RESUMEN
Background Despite the increasing use of contrast-enhanced mammography (CEM), there are limited data on the evaluation of findings on recombined images and the association with malignancy. Purpose To determine the rates of malignancy of enhancement findings on CEM images in the presence or absence of low-energy findings using the Breast Imaging Reporting and Data System (BI-RADS) lexicon developed for mammography and MRI. Materials and Methods All diagnostic CEM examinations performed at one academic institution between December 2015 and December 2019 had low-energy and recombined images retrospectively. Data were independently reviewed by three breast imaging radiologists with 5-25 years of experience using the BI-RADS mammography and MRI lexicon. Outcome was determined with pathologic analysis or 1-year imaging or clinical follow-up. The χ2 and Fisher exact tests were used for analysis. Results A total of 371 diagnostic CEM studies were performed in 371 women (mean age, 54 years ± 11[SD]). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value of enhancement on CEM images was 95% (104 of 109 [95% CI: 90, 98]), 67% (176 of 262 [95% CI: 61, 73]), 55% (104 of 190 [95% CI: 47, 62]), and 97% (176 of 181 [95% CI: 94, 99]), respectively. Among 190 CEM studies with enhancing findings, enhancing lesions were more likely to be malignant when associated with low-energy findings (26% vs 59%, P < .001). Among enhancement types, mass enhancement composed 71% (99 of 140) of all malignancies with PPV of 63% when associated with low-energy findings. Foci, non-mass enhancement, and mass enhancement without low-energy findings had PPV of 6%, 24%, and 38%, respectively. Neither background parenchymal enhancement nor density was associated with enhancement type (P = .19 and P = .28, respectively). Conclusion Mass enhancement on recombined images using CEM was most commonly associated with malignancy, especially when associated with low-energy findings. Enhancement types were more likely to be benign when not associated with low-energy findings; however, they should still be viewed with suspicion, given the high association with malignancy. © RSNA, 2022 Online supplemental material is available for this article.
Asunto(s)
Neoplasias de la Mama , Neoplasias , Humanos , Femenino , Persona de Mediana Edad , Estudios Retrospectivos , Mamografía/métodos , Imagen por Resonancia Magnética/métodos , Valor Predictivo de las Pruebas , Neoplasias de la Mama/diagnóstico por imagenRESUMEN
BACKGROUND: Body composition is associated with mortality; however its routine assessment is too time-consuming. PURPOSE: To demonstrate the value of artificial intelligence (AI) to extract body composition measures from routine studies, we aimed to develop a fully automated AI approach to measure fat and muscles masses, to validate its clinical discriminatory value, and to provide the code, training data and workflow solutions to facilitate its integration into local practice. METHODS: We developed a neural network that quantified the tissue components at the L3 vertebral body level using data from the Liver Tumor Challenge (LiTS) and a pancreatic cancer cohort. We classified sarcopenia using accepted skeletal muscle index cut-offs and visceral fat based its median value. We used Kaplan Meier curves and Cox regression analysis to assess the association between these measures and mortality. RESULTS: Applying the algorithm trained on LiTS data to the local cohort yielded good agreement [>0.8 intraclass correlation (ICC)]; when trained on both datasets, it had excellent agreement (>0.9 ICC). The pancreatic cancer cohort had 136 patients (mean age: 67 ± 11 years; 54% women); 15% had sarcopenia; mean visceral fat was 142 cm2. Concurrent with prior research, we found a significant association between sarcopenia and mortality [mean survival of 15 ± 12 vs. 22 ± 12 (p < 0.05), adjusted HR of 1.58 (95% CI: 1.03-3.33)] but no association between visceral fat and mortality. The detector analysis took 1 ± 0.5 s. CONCLUSIONS: AI body composition analysis can provide meaningful imaging biomarkers from routine exams demonstrating AI's ability to further enhance the clinical value of radiology reports.