{"id":"W4413901981","doi":"10.1002/mp.18070","title":"Radiomics‐based kidney lesion classification: Mitigating batch effect with nested combat harmonization","year":2025,"lang":"en","type":"article","venue":"Medical Physics","topic":"Renal cell carcinoma treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut universitaire de cardiologie et de pneumologie de Québec; Université Laval; Centre hospitalier universitaire de Québec","funders":"Natural Sciences and Engineering Research Council of Canada; National Cancer Institute; Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données","keywords":"Radiomics; Harmonization; Artificial intelligence; Machine learning; Computer science; Feature selection; Feature (linguistics); Medical imaging; Medical physics; Pattern recognition (psychology); Radiology; 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":[0.004624831,0.001198394,0.00143703,0.0009451417,0.0006773472,0.001019375,0.00193939,0.000729414,0.001786314],"category_scores_gemma":[0.007541561,0.0004213246,0.001565319,0.0007345277,0.0009433163,0.0009453403,0.001780759,0.001194995,0.001146008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006097383,"about_ca_system_score_gemma":0.001370443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005138861,"about_ca_topic_score_gemma":0.00511793,"domain_scores_codex":[0.997954,0.0006498837,0.0001459801,0.0006576283,0.0003604775,0.0002320414],"domain_scores_gemma":[0.9979097,0.0007926738,0.0002375521,0.0004524732,0.00052565,0.00008182342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002962526,0.0009021772,0.03181054,0.0002374196,0.0007421121,0.0003635766,0.0006914053,0.2892205,0.02711919,0.002604089,0.01183902,0.6315074],"study_design_scores_gemma":[0.00009164334,0.0004162187,0.007267892,0.00001572292,0.0001098039,0.0001252231,0.0001073904,0.9736517,0.01324708,0.002254154,0.002677558,0.00003562252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.371258,0.001011097,0.6160844,0.0008030316,0.0002093385,0.000639922,0.000869752,0.006464326,0.002660055],"genre_scores_gemma":[0.8335907,0.0001705478,0.1583697,0.0004794823,0.0002029429,0.0004886086,0.003612381,0.0003940838,0.00269164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005138861,"threshold_uncertainty_score":0.02445871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461214015715319,"score_gpt":0.2844214846459707,"score_spread":0.2598093444888175,"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."}}