{"id":"W4366605609","doi":"10.21203/rs.3.rs-2787380/v1","title":"A Priori Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy using Quantitative Ultrasound, Texture Derivative and Molecular Subtype","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canadian Institutes of Health Research; Terry Fox Foundation","keywords":"Margin (machine learning); Breast cancer; Texture (cosmology); Ultrasound; Computer science; Derivative (finance); Support vector machine; Chemotherapy; Oncology; Cancer; Medicine; Pattern recognition (psychology); Artificial intelligence; Internal medicine; Radiology; Machine learning","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":[0.002031617,0.0004039058,0.0005128126,0.0004921476,0.0001079894,0.0005027532,0.0002347955,0.0003159519,0.0007610368],"category_scores_gemma":[0.005519381,0.000137751,0.000373946,0.0002517949,0.0002005206,0.0003305599,0.000237283,0.0003675738,0.0002957273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001942226,"about_ca_system_score_gemma":0.0002212613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007952194,"about_ca_topic_score_gemma":0.001350278,"domain_scores_codex":[0.9994671,0.0002827789,0.0000389714,0.00007847082,0.00009329864,0.00003946766],"domain_scores_gemma":[0.9973115,0.001547236,0.0004639943,0.000299689,0.0002508709,0.0001267042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002331812,0.0003137792,0.9222482,0.00007436134,0.000202962,0.00008656747,0.00007665833,0.01569761,0.01472113,0.0001013504,0.0003558101,0.0437898],"study_design_scores_gemma":[0.00005438078,0.0012056,0.8182027,0.00002499163,0.0001637706,0.0003643549,0.0001652934,0.1688855,0.009823184,0.000530112,0.0005424228,0.00003766322],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939441,0.0002344663,0.00516711,0.00005598981,0.00001058435,0.0000162388,0.0002213619,0.00003263525,0.0003175637],"genre_scores_gemma":[0.9984674,0.00003143981,0.001086754,0.00001314196,0.00000750771,0.000008794646,0.0002238412,0.000003969373,0.0001571346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002031617,"threshold_uncertainty_score":0.01074439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06000879084192533,"score_gpt":0.4362547686208418,"score_spread":0.3762459777789164,"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."}}