{"id":"W2794513626","doi":"10.1016/j.jmir.2017.09.006","title":"Imaging Biomarkers for Precision Medicine in Locally Advanced Breast Cancer","year":2018,"lang":"en","type":"review","venue":"Journal of medical imaging and radiation sciences","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Breast cancer; Medicine; Precision medicine; Neoadjuvant therapy; Chemotherapy; Personalized medicine; Cancer; Oncology; Breast imaging; Tumour heterogeneity; Clinical trial; Translational research; Pathological; Radiology; Biomarker; Medical physics; Internal medicine; Bioinformatics; Pathology; Mammography; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007603788,0.0003037879,0.001555281,0.001098769,0.0001819469,0.00008420969,0.0004702539,0.0001486585,0.0001199647],"category_scores_gemma":[0.004364416,0.000177404,0.0002637077,0.0007691308,0.00133323,0.0003062063,0.00006775335,0.0008076612,0.000001255853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806012,"about_ca_system_score_gemma":0.001586501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006228513,"about_ca_topic_score_gemma":0.000002675855,"domain_scores_codex":[0.9957586,0.000199258,0.001496658,0.0004388456,0.001706433,0.0004002363],"domain_scores_gemma":[0.9966551,0.001191247,0.001167674,0.0001354646,0.0002824242,0.0005680778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003970109,0.00003616217,0.0009829445,0.001636727,0.00005618214,0.00006491697,0.000116581,0.000003322612,0.000002244481,0.00001858734,0.01241115,0.9846315],"study_design_scores_gemma":[0.002943139,0.0001588268,0.0006735417,0.06065576,0.0005426393,0.004317149,0.000395739,0.03257813,4.36905e-7,0.000163231,0.897333,0.0002383663],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00009567694,0.9579747,0.004070037,0.03567355,0.001676976,0.0003695331,0.000007120968,0.00001367689,0.00011874],"genre_scores_gemma":[0.0003296761,0.9944643,0.001847598,0.001056335,0.002219915,0.00001530672,0.000008079911,0.00002865834,0.00003010233],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9843931,"threshold_uncertainty_score":0.7234327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02293012045601431,"score_gpt":0.4157764810434848,"score_spread":0.3928463605874705,"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."}}