{"id":"W4220671431","doi":"10.3390/cancers14071674","title":"Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer","year":2022,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Radiomics; Head and neck cancer; Context (archaeology); Medicine; Head and neck; Center (category theory); Cancer imaging; Radiology; Medical physics; Cancer; Artificial intelligence; Computer science; Radiation therapy; Internal medicine; Surgery; Biology","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":[0.000599617,0.0005133844,0.0004720328,0.001496348,0.0001476535,0.0004260291,0.0003475477,0.000365132,0.0004569085],"category_scores_gemma":[0.001648267,0.0001426382,0.0006419702,0.0004125292,0.0001815112,0.0002661237,0.000393699,0.0001490475,0.0001212119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003435708,"about_ca_system_score_gemma":0.0002516125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00302235,"about_ca_topic_score_gemma":0.003699791,"domain_scores_codex":[0.9998486,0.00004173772,0.000008144038,0.00005531892,0.0000240052,0.00002219736],"domain_scores_gemma":[0.9996681,0.0001208479,0.00008214281,0.00002917301,0.00006461892,0.00003518376],"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":[0.002526752,0.0005065024,0.4019904,0.0004916055,0.00067132,0.001097928,0.0004628087,0.1461022,0.05708718,0.0004243583,0.001572526,0.3870666],"study_design_scores_gemma":[0.00004436757,0.0007257711,0.2283652,0.00003284107,0.0003095966,0.0008826756,0.0001828197,0.7598151,0.007608809,0.001120884,0.0008674542,0.00004449596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621484,0.0009146933,0.0354633,0.0001110131,0.00002674568,0.00008248091,0.0004212235,0.0003152438,0.0005168994],"genre_scores_gemma":[0.9946326,0.0001103952,0.004896264,0.00001239196,0.0000259312,0.00001702372,0.000224687,0.000007151982,0.0000736357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00302235,"threshold_uncertainty_score":0.006009519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106561780594232,"score_gpt":0.3270218956852678,"score_spread":0.3059562778793256,"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."}}