{"id":"W4414557714","doi":"10.1016/j.ijrobp.2025.06.2512","title":"Artificial Intelligence and Radiomic-Based Predictors of Response to Neoadjuvant SABR in Early-Stage Breast Cancer: Insights from the SPORT-DS Trial","year":2025,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Hôpital Maisonneuve-Rosemont; McGill University Health Centre; McGill University","funders":"Varian Medical Systems","keywords":"SABR volatility model; Correlation; Feature (linguistics); Breast cancer; Intraclass correlation; Clinical trial","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":[0.002612536,0.0005805901,0.001010184,0.0002030477,0.0001693097,0.001018662,0.000389179,0.0005792701,0.001624368],"category_scores_gemma":[0.005659941,0.0001190585,0.001123597,0.0003978153,0.0005319329,0.0005654554,0.0004025215,0.001172258,0.0003243165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004253885,"about_ca_system_score_gemma":0.0005241887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001026711,"about_ca_topic_score_gemma":0.001328037,"domain_scores_codex":[0.9991356,0.0006144389,0.0000464018,0.00007621446,0.00006922558,0.00005818464],"domain_scores_gemma":[0.997239,0.001754186,0.0004187116,0.0002382593,0.0001407368,0.0002090447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.3701443,0.005421056,0.4100752,0.0008551623,0.01080141,0.0001490163,0.0003411621,0.02107211,0.004529349,0.001890382,0.005488214,0.1692327],"study_design_scores_gemma":[0.04250804,0.07418263,0.7810442,0.0003970375,0.01231597,0.0005467605,0.0007589559,0.0455718,0.003960015,0.01612271,0.02238799,0.0002038489],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943855,0.001786398,0.0004427799,0.0008812937,0.00006376337,0.00005134595,0.0006305985,0.00001145517,0.001746961],"genre_scores_gemma":[0.9966255,0.0006678738,0.0004296586,0.0002640703,0.0001266289,0.00005285864,0.001247074,0.0000081002,0.0005781774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002612536,"threshold_uncertainty_score":0.01381654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666151961282412,"score_gpt":0.3417182683629784,"score_spread":0.3250567487501542,"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."}}