{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001436109,0.0001070604,0.0003223048,0.00009195775,0.00007021618,0.000006198246,0.00006434922,0.000008879759,0.0000817281],"category_scores_gemma":[0.00008457319,0.0001028758,0.00003985163,0.0001160085,0.0001189224,0.00004403818,0.00006921734,0.000195207,3.214003e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004389593,"about_ca_system_score_gemma":0.0001444289,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009567638,"about_ca_topic_score_gemma":0.00008680404,"domain_scores_codex":[0.9990019,0.00003533164,0.0003360289,0.000273423,0.0001903259,0.0001629535],"domain_scores_gemma":[0.9994125,0.00003973726,0.0001562325,0.0001743444,0.00005804525,0.0001591153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001791678,0.00005304495,0.9436397,0.0001600423,0.00008841883,0.00001520324,0.0008458217,0.001214836,0.01011487,0.00002099576,0.002421798,0.04124612],"study_design_scores_gemma":[0.01528204,0.0005451033,0.7925386,0.000311509,0.0003058238,0.000168701,0.002454676,0.1534861,0.002302818,0.00004862995,0.03223998,0.0003160036],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932204,0.001826118,0.002629048,0.00101384,0.0002589469,0.0004255596,0.0005206479,0.00002415215,0.00008131743],"genre_scores_gemma":[0.9967748,0.0005512634,0.001404805,0.0008807381,0.00006182306,0.0001019325,0.00008629043,0.00002038396,0.0001179882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1522712,"threshold_uncertainty_score":0.9970278,"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."}}