RESUMEN
OBJECTIVES: To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time. METHODS: A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or "continuous BI-RADS 100". Cohen's kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed. RESULTS: On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528-0.571) without AI and κ = 0.626, 95% CI (0.607-0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754). CONCLUSIONS: When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.
Asunto(s)
Neoplasias de la Mama , Femenino , Humanos , Inteligencia Artificial , Neoplasias de la Mama/diagnóstico por imagen , Neoplasias de la Mama/patología , Detección Precoz del Cáncer , Mamografía/métodos , Variaciones Dependientes del Observador , Estudios CruzadosRESUMEN
PURPOSE: Most transplant centers use chemoembolisation as locoregional bridge therapy for hepatocellular carcinoma (HCC) before liver transplantation (LT). Chemoembolisation using beads loaded with doxorubicin (DEBDOX) is a promising technique that enables delivery of a large quantity of drugs against HCC. We sought to assess the imaging-histologic correlation after DEBDOX chemoembolisation. MATERIALS AND METHODS: All consecutive patients who had undergone DEBDOX chemoembolisation before receiving liver graft for HCC were included. Tumour response was evaluated according to Response Evaluation Criteria in Solid Tumours (RECIST) and modified RECIST (mRECIST) criteria. The result of final imaging made before LT was correlated with histological data to predict tumour necrosis. RESULTS: Twenty-eight patients underwent 43 DEBDOX procedures for 45 HCC. Therapy had a significant effect as shown by a decrease in the mean size of the largest nodule (p = 0.02) and the sum of viable part of tumour sizes according to mRECIST criteria (p < 0.001). An objective response using mRECIST criteria was significantly correlated with mean tumour necrosis ≥90 % (p = 0.03). A complete response using mRECIST criteria enabled accurate prediction of complete tumour necrosis (p = 0.01). Correlations using RECIST criteria were not significant. CONCLUSION: Our data confirm the potential benefit of DEBDOX chemoembolisation as bridge therapy before LT, and they provide a rational basis for new studies focusing on recurrence-free survival after LT. Radiologic evaluation according to mRECIST criteria enables accurate prediction of tumour necrosis, whereas RECIST criteria do not.