{"id":"W4399388733","doi":"10.1117/1.jbo.29.6.065004","title":"Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens","year":2024,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Breast cancer; Lumpectomy; Hyperspectral imaging; Medicine; Cancer; Breast-conserving surgery; Mastectomy; Radiology; Mammography; Medical imaging; Pathology; Internal medicine; Artificial intelligence","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":[0.0002773662,0.0001615578,0.00008631754,0.0002631219,0.0001190447,0.0001317723,0.0001296319,0.0001841777,0.0009205723],"category_scores_gemma":[0.0004300886,0.0001697034,0.0001111663,0.0001036431,0.0002448488,0.0001621446,0.0002198276,0.0001527324,0.0001577485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001503503,"about_ca_system_score_gemma":0.000112728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007645214,"about_ca_topic_score_gemma":0.001658428,"domain_scores_codex":[0.9998802,0.00002846768,0.000007345825,0.00002960672,0.00004271874,0.00001169228],"domain_scores_gemma":[0.9998654,0.00003972719,0.00002990698,0.00002115428,0.00002698445,0.00001685878],"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":[0.0003482662,0.00008189111,0.07963104,0.0000383988,0.00001834216,0.0001583329,0.000169824,0.001800599,0.9027219,0.0001009192,0.00006932201,0.01486123],"study_design_scores_gemma":[0.0000194322,0.00085776,0.3927746,0.000009515784,0.00004899612,0.002138476,0.0004734403,0.04775874,0.5550258,0.0001727679,0.0006957761,0.00002460507],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949834,0.0000442597,0.004707909,0.00001078958,0.000001715035,0.00001175491,0.00002159748,0.00002291997,0.0001956948],"genre_scores_gemma":[0.9927149,0.00004953163,0.006806886,0.00001284462,0.000001520992,0.000009935515,0.00007897345,0.00000576627,0.0003195044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009205723,"threshold_uncertainty_score":0.003079593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01107871279686783,"score_gpt":0.3223251248320398,"score_spread":0.311246412035172,"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."}}