Hemophagocytic lymphohistiocytosis secondary to diffuse large B-cell lymphoma presenting with recurrent multi-territory infarcts: A case report.

Lymphoma-associated hemophagocytic syndrome is a life-threatening disease with poor prognosis and may present as ischemic stroke. We report a case of a 56-year-old female with recurrent multi-territory infarcts caused by diffuse large B-cell lymphoma with secondary hemophagocytic lymphohistiocytosis. She had been diagnosed with ischemic stroke and hemophagocytic syndrome probably secondary to Epstein-Barr virus infection 3 months previously and treated with Dexamethasone and Aspirin. High resolution vessel wall magnetic resonance imaging showed vessel wall thickening at some intracranial vessels suggesting vasculitis. Abdominal computed tomography scan revealed splenomegaly, multiple bilateral small nodules of the lung, multiple liver lesions, multiple bilateral renal masses, gastric wall thickening and multiple nodules in the omentum. Cerebrospinal fluid cytology showed increased cerebrospinal-fluid protein level. Hemophagocytosis was showed on bone marrow aspirate cytology. Gastric tissue biopsy revealed large B cell lymphoma. Chemotherapy was not given because the patient had severe pneumonia and sepsis. The patient died 28 days after the definitive diagnosis was confirmed. Ischemic stroke in our patient with diffuse large B-cell lymphoma may be due to vasculitis or intravascular large B-cell lymphoma.

Role of diffusion-weighted MRI in differentiation between benign and malignant anterior mediastinal masses.

Diffusion-weighted imaging (DWI) is considered to be a useful biomarker to characterize the cellularity of lesions, yet its application in the thorax to evaluate anterior mediastinal lesions has not been well investigated. The aims of our study were to describe the magnetic resonance (MR) characteristics of anterior mediastinal masses and to assess the role of apparent diffusion coefficient (ADC) value in distinguishing benign from malignant lesions of the anterior mediastinum. We conducted a retrospective cross-sectional study including 55 patients with anterior mediastinal masses who underwent preinterventional MR scanning with the following sequences: T1 VIBE DIXON pre and post-contrast, T2 HASTE, T2 TIRM, DWI-ADC map (b values of 0 and 2000 sec/mm2). The ADC measurements were obtained by two approaches: hot-spot ROI and whole-tumor histogram analysis. The lesions were grouped by three distinct ways: benign versus malignant, group A (benign lesions and type A, AB, B1 thymoma) versus group B (type B2, B3 thymoma and other malignant lesions), lymphoma versus other malignancies. The study was composed of 55 patients, with 5 benign lesions and 50 malignant lesions. The ADCmean, ADCmedian, ADC10, ADC90 in the histogram-based approach and the hot-spot-ROI-based mean ADC of the malignant lesions were significantly lower than those of benign lesions (P values< 0.05). The hot-spot-ROI-based mean ADC had the highest value in differentiation between benign and malignant mediastinal lesions, as well as between group A and group B; the ADC cutoffs (with sensitivity, specificity) to differentiate malignant from benign lesions and group A from group B were 1.17 x 10-3 mm2/sec (80%, 80%) and 0.99 x 10-3 mm2/sec (78.4%, 88.9%), respectively. The ADC values obtained by using the hot-spot-ROI-based and the histogram-based approaches are helpful in differentiating benign and malignant anterior mediastinal masses.

Correlation of Chest X-Ray Scores in SARS-CoV-2 Patients With the Clinical Severity Classification and the Quick COVID-19 Severity Index.

Objectives This study aimed to assess the role of chest X-ray (CXR) scoring methods and their correlations with the clinical severity categories and the Quick COVID-19 Severity Index (qCSI). Methods We conducted a retrospective study of 159 COVID-19 patients who were diagnosed and treated at the University Medical Center between July and September 2021. Chest X-ray findings were evaluated, and severity scores were calculated using the modified CXR (mCXR), Radiographic Assessment of Lung Edema (RALE), and Brixia scoring systems. The three scores were then compared to the clinical severity categories and the qCSI using Spearman's correlation coefficient. Results Overall, 159 patients (63 males and 96 females) (mean age: 58.3 ± 15.7 years) were included. The correlation coefficients between the mCXR score and the Brixia and RALE scores were 0.9438 and 0.9450, respectively. The correlation coefficient between the RALE and Brixia scores was marginally higher, at 0.9625. The correlation coefficients between the qCSI and the Brixia, RALE, and mCXR scores were 0.7298, 0.7408, and 0.7156, respectively. The significant difference in the mean values of the three CXR scores between asymptomatic, mild, moderate, severe, and critical groups was also noted. Conclusions There were strong correlations between the three CXR scores and the clinical severity classification and the qCSI.

