{"id":"W4408164091","doi":"10.1007/s10278-025-01458-x","title":"Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review","year":2025,"lang":"en","type":"review","venue":"Journal of Imaging Informatics in Medicine","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Research (Canada); BC Cancer Agency; University of British Columbia","funders":"","keywords":"Lung cancer; Computed tomography; Segmentation; Deep learning; Artificial intelligence; Medicine; Cancer; Radiology; Cartography; Computer science; Geography; Oncology; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001221915,0.0004062229,0.004821824,0.00078132,0.00003600521,0.00001811054,0.0002499821,0.00006221202,0.00008309201],"category_scores_gemma":[0.0007327005,0.0002463662,0.0003239624,0.0008008608,0.00003847305,0.0001264414,0.00006803036,0.0007183486,0.000002002539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106808,"about_ca_system_score_gemma":0.0003996563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003879248,"about_ca_topic_score_gemma":0.000007110812,"domain_scores_codex":[0.9946303,0.0001307495,0.004058535,0.000129376,0.0008054147,0.0002456135],"domain_scores_gemma":[0.9945708,0.0006587981,0.003864863,0.0003014961,0.0004299948,0.0001740839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0000182133,0.00009122258,0.002368117,0.8183132,0.000872204,0.00002001743,0.0009071938,0.0002503085,2.33976e-8,0.000003470703,0.004174022,0.172982],"study_design_scores_gemma":[0.00210453,0.000164506,0.00006145093,0.9744102,0.01615496,0.00003734216,0.0005479452,0.0009007651,3.958245e-7,0.000001882913,0.005451944,0.0001640931],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002517431,0.9945166,0.001293886,0.0001918568,0.0004872121,0.00281473,0.00001056399,0.000009216378,0.0006508321],"genre_scores_gemma":[0.0002887273,0.9969154,0.0009991511,0.001129884,0.0002111456,0.0002409906,0.0001661801,0.00002878168,0.0000197161],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1728179,"threshold_uncertainty_score":0.9999989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009753398908370554,"score_gpt":0.3437758202408474,"score_spread":0.3340224213324768,"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."}}