{"id":"W2972785816","doi":"10.1016/j.ijrobp.2019.06.2183","title":"Validation of Deep Learning-based Auto-Segmentation for Organs at Risk and Gross Tumor Volumes in Lung Stereotactic Body Radiotherapy","year":2019,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kelowna General Hospital; Saskatchewan Cancer Agency","funders":"","keywords":"Medicine; Contouring; Segmentation; Deep learning; Hausdorff distance; Nuclear medicine; Radiation treatment planning; Sørensen–Dice coefficient; Convolutional neural network; Lung cancer; Radiology; Radiation therapy; Artificial intelligence; Image segmentation; Computer science; Internal medicine","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":[0.002184809,0.0009849651,0.0008397699,0.001369291,0.0004465152,0.001067627,0.001477608,0.001771008,0.001019782],"category_scores_gemma":[0.004168615,0.000502633,0.0009430521,0.0005886239,0.0006001619,0.0006011706,0.001100315,0.0008486503,0.000677795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188282,"about_ca_system_score_gemma":0.001543923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0134312,"about_ca_topic_score_gemma":0.01377209,"domain_scores_codex":[0.9991838,0.0001590111,0.00005914781,0.0002851729,0.0002089948,0.0001037264],"domain_scores_gemma":[0.998018,0.000868066,0.0002067702,0.0002193974,0.0005845785,0.0001031552],"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":[0.001401016,0.0005615525,0.038388,0.0004106072,0.0004776967,0.0001621576,0.0001882286,0.6195803,0.03529189,0.0007485971,0.005277097,0.2975129],"study_design_scores_gemma":[0.00002164765,0.0001088924,0.00605594,0.00002439048,0.0000411853,0.00007987117,0.00002326605,0.9830621,0.00986219,0.0002925302,0.0004159721,0.0000119744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8713881,0.001697698,0.1135184,0.0003243795,0.0002196392,0.0002081785,0.002261274,0.00741519,0.002967052],"genre_scores_gemma":[0.9710821,0.0001669404,0.02391879,0.0001109536,0.00001751479,0.00005480604,0.003127266,0.0002637216,0.001257973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0134312,"threshold_uncertainty_score":0.02670604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006896681206317647,"score_gpt":0.3107736448640083,"score_spread":0.3038769636576907,"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."}}