{"id":"W4407868563","doi":"10.1101/2025.02.19.635300","title":"Artificial intelligence-enabled automated analysis of transmission electron micrographs to evaluate chemotherapy impact on mitochondrial morphology in triple negative breast cancer","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Triple-negative breast cancer; Electron micrographs; Micrograph; Morphology (biology); Transmission electron microscopy; Chemotherapy; Breast cancer; Materials science; Cancer; Oncology; Biology; Internal medicine; Medicine; Nanotechnology; Physics; Electron microscope; Scanning electron microscope; Optics; Composite material; Zoology","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":[0.0006121172,0.0006481162,0.0004116159,0.0007189501,0.0002547318,0.0007191422,0.0006047702,0.0007516547,0.0007189569],"category_scores_gemma":[0.001733369,0.0002413982,0.0006609288,0.000347081,0.0002846802,0.0003010351,0.0003677282,0.000464105,0.0001900049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008552069,"about_ca_system_score_gemma":0.0005472362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01243153,"about_ca_topic_score_gemma":0.01055214,"domain_scores_codex":[0.9998267,0.00003858367,0.00001222429,0.00006022797,0.0000369927,0.00002530789],"domain_scores_gemma":[0.9994569,0.000267317,0.00006091579,0.0000420914,0.0001463987,0.00002632081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002844877,0.0001821504,0.01108923,0.0001138014,0.0001074514,0.0002260253,0.0001091982,0.8334793,0.0215386,0.0004689879,0.001206695,0.131194],"study_design_scores_gemma":[0.000002409648,0.0000288643,0.001302759,0.000003293247,0.000006488441,0.00002131458,0.000009027606,0.996238,0.002131688,0.0001468072,0.0001062502,0.000002962906],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8193314,0.0005976173,0.1736345,0.0003435994,0.00005985798,0.0001373543,0.00053424,0.00287044,0.00249113],"genre_scores_gemma":[0.9294422,0.0001905993,0.06767403,0.0001045294,0.00001385368,0.0000763202,0.0009942467,0.00007468055,0.001429538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01243153,"threshold_uncertainty_score":0.02471834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116025031917119,"score_gpt":0.3170246130433978,"score_spread":0.3054221098516859,"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."}}