{"id":"W4399388943","doi":"10.3390/diagnostics14111205","title":"Gastro-Esophageal Cancer: Can Radiomic Parameters from Baseline 18F-FDG-PET/CT Predict the Development of Distant Metastatic Disease?","year":2024,"lang":"en","type":"article","venue":"Diagnostics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Sinai Health System; Women's College Hospital; Toronto General Hospital; University of Toronto; University Health Network","funders":"","keywords":"Medicine; Radiomics; Sarcopenia; Stage (stratigraphy); PET-CT; Radiology; Disease; Esophageal cancer; Proportional hazards model; Cancer; Internal medicine; Oncology; Nuclear medicine; Positron emission tomography","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":[0.0005738764,0.000257283,0.0004356172,0.0001071162,0.0001146438,0.00006130517,0.0002236575,0.000006747234,0.0001420711],"category_scores_gemma":[0.001747972,0.0001711266,0.0001482201,0.0002493331,0.0002446159,0.00003962591,0.00007293844,0.0004907268,0.0000135869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001819012,"about_ca_system_score_gemma":0.0008032615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004415682,"about_ca_topic_score_gemma":0.00005495487,"domain_scores_codex":[0.9979644,0.0001225055,0.0005970452,0.0003917704,0.0005488931,0.0003753491],"domain_scores_gemma":[0.9969478,0.002103539,0.0001087975,0.0003870644,0.00006021297,0.0003925696],"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":[0.000876307,0.001910209,0.5324359,0.004182264,0.006482172,0.04427858,0.008044127,0.01263282,0.002602862,0.002404399,0.07699212,0.3071582],"study_design_scores_gemma":[0.00460226,0.000363946,0.1661637,0.007038906,0.006538485,0.0004986677,0.00133796,0.7396941,0.004895843,0.001718204,0.06607697,0.001071008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685059,0.01001297,0.01362308,0.005371454,0.00118058,0.0005243006,0.0006203338,0.0001148437,0.00004657333],"genre_scores_gemma":[0.982743,0.001247397,0.01426867,0.0007340517,0.0002357606,0.00009641215,0.0004992414,0.00005938796,0.0001160644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7270613,"threshold_uncertainty_score":0.6978341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436893609205829,"score_gpt":0.2915569930467276,"score_spread":0.2771880569546692,"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."}}