{"id":"W4385349335","doi":"10.1007/s10489-023-04844-6","title":"A texture-based method for predicting molecular markers and survival outcome in lower grade glioma","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Specific Research Project of Guangxi for Research Bases and Talents; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Random forest; Principal component analysis; Overfitting; Pattern recognition (psychology); Radiomics; Classifier (UML); Glioma; Artificial neural network; Medicine; Cancer research","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":[0.0003963977,0.0003104015,0.0005453905,0.002143125,0.0001916662,0.0009100209,0.0003983452,0.0005263624,0.0008983048],"category_scores_gemma":[0.001482734,0.0001330909,0.0004939671,0.001292719,0.0001774089,0.0002962953,0.0003834446,0.0003835213,0.0003238966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003181594,"about_ca_system_score_gemma":0.0003878815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003590049,"about_ca_topic_score_gemma":0.003196956,"domain_scores_codex":[0.9998474,0.00002588982,0.00001336622,0.00003005197,0.00005641594,0.00002684032],"domain_scores_gemma":[0.9996426,0.0001249887,0.00005989226,0.00002445348,0.0001132994,0.00003462291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001477207,0.0002500993,0.08070078,0.0002220438,0.0002517864,0.0004176416,0.0000926245,0.05944669,0.06755511,0.001299764,0.005206115,0.7830801],"study_design_scores_gemma":[0.00006128082,0.0002135075,0.04897239,0.00002985174,0.0001709178,0.0006928112,0.00008778333,0.9329796,0.01260785,0.001623302,0.002506623,0.00005412931],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5707737,0.002122467,0.4202146,0.0006424094,0.0002309013,0.0001081201,0.002019397,0.00130699,0.002581277],"genre_scores_gemma":[0.9408342,0.0005126045,0.05576334,0.0001061464,0.0001442847,0.00007235706,0.0009541214,0.00006443168,0.001548644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003590049,"threshold_uncertainty_score":0.007138312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02462618237790694,"score_gpt":0.3534482330142754,"score_spread":0.3288220506363685,"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."}}