{"id":"W3043602140","doi":"10.1136/gutjnl-2019-319866","title":"Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning","year":2020,"lang":"en","type":"article","venue":"Gut","topic":"AI in cancer detection","field":"Computer Science","cited_by":272,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"NIHR Oxford Biomedical Research Centre; Medical Research Council; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Wellcome Trust; Academy of Medical Sciences; Biomedical Research Council; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research; Cancer Research UK; National Science Foundation","keywords":"Colorectal cancer; Random forest; Grading (engineering); Medicine; Tumour heterogeneity; Precision medicine; Gene expression profiling; Pathology; Oncology; Internal medicine; Artificial intelligence; Computational biology; Bioinformatics; Cancer; Biology; Gene expression; Gene; Computer science; Genetics","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":[0.0008602414,0.0005046141,0.0004058898,0.001048367,0.0001514034,0.0004617581,0.0007188398,0.0005297051,0.0007144389],"category_scores_gemma":[0.001801271,0.0001512443,0.000403764,0.0004323868,0.00019946,0.0003764444,0.0006270419,0.0004963363,0.000310138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006142878,"about_ca_system_score_gemma":0.0005595933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004310024,"about_ca_topic_score_gemma":0.00753729,"domain_scores_codex":[0.9997066,0.00006404273,0.00001815316,0.00009783958,0.00006530138,0.00004810359],"domain_scores_gemma":[0.9995466,0.0001232241,0.00009884319,0.00005500933,0.0001331559,0.00004314578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00158382,0.0005400889,0.2978027,0.0002955862,0.0004563217,0.0003258314,0.0001626855,0.1169325,0.04814943,0.001161624,0.008693961,0.5238954],"study_design_scores_gemma":[0.0000448242,0.0002221848,0.0384638,0.00002800175,0.000080972,0.0001840379,0.00008066025,0.9467049,0.01169905,0.001541881,0.0009299227,0.00001976468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8996065,0.000899556,0.09432666,0.0003819813,0.00004295899,0.0001317038,0.001548563,0.001076736,0.001985343],"genre_scores_gemma":[0.9674264,0.0001034331,0.02953151,0.0001034351,0.0000189264,0.00006598204,0.001913866,0.00003077631,0.0008056585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004310024,"threshold_uncertainty_score":0.008569837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03426763760177132,"score_gpt":0.2807247083307751,"score_spread":0.2464570707290037,"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."}}