RESUMEN
Upper gastrointestinal hemorrhage represents a common symptom in the internal medical practice. This Case Report depicts a severe gastrointestinal bleeding from an angiosarcoma that required multiple blood transfusion. As shown in this case, the actual cause may initially remain unclear. Repeated tissue sampling is mandatory to confirm a diagnosis and provide adequate treatment. Primary gastrointestinal angiosarcomas are extremely rare and highly aggressive malignant tumors that tend to grow multifocally and metastasise early. The only curative treatment option is radical surgery, thus an early stage diagnosis is crucial. Palliative care for metastatic angiosarkoma is currently a topic for clinical studies.
Asunto(s)
Transfusión Sanguínea , Hemorragia Gastrointestinal , Hemangiosarcoma , Humanos , Masculino , Persona de Mediana EdadRESUMEN
INTRODUCTION: Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design. METHODS: A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model. RESULTS: Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model. CONCLUSIONS: Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy.