{"id":"W4416780876","doi":"10.1016/j.esmorw.2025.100548","title":"352P Pathomic embeddings improve prediction of clinically actionable breast cancer related mutations from whole slide images","year":2025,"lang":"en","type":"article","venue":"ESMO Real World Data and Digital Oncology","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Breast cancer; Mutation; Cancer; Feature (linguistics)","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":[0.0003579007,0.001149156,0.0005556224,0.001369536,0.0003073183,0.0006408299,0.0005505962,0.00118046,0.005673423],"category_scores_gemma":[0.002850773,0.0002470208,0.0007037508,0.0007742823,0.000289235,0.0009457483,0.001048003,0.001074603,0.002338045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003565557,"about_ca_system_score_gemma":0.0005462278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004615067,"about_ca_topic_score_gemma":0.006565736,"domain_scores_codex":[0.9997701,0.00003773097,0.00001035008,0.0001028268,0.00004075966,0.00003824059],"domain_scores_gemma":[0.9994048,0.0003394654,0.00004378651,0.00006704977,0.0001069563,0.00003784806],"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":[0.002833836,0.000773854,0.06808835,0.001233803,0.0006280987,0.001590959,0.0001985069,0.1261887,0.04912522,0.005319809,0.09621345,0.6478055],"study_design_scores_gemma":[0.0001912818,0.0003945192,0.02566329,0.000184325,0.0003870505,0.001111739,0.0002454203,0.9137682,0.01415965,0.02465638,0.01917451,0.00006356333],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7506512,0.007345148,0.1611366,0.004284144,0.001437147,0.0002309416,0.04659926,0.01624593,0.0120696],"genre_scores_gemma":[0.9341813,0.001054742,0.03161834,0.0006369689,0.0002305673,0.0001141588,0.02662408,0.0004172085,0.005122656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005673423,"threshold_uncertainty_score":0.01897943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01837691542244549,"score_gpt":0.3267523096492561,"score_spread":0.3083753942268107,"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."}}