Intravascular large B-cell lymphoma (IVLBCL) is a rare lymphoma that affects the brain, skin, and bone marrow. We describe the case of a 75-year-old man who was admitted to the hospital after 4 h of stomach aches. A thorough physical examination indicated stomach discomfort and skin discoloration. Laboratory tests revealed thrombocytopenia and elevated lactate dehydrogenase levels. A computed tomography scan of the abdomen revealed that the small intestine wall was thickened, edematous, and necrotic. The necrotic small bowel was surgically removed, revealing many little round, homogenous, and unusual cells in the mesenteric vein. In-situ hybridization revealed that these cells were positive for PAX5, CD20, CD79a, CD10, and BCL2, as well as Epstein-Barr virus-encoded small RNA. After 1 week of hospitalization without treatment, the patient was diagnosed with IVLBCL and died of multiple organ dysfunction syndrome. IVLBCL is a rare illness that affects the small intestine and possibly the gastrointestinal system. It has an insidious start, a fast development, and a dismal prognosis. Knowing its clinicopathologic traits helps in understanding the illness, making an early diagnosis, and preventing rapid worsening.

The advent of whole-slide imaging, faster image data generation, and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples. In parallel, with continuous breakthroughs in object detection, image feature extraction, image classification and image segmentation, artificial intelligence (AI) is becoming the most beneficial technology for high-throughput analysis of image data in various biomedical imaging disciplines. Integrating digital images into biological workflows, advanced algorithms, and computer vision techniques expands the biologist\'s horizons beyond the microscope slide. Here, we introduce recent developments in AI applied to microscopy in hematopathology. We give an overview of its concepts and present its applications in normal or abnormal hematopoietic cells identification. We discuss how AI shows great potential to push the limits of microscopy and enhance the resolution, signal and information content of acquired data. Its shortcomings are discussed, as well as future directions for the field.

Hematopoietic disorders are serious diseases that threaten human health, and the diagnosis of these diseases is essential for treatment. However, traditional diagnosis methods rely on manual operation, which is time consuming and laborious, and examining entire slide is challenging. In this study, we developed a weakly supervised deep learning method for diagnosing malignant hematological diseases requiring only slide-level labels. The method improves efficiency by converting whole-slide image (WSI) patches into low-dimensional feature representations. Then the patch-level features of each WSI are aggregated into slide-level representations by an attention-based network. The model provides final diagnostic predictions based on these slide-level representations. By applying the proposed model to our collection of bone marrow WSIs at different magnifications, we found that an area under the receiver operating characteristic curve of 0.966 on an independent test set can be obtained at 10× magnification. Moreover, the performance on microscopy images can achieve an average accuracy of 94.2% on two publicly available datasets. In conclusion, we have developed a novel method that can achieve fast and accurate diagnosis in different scenarios of hematological disorders.

OBJECTIVE: To report the demonstration of double minutes with MYC amplification in a case of myeloproliferative neoplasm with monocytosis in transformation by a combination of standard karyotyping and interphase and metaphase fluorescence in situ hybridization (FISH).
METHODS: To determine the lineage involvement, we applied combined morphology and an interphase FISH study using dual-color break-apart probes for MYC on peripheral blood film.
RESULTS: MYC amplification was demonstrated in both myeloid and monocytic cells but not lymphocytes. The MYC amplification was not associated with loss of MYC signals at the homologous 8q24 regions where the genes were located. Furthermore, the extent of MYC amplification has been shown to diminish as the granulocytes mature.
CONCLUSIONS: Combined morphology and FISH study has shown a pluripotent myeloid disorder and also an inverse relationship between cell maturity and MYC amplification.