{"id":"W4210674339","doi":"10.1016/j.jbi.2022.104006","title":"Deep neural networks for neuro-oncology: Towards patient individualized design of chemo-radiation therapy for Glioblastoma patients","year":2022,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Regimen; Computer science; Glioblastoma; Artificial intelligence; Radiation therapy; Deep learning; Personalized medicine; Artificial neural network; Medicine; Reinforcement learning; Scalability; Bioinformatics; Internal 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":[0.0007226163,0.000452312,0.0005058448,0.0002478661,0.0001728215,0.0007340736,0.0006761556,0.0006001561,0.001356899],"category_scores_gemma":[0.002457677,0.0003045796,0.0004219888,0.0002052633,0.0002530287,0.0005812615,0.0006523883,0.000963804,0.0002532645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009244103,"about_ca_system_score_gemma":0.001538226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003673331,"about_ca_topic_score_gemma":0.006452837,"domain_scores_codex":[0.9997916,0.00009442699,0.0000108624,0.00003725288,0.00004169214,0.00002423065],"domain_scores_gemma":[0.9996293,0.0001991776,0.00004507727,0.00001975448,0.00007909934,0.00002761471],"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":[0.0001536444,0.0000881425,0.002025102,0.0001049047,0.00007493545,0.00004305927,0.00004198506,0.8599895,0.004078069,0.006483698,0.003186081,0.123731],"study_design_scores_gemma":[0.000006548448,0.000024469,0.0001797833,0.000009263026,0.00001352774,0.00001229501,0.000007219242,0.9930242,0.001087648,0.005005284,0.0006256059,0.000004072765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04167625,0.001135315,0.9521517,0.00208729,0.00007747554,0.00006704347,0.0002452833,0.0004973473,0.002062289],"genre_scores_gemma":[0.8407488,0.0008229807,0.153806,0.0006572261,0.00008361198,0.0002006468,0.0003816398,0.0001613353,0.003137783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003673331,"threshold_uncertainty_score":0.007303953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03315069655158554,"score_gpt":0.3083769452929038,"score_spread":0.2752262487413183,"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."}}