{"id":"W7139526579","doi":"","title":"Signals in the Spread: Quantitative Imaging and Tumor Heterogeneity in Metastatic Cancer","year":2025,"lang":"","type":"dissertation","venue":"TSpace (University of Toronto)","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":"Radiomics; Medical imaging; Benchmarking; Imaging biomarker; Feature (linguistics); Biomarker; Cancer; Sarcoma","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":[0.002797399,0.0005097409,0.0004300962,0.001438212,0.0002229323,0.001583392,0.0005020648,0.0005993879,0.0005755545],"category_scores_gemma":[0.008894747,0.0002562643,0.0004971263,0.001000377,0.001035933,0.001509183,0.0009334587,0.0009026182,0.0001149511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007763322,"about_ca_system_score_gemma":0.0004439413,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001779985,"about_ca_topic_score_gemma":0.001315747,"domain_scores_codex":[0.9993647,0.0002521922,0.00002668857,0.0001447746,0.0001701152,0.00004152992],"domain_scores_gemma":[0.9975173,0.001527263,0.0004500485,0.0001979481,0.0002184979,0.00008895213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001147328,0.0001857046,0.1178016,0.000773665,0.000452234,0.0005555094,0.001293372,0.4520713,0.142707,0.03590625,0.002118563,0.2449875],"study_design_scores_gemma":[0.00004419107,0.0005291559,0.08001312,0.0001231735,0.0001753611,0.0009952539,0.0005203523,0.8361189,0.0333029,0.04252607,0.005523879,0.0001276866],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6061569,0.005093851,0.3828817,0.001391832,0.00005514296,0.0001006512,0.0005764739,0.0004579976,0.003285409],"genre_scores_gemma":[0.9772893,0.0005460428,0.02135339,0.00007318943,0.00004337199,0.00004376939,0.000219945,0.00005641484,0.000374585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.99822,"threshold_uncertainty_score":0.01479423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01995169804855246,"score_gpt":0.3320235202228615,"score_spread":0.312071822174309,"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."}}