Hidden treasures: Incidental findings in two cases of chronic subdural hematoma.

Background: Extramedullary hematopoiesis (EMH) and plasmacytomas occurring within the cranium are rare entities. Case Description: We review two cases in which patients presented with subdural hematoma and underwent evacuation. On routine histopathologic examination of their membranes, both patients were subsequently found to have focal EMH, as well as a clonal plasma cell proliferation in one case. Conclusion: EMH is rare and usually found in individuals with profound and chronic anemia. However, this entity may be more common in chronic subdural hematomas. Solitary extraosseous plasmacytoma is exceedingly rare in the cranium, and its presence in chronic subdural hematoma membranes is of uncertain significance. The cytokine milieu that promotes organization of chronic subdural hematomas may play a role in the establishment of both of entities in this location.

Limited Number of Cases May Yield Generalizable Models, a Proof of Concept in Deep Learning for Colon Histology.

BACKGROUND: Little is known about the effect of a minimum number of slides required in generating image datasets used to build generalizable machine-learning (ML) models. In addition, the assumption within deep learning is that the increased number of training images will always enhance accuracy and that the initial validation accuracy of the models correlates well with their generalizability. In this pilot study, we have been able to test the above assumptions to gain a better understanding of such platforms, especially when data resources are limited. METHODS: Using 10 colon histology slides (5 carcinoma and 5 benign), we were able to acquire 1000 partially overlapping images (Dataset A) that were then trained and tested on three convolutional neural networks (CNNs), ResNet50, AlexNet, and SqueezeNet, to build a large number of unique models for a simple task of classifying colon histopathology into benign and malignant. Different quantities of images (10-1000) from Dataset A were used to construct >200 unique CNN models whose performances were individually assessed. The performance of these models was initially assessed using 20% of Dataset A's images (not included in the training phase) to acquire their initial validation accuracy (internal accuracy) followed by their generalization accuracy on Dataset B (a very distinct secondary test set acquired from public domain online sources). RESULTS: All CNNs showed similar peak internal accuracies (>97%) from the Dataset A test set. Peak accuracies for the external novel test set (Dataset B), an assessment of the ability to generalize, showed marked variation (ResNet50: 98%; AlexNet: 92%; and SqueezeNet: 80%). The models with the highest accuracy were not generated using the largest training sets. Further, a model's internal accuracy did not always correlate with its generalization accuracy. The results were obtained using an optimized number of cases and controls. CONCLUSIONS: Increasing the number of images in a training set does not always improve model accuracy, and significant numbers of cases may not always be needed for generalization, especially for simple tasks. Different CNNs reach peak accuracy with different training set sizes. Further studies are required to evaluate the above findings in more complex ML models prior to using such ancillary tools in clinical settings.

Educational Case: Myeloid Sarcoma: A Subtype of Acute Myeloid Leukemia.

The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http://journals.sagepub.com/doi/10.1177/2374289517715040.1.

Ovarian teratoma associated Anti-N-methyl-D-aspartate receptor encephalitis: a difficult diagnosis with a favorable prognosis

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a recently described auto-immune and paraneoplastic encephalitis with prominent neuropsychiatric manifestations affecting young adults with ovarian teratoma. The availability of a novel assay to measure these antibodies might suggest an etiology for this potentially life-threatening disease, which if early recognized can be treated promptly with surgery with chances of a good clinical outcome. Reported prognostic indicators for a good outcome depend on the presence of a tumor, prompt treatment and no admission to an intensive care unit. However, due to the rarity and unawareness of this disease, the diagnosis may be delayed as primary psychiatric disorders, and infective encephalitis is taken more into consideration and ruled out first. Here we report a case of anti-NMDAR encephalitis in a 22-year-old female prompted by an ovarian teratoma with a gradual and complete resolution of symptoms after surgical excision of the teratoma and immunomodulating therapies.

Ovarian teratoma associated Anti-N-methyl-D-aspartate receptor encephalitis: a difficult diagnosis with a favorable prognosis.

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a recently described auto-immune and paraneoplastic encephalitis with prominent neuropsychiatric manifestations affecting young adults with ovarian teratoma. The availability of a novel assay to measure these antibodies might suggest an etiology for this potentially life-threatening disease, which if early recognized can be treated promptly with surgery with chances of a good clinical outcome. Reported prognostic indicators for a good outcome depend on the presence of a tumor, prompt treatment and no admission to an intensive care unit. However, due to the rarity and unawareness of this disease, the diagnosis may be delayed as primary psychiatric disorders, and infective encephalitis is taken more into consideration and ruled out first. Here we report a case of anti-NMDAR encephalitis in a 22-year-old female prompted by an ovarian teratoma with a gradual and complete resolution of symptoms after surgical excision of the teratoma and immunomodulating therapies.