{"id":"W4379380514","doi":"10.1101/2023.05.31.23290537","title":"Towards Longitudinal Glioma Segmentation: Evaluating combined pre- and post-treatment MRI training data for automated tumor segmentation using nnU-Net","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services","funders":"National Institutes of Health","keywords":"Segmentation; Convolutional neural network; Computer science; Magnetic resonance imaging; Workflow; Glioma; Artificial intelligence; Market segmentation; Medicine; Pattern recognition (psychology); Radiology; Database","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":[0.004756922,0.001647323,0.0009742188,0.001072049,0.0005412127,0.001109562,0.001283906,0.001970576,0.0008725401],"category_scores_gemma":[0.007190894,0.0005380418,0.00129979,0.0006519122,0.0007235215,0.001060522,0.001051533,0.001504684,0.000679052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001750658,"about_ca_system_score_gemma":0.00195942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02220471,"about_ca_topic_score_gemma":0.024843,"domain_scores_codex":[0.9990093,0.0003273286,0.0000738933,0.000350775,0.0001302531,0.0001085391],"domain_scores_gemma":[0.9978756,0.001034548,0.0002208017,0.0002901011,0.0004253563,0.0001536321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003298094,0.001146132,0.06109603,0.0005379474,0.001076223,0.0004071488,0.0004074522,0.6477138,0.01990595,0.0007576618,0.008199711,0.2554539],"study_design_scores_gemma":[0.00005184527,0.0003975886,0.006659766,0.00004689005,0.0001319916,0.000106787,0.00007411711,0.9810079,0.009656573,0.0006196607,0.001216938,0.00002992407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8975115,0.004241509,0.08471271,0.001260493,0.0003368731,0.0003416792,0.002966771,0.005987156,0.002641324],"genre_scores_gemma":[0.9284915,0.0006815203,0.05877287,0.0005447822,0.00007093414,0.0002458495,0.008964974,0.0004235588,0.001803931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02220471,"threshold_uncertainty_score":0.04415089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1962597979609859,"score_gpt":0.4216307078200062,"score_spread":0.2253709098590203,"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."}}