{"id":"W4391295742","doi":"10.1038/s41598-024-52858-y","title":"Transfer learning of pre-treatment quantitative ultrasound multi-parametric images for the prediction of breast cancer response to neoadjuvant chemotherapy","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"AI in cancer detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; Health Sciences Centre; Toronto Metropolitan University; University of Toronto; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Terry Fox Foundation","keywords":"Breast cancer; Medicine; Neoadjuvant therapy; Mammography; Transfer of learning; Radiology; Cancer; Computer science; Artificial intelligence; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001759673,0.0001404505,0.0001942576,0.0003788823,0.0002144939,0.000225069,0.0002337017,0.00004541329,0.00001763216],"category_scores_gemma":[0.0001684883,0.00009531221,0.0001548195,0.001745996,0.0001799786,0.0004123407,0.00002545295,0.00008137337,0.000001533007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002415897,"about_ca_system_score_gemma":0.0003455575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002598058,"about_ca_topic_score_gemma":0.00002041623,"domain_scores_codex":[0.9980745,0.0001013509,0.000472509,0.0006967378,0.0004431915,0.0002117553],"domain_scores_gemma":[0.9981499,0.0007349485,0.0001353033,0.0006005943,0.000323387,0.00005587234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005511317,0.0001591424,0.00129826,0.0001108232,0.0001990991,0.00001572827,0.01057907,0.04616035,0.8252061,0.0001488213,0.001359779,0.1142117],"study_design_scores_gemma":[0.0004574819,0.0009239078,0.03997586,0.0002672083,0.0000820436,0.0002264114,0.0003842756,0.1216836,0.8181711,0.0004972536,0.01713073,0.0002000846],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3789214,0.0008982218,0.6140404,0.0006549293,0.004486762,0.000840947,0.00005319195,0.00009692425,0.000007210696],"genre_scores_gemma":[0.9890085,0.00007781701,0.009171631,0.00001000126,0.00003687465,0.0002441672,0.000003162769,0.00001618426,0.001431624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6100872,"threshold_uncertainty_score":0.3886719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02455903099549455,"score_gpt":0.313528389494522,"score_spread":0.2889693584990274,"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."}}