{"id":"W4413998229","doi":"10.1002/mp.18078","title":"Feature level quantitative ultrasound and CT information fusion to predict the outcome of head &amp; neck cancer radiotherapy treatment: Enhanced principal component analysis","year":2025,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Terry Fox Research Institute","keywords":"Principal component analysis; Artificial intelligence; Radiation therapy; Support vector machine; Pattern recognition (psychology); Feature (linguistics); Medical imaging; Computer science; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002522038,0.0008143915,0.0008372994,0.00199605,0.0002473286,0.0009357521,0.0004520603,0.0005521525,0.0009214389],"category_scores_gemma":[0.004163462,0.0001960547,0.00120277,0.001121025,0.000320251,0.0004777079,0.0005778871,0.000671608,0.00023028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004377117,"about_ca_system_score_gemma":0.0007363581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0026786,"about_ca_topic_score_gemma":0.001725391,"domain_scores_codex":[0.9990415,0.0003180155,0.00006894282,0.000207345,0.0002627401,0.0001014478],"domain_scores_gemma":[0.9987387,0.0005151804,0.0001652386,0.00010774,0.0004099225,0.00006318277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002579065,0.001074641,0.1180499,0.0004300745,0.001162063,0.0003219887,0.0002030321,0.1964839,0.06971987,0.001180302,0.005084415,0.6037107],"study_design_scores_gemma":[0.00003831162,0.0006383203,0.06599113,0.0000361514,0.0003563225,0.000206426,0.00006073296,0.9144844,0.01601252,0.0009763214,0.001135439,0.000063871],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6589656,0.002539325,0.333141,0.0005696827,0.0001824116,0.0003154443,0.001139881,0.001452055,0.001694393],"genre_scores_gemma":[0.961478,0.0002272297,0.03696482,0.00005604449,0.00005148737,0.0001067536,0.0006553218,0.00003160612,0.000428682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0026786,"threshold_uncertainty_score":0.01333797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02740263954068578,"score_gpt":0.3691947812038415,"score_spread":0.3417921416631557,"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."}}