{"id":"W4417290288","doi":"10.1016/j.ijrobp.2025.12.005","title":"Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma","year":2025,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Lymphoma Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; Centre Hospitalier de l’Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; BC Cancer Agency","funders":"National Institute of Biomedical Imaging and Bioengineering; GE Healthcare; National Cancer Institute; National Institutes of Health; American Radium Society; U.S. Department of Defense; Society of Nuclear Medicine and Molecular Imaging; St. Baldrick's Foundation; American Society of Hematology; Children’s Oncology Group; Lymphoma Research Foundation","keywords":"Radiation therapy; Hodgkin lymphoma; Radiation treatment planning; Volume (thermodynamics); Computed tomography; Multimodality","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":[0.0005909641,0.0003156273,0.00030105,0.0005643766,0.0001850854,0.0007454828,0.0004121112,0.000422311,0.0008209136],"category_scores_gemma":[0.002104762,0.0001332972,0.0003301518,0.0003427872,0.000212861,0.0003166485,0.0004701203,0.0003473937,0.0001183953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005825445,"about_ca_system_score_gemma":0.0004363323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003218869,"about_ca_topic_score_gemma":0.003675401,"domain_scores_codex":[0.999814,0.00007699387,0.00001598859,0.00003565643,0.00004210383,0.00001540649],"domain_scores_gemma":[0.9995295,0.0003117543,0.00005621575,0.00001993368,0.00006153166,0.00002107242],"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":[0.0003207733,0.000178508,0.01364349,0.0002111307,0.0001439313,0.0003108071,0.0002711641,0.4960816,0.01123423,0.002611655,0.003130745,0.471862],"study_design_scores_gemma":[0.000009854461,0.00006031048,0.002282005,0.00001534405,0.00002646535,0.000132267,0.00004189146,0.9919727,0.0023748,0.002081506,0.0009958755,0.000006997202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4709114,0.003942068,0.5148924,0.001667964,0.0001099585,0.0001589799,0.0003987252,0.001290243,0.006628312],"genre_scores_gemma":[0.9074284,0.000465729,0.09071361,0.0001405007,0.00003385545,0.00006281125,0.0001388519,0.00004220782,0.0009740011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003218869,"threshold_uncertainty_score":0.006400228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03906739976952554,"score_gpt":0.3802946831017202,"score_spread":0.3412272833321947,"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."}}