{"id":"W4319020042","doi":"10.3389/fonc.2023.898854","title":"A radiomic biomarker for prognosis of resected colorectal cancer liver metastases generalizes across MRI contrast agents","year":2023,"lang":"en","type":"article","venue":"Frontiers in Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa; University of Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Medicine; Cohort; Colorectal cancer; Logistic regression; Retrospective cohort study; Biomarker; Magnetic resonance imaging; Radiology; Cancer; Oncology; Nuclear medicine; 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":[0.0008261292,0.0001654834,0.0007452093,0.0003197874,0.00007669457,0.00001028157,0.0001610682,0.0001793858,0.00006745001],"category_scores_gemma":[0.000734564,0.0001445346,0.0001555313,0.0005896245,0.0003726099,0.00004704351,0.00006907738,0.000244393,0.000003098584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002944158,"about_ca_system_score_gemma":0.0002595218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000478621,"about_ca_topic_score_gemma":0.0001241222,"domain_scores_codex":[0.9983344,0.0001811572,0.0004400894,0.0003072188,0.0001887839,0.0005483195],"domain_scores_gemma":[0.9991726,0.0002716233,0.0001740988,0.0001298619,0.0001112056,0.0001405664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.002475426,0.000357919,0.3857693,0.0003105203,0.0007779418,0.0002630122,0.002037519,0.0002466386,0.02514936,0.00003467768,0.4397147,0.1428629],"study_design_scores_gemma":[0.01883007,0.00162021,0.463242,0.0003247197,0.0005236145,0.00009631586,0.001761999,0.2977343,0.01067716,0.0002199415,0.2045347,0.0004349745],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880163,0.001841438,0.003775229,0.003087011,0.00182227,0.001075381,0.0001812353,0.00007837446,0.0001227352],"genre_scores_gemma":[0.9711592,0.002397881,0.02397642,0.0006611824,0.0002440523,0.0007183409,0.0002506064,0.00005853082,0.0005338187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2974877,"threshold_uncertainty_score":0.589395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0357614394481388,"score_gpt":0.3816494517805568,"score_spread":0.345888012332418,"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."}}