{"id":"W2121202398","doi":"10.6000/1929-6029.2014.03.04.2","title":"Application of Survival Tree Based on Texture Features Obtained through MRI of Patients with Brain Metastases from Breast Cancer","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Covariate; Proportional hazards model; Medicine; Breast cancer; Magnetic resonance imaging; Survival analysis; Cancer; Wavelet; Radiology; Statistics; Artificial intelligence; Internal medicine; Mathematics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007895537,0.0002723529,0.0004413572,0.001132944,0.0002033692,0.0004072865,0.0001471679,0.0002383735,0.0008330569],"category_scores_gemma":[0.004534265,0.0000950639,0.0005391964,0.0008632836,0.0001213605,0.0004095147,0.0002728621,0.0002661991,0.0001579943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001991592,"about_ca_system_score_gemma":0.0003456429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00190988,"about_ca_topic_score_gemma":0.001937046,"domain_scores_codex":[0.9996862,0.0001340314,0.000025293,0.00004916066,0.00006381713,0.00004142973],"domain_scores_gemma":[0.9984323,0.0009153482,0.0002692601,0.00008225389,0.0002010432,0.00009977107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001718297,0.0001230467,0.8545226,0.00009283346,0.0002096045,0.000407981,0.0002517058,0.03357221,0.005536704,0.0003621683,0.0008180775,0.1023847],"study_design_scores_gemma":[0.00004592399,0.001111869,0.6574276,0.00003421583,0.0002555941,0.000873305,0.0003270998,0.3336128,0.002683727,0.001932282,0.00164736,0.00004823384],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784006,0.0002652518,0.02002368,0.0001122883,0.00001621805,0.00002237558,0.0008007835,0.00007277951,0.0002859555],"genre_scores_gemma":[0.9949698,0.00008795696,0.004016527,0.000007372204,0.00001144575,0.00001472869,0.0008034199,0.000006297108,0.00008251302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00190988,"threshold_uncertainty_score":0.004175603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531179280662305,"score_gpt":0.3985411081200439,"score_spread":0.3832293153134209,"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."}}