{"id":"W4306938191","doi":"10.3390/cancers14205133","title":"Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis","year":2022,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto; York University","funders":"Terry Fox Foundation; Lotte and John Hecht Memorial Foundation","keywords":"Radiomics; Segmentation; Medicine; Radiation therapy; Dice; Brain metastasis; Magnetic resonance imaging; Radiology; Computer science; Metastasis; Artificial intelligence; Cancer; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005088308,0.0007889884,0.0007359963,0.00121803,0.0003072766,0.001456474,0.00040464,0.00080507,0.000576515],"category_scores_gemma":[0.02407941,0.0002817795,0.0006431993,0.0004877381,0.0005469965,0.000826766,0.0005817519,0.000519347,0.0003080237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005695231,"about_ca_system_score_gemma":0.0004037008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002266824,"about_ca_topic_score_gemma":0.002156366,"domain_scores_codex":[0.9971504,0.001418644,0.000238207,0.0005361667,0.0005318317,0.0001248384],"domain_scores_gemma":[0.990187,0.006246752,0.001490277,0.0008926618,0.001044284,0.0001390283],"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":[0.006068586,0.0004288769,0.2772639,0.0007711108,0.00131482,0.0004814893,0.0004276132,0.2539557,0.09054668,0.001358432,0.003166466,0.3642163],"study_design_scores_gemma":[0.00007727082,0.001293995,0.1810167,0.0001207179,0.0005294995,0.0008053962,0.0001381983,0.723112,0.08861376,0.001901572,0.002259589,0.0001313113],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9555918,0.003354868,0.03739717,0.0003685972,0.0001142564,0.00007225328,0.0006338951,0.0006979616,0.00176905],"genre_scores_gemma":[0.9919122,0.0002182886,0.007096346,0.00004207593,0.00002179449,0.0000169818,0.0004825877,0.00003846502,0.0001712661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005088308,"threshold_uncertainty_score":0.02690989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03162233176784656,"score_gpt":0.4022878320709581,"score_spread":0.3706655003031116,"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."}}