Deep Learning Model With Convolutional Neural Network for Detecting and Segmenting Hepatocellular Carcinoma in CT: A Preliminary Study.

Introduction Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world. Early detection and accurate diagnosis of HCC play an important role in patient management. This study aimed to develop a convolutional neural network-based model to identify and segment HCC lesions utilizing dynamic contrast agent-enhanced computed tomography (CT). Methods This retrospective study used CT image sets of histopathology-confirmed hepatocellular carcinoma over three phases (arterial, venous, and delayed). The proposed convolutional neural network (CNN) segmentation method was based on the U-Net architecture and trained using the domain adaptation technique. The proposed method was evaluated using 115 liver masses of 110 patients (87 men and 23 women; mean age, 56.9 years ± 11.9 (SD); mean mass size, 6.0 cm ± 3.6). The sensitivity for identifying HCC of the model and Dice score for segmentation of liver masses between radiologists and the CNN model were calculated for the test set. Results The sensitivity for HCC identification of the model was 100%. The median Dice score for HCC segmenting between radiologists and the CNN model was 0.81 for the test set. Conclusion Deep learning with CNN had high performance in the identification and segmentation of HCC on dynamic CT.

Central Hyperthyroidism due to Thyroid-Stimulating Hormone-Secreting Pituitary Microadenoma in an Adolescent Boy: Case Report and Review of the Literature.

Thyroid-stimulating hormone- (TSH-) secreting pituitary adenoma (TSH-oma) is a rare cause of secondary hyperthyroidism and can be misdiagnosed as primary hyperthyroidism. We report a case of a 15-year-old male patient who was one of two monozygotic twins and exhibited hyperthyroidism syndrome. The laboratory results showed secondary hyperthyroidism, with increased levels of free T3 (FT3) and free T4 (FT4) and no TSH inhibition. Magnetic resonance imaging (MRI) and histopathological examination of the pituitary gland confirmed pituitary microadenoma. The patient was treated with methimazole, propranolol, and somatostatin analogs to restore euthyroidism before undergoing an endoscopic transsphenoidal resection of the pituitary tumor. After surgery, the hyperthyroidism symptoms improved, thyroid hormones normalized, and MRI of the pituitary gland showed the complete removal of the tumor with no recurrence after 2 years of follow-up.

Differentiation Between Surgical and Nonsurgical Intussusception: A Diagnostic Model Using Multi-Detector Computed Tomography.

BACKGROUND: The differentiation of surgical from nonsurgical adult intussusception may enable the appropriate selection of management strategies. OBJECTIVE: This study aimed to investigate the diagnostic potential of multidetector computed tomography (MCDT) features to differentiate surgical from nonsurgical adult intussusception and develop a diagnostic model. METHODS: A retrospective study was performed on 96 patients with intussusceptions at the University Medical Center Hospital between January 2014 and January 2020. Two radiologists reviewed all images, and intussusception characteristics were documented. The location of intussusception, length, diameter, interposed fat thickness, lead point, and complications were evaluated. Based on the results, a diagnostic tree model was developed to differentiate between surgical and nonsurgical adult intussusception. RESULTS: A total of 99 intussusceptions in 96 patients (mean age: 53.0 ± 16.5 years), including 35 (35.3%) enteroenteric, 27 (27.3%) enterocolic, and 37 (37.4%) colocolic lesions, were evaluated. Of the enteroenteric intussusceptions, 22 (62.9%) were surgical, including 19 (79.2%) with lead points. Among colon intussusceptions, 63 (98.4%) were surgical, and 100% had lead points. The characteristics used to predict surgical intussusceptions included lead point presence, length ≥ 5.0 cm, diameter ≥ 3.2 cm, interposed fat thickness ≥ 0.5 cm, and complications (p < 0.001). Based on these features, we established a diagnostic tree model that correctly classified 96 (97%) of 99 lesions. CONCLUSION: Our study reinforces the importance of MDCT for the diagnosis and guided management of adult intussusceptions. The characteristics that predicted surgical intussusceptions included lead points, length, diameter, interposed fat thickness, and complications. A systematic approach using this diagnostic tree model could be used to distinguish surgical and nonsurgical adult intussusception.