Transformation to diffuse large B-cell lymphoma with germinal center B-cell like subtype and discordant light chain expression in a patient with Waldenström macroglobulinemia/lymphoplasmacytic lymphoma

Clonally related transformation from Waldenström macroglobulinemia to diffuse large B-cell lymphoma with central nervous system involvement at diagnosis: a case report and literature review

Histological transformation to diffuse large B-cell lymphoma (DLBCL) rarely occurs in patients with Waldenström macroglobulinemia (WM). The median time from WM diagnosis to DLBCL is 4-5 years. Extranodal involvement is common in transformed WM. However, central nervous system (CNS) involvement is relatively uncommon. Here, we report a case of a simultaneous diagnosis of WM and clonally related DLBCL, with the involvement of CNS demonstrated by dual enhancement in MRI. Nevertheless, it is unclear if CNS infiltration is caused by DLBCL or WM for the inaccessibility of brain biopsy. Intensified chemotherapy and Bruton tyrosine kinase inhibitor were administrated, and a good response was achieved.Please check the edit made in the article title.we have checked it.

Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas

Malignant lymphoma is one of the most severe types of disease that leads to death as a result of exposure of lymphocytes to malignant tumors. The transformation of cells from indolent B-cell lymphoma to B-cell lymphoma (DBCL) is life-threatening. Biopsies taken from the patient are the gold standard for lymphoma analysis. Glass slides under a microscope are converted into whole slide images (WSI) to be analyzed by AI techniques through biomedical image processing. Because of the multiplicity of types of malignant lymphomas, manual diagnosis by pathologists is difficult, tedious, and subject to disagreement among physicians. The importance of artificial intelligence (AI) in the early diagnosis of malignant lymphoma is significant and has revolutionized the field of oncology. The use of AI in the early diagnosis of malignant lymphoma offers numerous benefits, including improved accuracy, faster diagnosis, and risk stratification. This study developed several strategies based on hybrid systems to analyze histopathological images of malignant lymphomas. For all proposed models, the images and extraction of malignant lymphocytes were optimized by the gradient vector flow (GVF) algorithm. The first strategy for diagnosing malignant lymphoma images relied on a hybrid system between three types of deep learning (DL) networks, XGBoost algorithms, and decision tree (DT) algorithms based on the GVF algorithm. The second strategy for diagnosing malignant lymphoma images was based on fusing the features of the MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models and classifying them by XGBoost and DT algorithms based on the ant colony optimization (ACO) algorithm. The color, shape, and texture features, which are called handcrafted features, were extracted by four traditional feature extraction algorithms. Because of the similarity in the biological characteristics of early-stage malignant lymphomas, the features of the fused MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models were combined with the handcrafted features and classified by the XGBoost and DT algorithms based on the ACO algorithm. We concluded that the performance of the two networks XGBoost and DT, with fused features between DL networks and handcrafted, achieved the best performance. The XGBoost network based on the fused features of MobileNet-VGG16 and handcrafted features resulted in an AUC of 99.43%, accuracy of 99.8%, precision of 99.77%, sensitivity of 99.7%, and specificity of 99.8%. This highlights the significant role of AI in the early diagnosis of malignant lymphoma, offering improved accuracy, expedited diagnosis, and enhanced risk stratification. This study highlights leveraging AI techniques and biomedical image processing; the analysis of whole slide images (WSI) converted from biopsies allows for improved accuracy, faster diagnosis, and risk stratification. The developed strategies based on hybrid systems, combining deep learning networks, XGBoost and decision tree algorithms, demonstrated promising results in diagnosing malignant lymphoma images. Furthermore, the fusion of handcrafted features with features extracted from DL networks enhanced the performance of the classification models.

Occurrence of MYD88L265P and CD79B mutations in diffuse large b cell lymphoma with bone marrow infiltration: A case report

BACKGROUND

Over the past 20 years, we have gained a deep understanding of the biological heterogeneity of diffuse large B cell lymphoma (DLBCL) and have developed a range of new treatment programs based on the characteristics of the disease, bringing us to the era of immune-chemotherapy. However, the effectiveness and molecular mechanisms of targeted-immunotherapy remain unclear in DLBCL. Targeted-immunotherapy may be beneficial for specific subgroups of patients, thus requiring biomarker assessment.

CASE SUMMARY

Here, we report a case of MCD subtype DLBCL with MYD88L265P and CD79B mutations, considered in the initial stage as lymphoplasmic lymphoma (LPL) or Waldenstrom macroglobulinemia (WM). Flow cytometry supported this view; however, the immunohistochemical results of the lymph nodes overturned the above diagnosis, and the patient was eventually diagnosed with MCD subtype DLBCL. The presence of a monoclonal IgM component in the serum and infiltration of small lymphocytes with a phenotype compatible with WM into the bone marrow led us to propose a hypothesis that the case we report may have transformed from LPL/WM.

CONCLUSION

This highlights the possible transformation from WM to DLBCL, CD79B mutation may be a potential biomarker for predicting this conversion.