Detection of Pulmonary Embolism: Workflow Architecture and Comparative Analysis of the CNN Models

Machine learning has proven to be a practical medical image processing technique for pattern discovery in low-quality labelled and unlabeled datasets. Deep vein thrombosis and pulmonary embolism are both examples of venous thromboembolism, which is a key factor in patient mortality and necessitates prompt diagnosis by experts. An immediate diagnosis and course of treatment are necessary for the life-threatening cardiovascular condition known as pulmonary embolism (PE). In the study of medical imaging, especially the identification of PE, machine learning (ML) algorithms have produced encouraging results. This study's objective is to assess how well machine learning (ML) algorithms perform in identifying PE in computed tomography (CT) scans. A range of ML approaches were used to the dataset, including deep learning algorithms such as convolutional neural networks. The effectiveness of PE detection systems can be greatly enhanced by the use of cutting-edge methodologies like deep learning, which lowers the possibility of incorrect diagnoses and enables the quick administration of therapy to individuals who require it. This work contributes to the growing body of evidence that supports the use of ML in medical imaging and diagnosis. Future research should examine how these algorithms might be included into clinical workflows, resolving any potential implementation challenges, and making sure their adoption is done so in a secure and efficient way. In this study, we provide a thorough evaluation of three different models: the streamlined architecture MobileNetV2 with an accuracy of 96%, compared to other models like the Xception model with an accuracy of 91%, and the Efficientnet B5 model with an accuracy of 97%, after observation and process following

Primary Bone Lymphoma: A Case Series and Review of Literature

Primary bone lymphoma (PBL) is a subtype of lymphoma that exclusively affects skeletal tissue. Despite the relatively common involvement of skeletal structures as a manifestation of non-Hodgkin’s lymphoma (NHL), primary and exclusive involvement of the skeletal system is rare. The prevalence of PBL is estimated to be 3–7% amongst primary bone tumors and less than 2% amongst all lymphomas in adults. However, the definition of primary bone lymphoma has been inconsistent over time. Within our institution, we identified four cases of primary bone lymphoma based on diagnostic criteria formed from the general consensus of multiple organizations, including the World Health Organization (WHO) and International Extranodal Lymphoma Study Group (IELSG). Here, we discuss the distinct characteristics amongst these cases in addition to performing a systematic review of current literature regarding this lymphoproliferative entity

A proteasome inhibitor, bortezomib, inhibits breast cancer growth and reduces osteolysis by downregulating metastatic genes

PURPOSE: The incidence of bone metastasis in advanced breast cancer (BrCa) exceeds 70%. Bortezomib, a proteasome inhibitor used for the treatment of multiple myeloma, also promotes bone formation. We tested the hypothesis that proteasome inhibitors can ameliorate BrCa osteolytic disease. EXPERIMENTAL DESIGN: To address the potentially beneficial effect of bortezomib in reducing tumor growth in the skeleton and counteracting bone osteolysis, human MDA-MB-231 BrCa cells were injected into the tibia of mice to model bone tumor growth for in vivo assessment of treatment regimens before and after tumor growth. RESULTS: Controls exhibited tumor growth, destroying trabecular and cortical bone and invading muscle. Bortezomib treatment initiated following inoculation of tumor cells strikingly reduced tumor growth, restricted tumor cells mainly to the marrow cavity, and almost completely inhibited osteolysis in the bone microenvironment over a 3- to 4-week period as shown by [(18)F]fluorodeoxyglucose positron emission tomography, micro-computed tomography scanning, radiography, and histology. Thus, proteasome inhibition is effective in killing tumor cells within the bone. Pretreatment with bortezomib for 3 weeks before inoculation of tumor cells was also effective in reducing osteolysis. Our in vitro and in vivo studies indicate that mechanisms by which bortezomib inhibits tumor growth and reduces osteolysis result from inhibited cell proliferation, necrosis, and decreased expression of factors that promote BrCa tumor progression in bone. CONCLUSION: These findings provide a basis for a novel strategy to treat patients with BrCa osteolytic lesions, and represent an approach for protecting the entire skeleton from metastatic bone disease