{"id":"W3166123591","doi":"10.1200/cci.20.00121","title":"Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade","year":2021,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Neuroendocrine Tumor Research Advances","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Cancer Institute","keywords":"Radiomics; Neuroendocrine tumors; Radiology; Pathological; Pancreatic neuroendocrine tumor; Computed tomography; Radiography; Predictive value; Medicine; Nuclear medicine; Pathology; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003368804,0.0003584524,0.001372582,0.0006133067,0.0001209427,0.0001203409,0.0003496782,0.00001284673,0.00041755],"category_scores_gemma":[0.003057015,0.0003063679,0.0007707222,0.004243584,0.0002954553,0.0003989939,0.00036713,0.0008622884,0.0001476359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008562903,"about_ca_system_score_gemma":0.0004735744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007462793,"about_ca_topic_score_gemma":0.000182378,"domain_scores_codex":[0.9953888,0.0002521281,0.002002588,0.0004385395,0.001105345,0.0008125782],"domain_scores_gemma":[0.9955397,0.001297178,0.0003994925,0.0009400236,0.0008290717,0.0009945814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003138364,0.002659463,0.8934724,0.002427385,0.00755583,0.02316207,0.0006817629,0.003323283,0.0009227956,0.001036726,0.05348352,0.008136334],"study_design_scores_gemma":[0.01369318,0.009486521,0.7252231,0.0008692413,0.009513428,0.002842824,0.003509964,0.1051427,0.01413795,0.0005740095,0.1132592,0.001747905],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846613,0.0005602907,0.004257042,0.005517983,0.0005653344,0.001219357,0.0003207979,0.0003182645,0.002579661],"genre_scores_gemma":[0.8562984,0.00150356,0.1178136,0.02158649,0.000528858,0.0003715338,0.0003709045,0.0001127511,0.001413848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1682493,"threshold_uncertainty_score":0.9999388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0668042523606518,"score_gpt":0.4378712353746405,"score_spread":0.3710669830139887,"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."}}