{"id":"W4403055402","doi":"10.1088/2057-1976/ad8201","title":"Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information","year":2024,"lang":"en","type":"article","venue":"Biomedical Physics & Engineering Express","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Guelph General Hospital; University of Ottawa; University of Guelph","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Radiomics; Prostate cancer; Radiation therapy; Pathological; Medicine; Artificial intelligence; Medical physics; Cancer; Computer science; Radiology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00187721,0.0008211139,0.0007879959,0.002022065,0.0002961781,0.0008877508,0.0006463018,0.0007366637,0.000518348],"category_scores_gemma":[0.002728553,0.000221977,0.001242414,0.000771308,0.000340787,0.0005704935,0.0006327991,0.0006367927,0.0002816992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009182163,"about_ca_system_score_gemma":0.0008975031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005837595,"about_ca_topic_score_gemma":0.005369025,"domain_scores_codex":[0.9995372,0.0001164041,0.00003920646,0.0001401211,0.00009677502,0.00007036493],"domain_scores_gemma":[0.999065,0.0003769406,0.0001976679,0.00008239994,0.000212143,0.00006589889],"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":[0.00161781,0.0006741761,0.2577623,0.0001331983,0.0006746993,0.0003851583,0.0002073954,0.3485581,0.01729097,0.0003453282,0.001274032,0.3710769],"study_design_scores_gemma":[0.00001891051,0.0003105722,0.03185346,0.00001857003,0.0001805601,0.0001805813,0.00004222028,0.9626846,0.003755044,0.0006672862,0.0002542208,0.00003413138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8944016,0.0008465717,0.1022721,0.0003005879,0.00003563795,0.00006752287,0.0003239704,0.000717384,0.00103477],"genre_scores_gemma":[0.986639,0.0000949168,0.01269867,0.00003328174,0.0000148827,0.00002679037,0.0002874769,0.00001105638,0.0001938374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005837595,"threshold_uncertainty_score":0.01160723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922426926035153,"score_gpt":0.2808961662478223,"score_spread":0.2616718969874708,"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."}}