A Multiplex Test Assessing MiR663ame and VIMme in Urine Accurately Discriminates Bladder Cancer from Inflammatory Conditions

Bladder cancer (BlCa) is a common malignancy with significant morbidity and mortality. Current diagnostic methods are invasive and costly, showing the need for newer biomarkers. Although several epigenetic-based biomarkers have been proposed, their ability to discriminate BlCa from common benign conditions of the urinary tract, especially inflammatory diseases, has not been adequately explored. Herein, we sought to determine whether VIMme and miR663ame might accurately discriminate those two conditions, using a multiplex test. Performance of VIMme and miR663ame in tissue samples and urines in testing set confirmed previous results (96.3% sensitivity, 88.2% specificity, area under de curve (AUC) 0.98 and 92.6% sensitivity, 75% specificity, AUC 0.83, respectively). In the validation sets, VIMme-miR663ame multiplex test in urine discriminated BlCa patients from healthy donors or patients with inflammatory conditions, with 87% sensitivity, 86% specificity and 80% sensitivity, 75% specificity, respectively. Furthermore, positive likelihood ratio (LR) of 2.41 and negative LR of 0.21 were also disclosed. Compared to urinary cytology, VIMme-miR663ame multiplex panel correctly detected 87% of the analysed cases, whereas cytology only forecasted 41%. Furthermore, high miR663ame independently predicted worse clinical outcome, especially in patients with invasive BlCa. We concluded that the implementation of this panel might better stratify patients for confirmatory, invasive examinations, ultimately improving the cost-effectiveness of BlCa diagnosis and management. Moreover, miR663ame analysis might provide relevant information for patient monitoring, identifying patients at higher risk for cancer progression

Surgical approach of a dentigerous cyst in regard to a clinical case

Poster apresentado no XXIII Congresso da Ordem dos Médicos Dentistas. Porto, 6-8 de Novembro de 2014A 54-year-old, came to the Dental Service at the Armed Forces Hospital, complaining of pain and discomfort with the use of prosthesis. After clinical examination was requested was requested one Orthopantomography. With this complementary diagnostic exam was found the inclusion of teeth # 44 and # 45 and a radiolucent lesion involving the # 48 tooth of approximately 3cm, well-defined, unilocular, compatible with a cystic lesion. After surgical treatment, the histological diagnosis was dentigerous cyst. The dentigerous cyst is formed from the accumulation of fluid between the the reduced enamel epithelium and the crown of an unrupted tooth. It is the second most common type of odontogenic cyst with an occurrence of about 24% compared to all maxillary and mandibular cysts. It is seen more frequently associated with mandibular third molars, maxillary canines and maxillary third molars. It appear mostly as unilateral cysts, unilocular and asymptomatic episodes of acute pain occurring when there is secondary infection. Complementary exams for diagnosis are important both for planning issues either for reasons of identification of potential clinical and pathological situations adjacent to the reason for patient consultation. Our patient complained of pain at the level of pre-molars, because the prosthesis was traumatizing the inclusion zone. After request of panoramic radiography was found a suggestive radiological image of a dentigerous cyst, assymptomatic, but with considerable dimensions.info:eu-repo/semantics/publishedVersio

iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images

Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets