0001703395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001766128,0.0008130677,0.8130874,0.0001824548,0.0003677161,0.0003880763,0.001112212,0.1076215,0.06714909,0.00006357996,0.00002351601,0.007425261],"study_design_scores_gemma":[0.004727435,0.0009976432,0.1799363,0.00002358209,0.0007509399,0.00007585582,0.00004597342,0.8126377,0.0004123416,0.000007756199,0.0002931714,0.00009131299],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863651,0.0008471541,0.008607407,0.003471375,0.00007311763,0.0003362033,0.0001032533,0.00003261464,0.0001637727],"genre_scores_gemma":[0.9878235,0.0000476084,0.01045807,0.0005039544,0.0001051685,0.000005715246,0.001014326,0.00003067876,0.0000109497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7050162,"threshold_uncertainty_score":0.6958789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02766428694063654,"score_gpt":0.3124142368143861,"score_spread":0.2847499498737496,"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."}}