{"id":"W4408654066","doi":"10.1117/12.3041415","title":"Development of a high-throughput wide-field imaging robotic system for assessing margin status based on cancer biomarkers derived from Raman spectroscopy with preliminary validation in lumpectomy specimens from breast-conserving surgery","year":2025,"lang":"en","type":"article","venue":"","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lumpectomy; Throughput; Breast cancer; Margin (machine learning); Raman spectroscopy; Surgical margin; Cancer; Medical physics; Oncology; Medicine; Computer science; Mastectomy; Internal medicine; Optics; Machine learning; Physics; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133879,0.0003716473,0.0003425074,0.0003202378,0.0002441241,0.0004485,0.0006857495,0.0004846765,0.000877526],"category_scores_gemma":[0.0007049116,0.0002610076,0.000326386,0.000151892,0.000243761,0.0004398581,0.0004239462,0.0003447201,0.0004120529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003141186,"about_ca_system_score_gemma":0.0005173413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007446781,"about_ca_topic_score_gemma":0.00106108,"domain_scores_codex":[0.9995704,0.00006030011,0.00001883335,0.0001408198,0.0001779288,0.00003176632],"domain_scores_gemma":[0.9996872,0.00007402184,0.00005321022,0.0000519728,0.0001020802,0.00003154048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001221349,0.0001131244,0.001826885,0.0001033168,0.00002292995,0.00007167725,0.0000679197,0.0008543092,0.9726897,0.0002051087,0.0004244155,0.0234985],"study_design_scores_gemma":[0.00004301754,0.001891847,0.01737599,0.00002030916,0.00007203918,0.0008577195,0.00008742067,0.03381331,0.9385133,0.0001963393,0.007043509,0.00008531368],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.608385,0.001124917,0.3829721,0.0004464946,0.0001710748,0.001041849,0.0007890275,0.002960265,0.002109272],"genre_scores_gemma":[0.5945758,0.0005274699,0.3998789,0.0003099628,0.0000363634,0.0006730036,0.0005942267,0.00009580881,0.003308403],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001133879,"threshold_uncertainty_score":0.005996585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01062044398186324,"score_gpt":0.3027818862758712,"score_spread":0.2921614422940079,"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."}}