Case report : aplastic anaemia and gray matter heterotopias in an autopsy of a 17-year-old puerperal woman

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Case report: Aplastic anaemia and gray matter heterotopias in an autopsy of a 17-year-old puerperal woman

In this report, we present an autopsy case of a 17-year-old girl with aplastic anaemia in the puerperal context, along with the presence of four independent nodular gray matter heterotopias in brain slices. The case is remarkable both by the rareness of the cause of death – septicaemia resulting from immunodepression due to aplastic anaemia in the obstetric context – and the brain morphologic findings unrelated to any known clinical manifestation. Immunohistochemistry was performed in order to ensure the precision of the diagnoses.

Detecting and grading prostate cancer in radical prostatectomy specimens through deep learning techniques

OBJECTIVES: This study aims to evaluate the ability of deep learning algorithms to detect and grade prostate cancer (PCa) in radical prostatectomy specimens. METHODS: We selected 12 whole-slide images of radical prostatectomy specimens. These images were divided into patches, and then, analyzed and annotated. The annotated areas were categorized as follows: stroma, normal glands, and Gleason patterns 3, 4, and 5. Two analyses were performed: i) a categorical image classification method that labels each image as benign or as Gleason 3, Gleason 4, or Gleason 5, and ii) a scanning method in which distinct areas representative of benign and different Gleason patterns are delineated and labeled separately by a pathologist. The Inception v3 Convolutional Neural Network architecture was used in categorical model training, and a Mask Region-based Convolutional Neural Network was used to train the scanning method. After training, we selected three new whole-slide images that were not used during the training to evaluate the model as our test dataset. The analysis results of the images using deep learning algorithms were compared with those obtained by the pathologists. RESULTS: In the categorical classification method, the trained model obtained a validation accuracy of 94.1% during training; however, the concordance with our expert uropathologists in the test dataset was only 44%. With the image-scanning method, our model demonstrated a validation accuracy of 91.2%. When the test images were used, the concordance between the deep learning method and uropathologists was 89%. CONCLUSION: Deep learning algorithms have a high potential for use in the diagnosis and grading of PCa. Scanning methods are likely to be superior to simple classification methods

Case report: Aplastic anaemia and gray matter heterotopias in an autopsy of a 17-year-old puerperal woman

In this report, we present an autopsy case of a 17-year-old girl with aplastic anaemia in the puerperal context, along with the presence of four independent nodular gray matter heterotopias in brain slices. The case is remarkable both by the rareness of the cause of death – septicaemia resulting from immunodepression due to aplastic anaemia in the obstetric context – and the brain morphologic findings unrelated to any known clinical manifestation. Immunohistochemistry was performed in order to ensure the precision of the diagnoses

Additional file 1 of The Brazilian national prospective active surveillance (AS) cohort of patients with low-risk prostate cancer in the public health system: vigiaSUS study protocol

Supplementary Material