{"id":"W4310828474","doi":"10.1016/s2589-7500(22)00210-2","title":"Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts","year":2022,"lang":"en","type":"article","venue":"The Lancet Digital Health","topic":"Endometrial and Cervical Cancer Treatments","field":"Medicine","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre","funders":"Leids Universitair Medisch Centrum; Hanarth Fonds; KWF Kankerbestrijding; Eidgenössische Technische Hochschule Zürich; Universiteit Leiden","keywords":"Medicine; Endometrial cancer; Cohort; Receiver operating characteristic; Artificial intelligence; Oncology; Haematoxylin; Internal medicine; Cancer; Radiology; Computer science; Immunohistochemistry","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":[0.01043047,0.00091539,0.001273849,0.000868395,0.0001601926,0.0009472508,0.00116586,0.0008499702,0.001817931],"category_scores_gemma":[0.0125829,0.0003472195,0.001691269,0.0004774796,0.0003851694,0.0005614311,0.0009484365,0.001344609,0.0003704468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006344177,"about_ca_system_score_gemma":0.0007301366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001607454,"about_ca_topic_score_gemma":0.002076259,"domain_scores_codex":[0.9977427,0.001504748,0.0001190394,0.0003430245,0.000187777,0.000102769],"domain_scores_gemma":[0.9945683,0.003591019,0.0005551974,0.000665724,0.0004080262,0.0002117846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.05873441,0.001632265,0.3616563,0.001692461,0.0089693,0.0008360407,0.0002589813,0.2981628,0.01200494,0.001579851,0.02204513,0.2324275],"study_design_scores_gemma":[0.004180884,0.003928901,0.09731997,0.0001731373,0.002650783,0.0006128125,0.0001269263,0.8735219,0.00713761,0.004119693,0.006119819,0.0001076306],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500518,0.002856327,0.03385559,0.001153504,0.000145807,0.0003996954,0.009690998,0.0006054997,0.001240831],"genre_scores_gemma":[0.9674884,0.0002665559,0.01649825,0.0003220449,0.00006761838,0.0005424853,0.01373759,0.00007529282,0.001001829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01043047,"threshold_uncertainty_score":0.05516225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07055243749222065,"score_gpt":0.3829373907691396,"score_spread":0.312384953276919,"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."}}