Pancreas Ductal Adenocarcinoma and its Mimics: Review of Cross-sectional Imaging Findings for Differential Diagnosis

Ductal adenocarcinoma is the most common pancreatic neoplasm. A variety of pancreatic lesions mimic pancreas ductal adenocarcinoma (PDAC), such as high-grade neuroendocrine tumors, small solid pseudopapillary tumors, metastases, focal autoimmune pancreatitis, and groove pancreatitis. These occasionally look similar in images, but they have differential diagnosis points. Familiarity with the imaging features of PDAC and its mimics is paramount for correct diagnosis and management of patients. In this essay, we describe imaging findings of PDAC and its mimics for differential diagnosis

Remarkable Effect of Gefitinib Retreatment in a Lung Cancer Patient With Lepidic Predominat Adenocarcinoma who had Experienced Favorable Results From Initial Treatment With Gefitinib: A Case Report

Gefitnib is an oral agent of epidermal growth factor receptor tyrosine kinase inhibitor, and it has a certain efficacy against non-small cell lung cancer. There are some reports that the non-small cell lung cancer patients who experienced disease progression after responding to gefitinib were again sensitive to re-administration of gefitinib following temporary cessation of gefitinib. This is the case report showing a remarkable effect of gefitinib re-treatment in a patient with metastatic invasive adenocarinoma who had experienced favorable results from the initial treatment with gefitinib

Deep-Learning-Based Coronary Artery Calcium Detection from CT Image

One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible