Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.

PURPOSE: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions. MATERIALS AND METHODS: Retrospective analysis of DWI images of 218 consecutive acute anterior or posterior ischemic stroke patients examined between January 2017 to April 2020 in a single center was carried out. Each stroke area was defined as distinct segmentation target with different class labels. U-Net based network was trained followed by majority voting of the voxel wise predictions of the model to transform them into patient level stroke territory classes. Effects of bias field correction and registration to a common space were explored. RESULTS: Of the 218 patients included in this study, 141 (65%) were anterior stroke, and 77 were posterior stroke (35%) whereas 117 (53%) were male and 101 (47%) were female. The model built with original images reached 0.77 accuracy, while the model built with N4 bias corrected images reached 0.80 and the model built with images which were N4 bias corrected and then registered into a common space reached 0.83 accuracy values. CONCLUSION: Voxel wise dense prediction coupled with bias field correction to eliminate artificial signal increase and registration to a common space help models for better performance than using original images. Knowing the properties of target domain while designing deep learning models is important for the overall success of these models.

Radiomics in differential diagnosis of Wilms tumor and neuroblastoma with adrenal location in children.

OBJECTIVES: Machine learning methods can be applied successfully to various medical imaging tasks. Our aim with this study was to build a robust classifier using radiomics and clinical data for preoperative diagnosis of Wilms tumor (WT) or neuroblastoma (NB) in pediatric abdominal CT. MATERIAL AND METHODS: This is a single-center retrospective study approved by the Institutional Ethical Board. CT scans of consecutive patients diagnosed with WT or NB admitted to our hospital from January 2005 to December 2021 were evaluated. Three distinct datasets based on clinical centers and CT machines were curated. Robust, non-redundant, high variance, and relevant radiomics features were selected using data science methods. Clinically relevant variables were integrated into the final model. Dice score for similarity of tumor ROI, Cohen's kappa for interobserver agreement among observers, and AUC for model selection were used. RESULTS: A total of 147 patients, including 90 WT (mean age 34.78 SD: 22.06 months; 43 male) and 57 NB (mean age 23.77 SD:22.56 months; 31 male), were analyzed. After binarization at 24 months cut-off, there was no statistically significant difference between the two groups for age (p = .07) and gender (p = .54). CT clinic radiomics combined model achieved an F1 score of 0.94, 0.93 accuracy, and an AUC 0.96. CONCLUSION: In conclusion, the CT-based clinic-radiologic-radiomics combined model could noninvasively predict WT or NB preoperatively. Notably, that model correctly predicted two patients, which none of the radiologists could correctly predict. This model may serve as a noninvasive preoperative predictor of NB/WT differentiation in CT, which should be further validated in large prospective models. CLINICAL RELEVANCE STATEMENT: CT-based clinic-radiologic-radiomics combined model could noninvasively predict Wilms tumor or neuroblastoma preoperatively. KEY POINTS: • CT radiomics features can predict Wilms tumor or neuroblastoma from abdominal CT preoperatively. • Integrating clinic variables may further improve the performance of the model. • The performance of the combined model is equal to or greater than human readers, depending on the lesion size.

Rarer Components of a Rare Disease: Accessory Hemidiaphragm and Horseshoe Lung with Scimitar Syndrome; Two Case Reports.

INTRODUCTION: Scimitar syndrome is a rare developmental anomaly with an incidence of 2/100.000 births. Major components of this disease are partial anomalous pulmonary venous drainage, pulmonary hypoplasia, systemic arterialization of the right basal lung, and dextroposition of the heart. Horseshoe lung and accessory hemidiaphragm are two rarer components of this disease. CASE PRESENTATION: In this paper, horseshoe lung and accessory diaphragm associated with Scimitar syndrome have been reported in two cases. CONCLUSION: In conclusion, being aware of rare manifestations of rare diseases is important to fully describe the pathologic spectrum of the disease. This will assist in better management and decision-making process.

Detection and vascular territorial classification of stroke on diffusion-weighted MRI by deep learning.

PURPOSE: Rapid detection and vascular territorial classification of stroke enable the determination of the most appropriate treatment. In this study, we aimed to investigate the performance of convolutional neural network (CNN) models in the detection and vascular territorial classification of stroke on diffusion-weighted images (DWI). METHODS: DWI of 421 cases (271 acute ischemic stroke patients and 150 cases without any ischemia findings on DWI) obtained between January 2017 to April 2020 were reviewed. We created two custom datasets. A stroke detection dataset was created with 1800 slices (900 S and 900 normal) consisting of 1400 for training, 200 for validation, 200 for test. A vascular territorial type dataset was created with 1717 slices (883 middle cerebral artery stroke, 416 posterior circulatory stroke, and 418 watershed stroke) consisting of 1117 slices for training, 300 for validation, 300 for test. A transfer learning approach based on MobileNetV2 and EfficientNet-B0 CNN architecture was used. The performance of the models was evaluated. RESULTS: Modified MobileNetV2 and EfficientNet-B0 models achieved 96% (κ: 0.92) and 93% (κ: 0.86) accuracy in stroke detection, respectively. In vascular territorial classification of stroke as middle cerebral artery, posterior circulation, or watershed infarction, an accuracy of 93% (κ: 0.895) was achieved with modified MobileNetV2 model and 87% (κ: 0.805) with modified EfficientNet-B0 CNN model. CONCLUSION: Transfer learning approach with custom top CNN models achieve sufficiently high performance for both the detection of ischemic stroke and the classification of its vascular territorial type on DWI.

Evaluation of development of subclinical atherosclerosis in children with uveitis.

Uveitis is a chronic inflammatory disease. Chronic inflammation has been shown to have a role in pathogenesis of atherosclerosis. Atherosclerosis is the most important risk factor of cardiovascular diseases and is shown to start as early as childhood. In this study, we investigated the presence of subclinical atherosclerosis in children with uveitis. Seventy five patients who were diagnosed as having uveitis in ophthalmology and pediatric rheumatology clinics were included in the study. Patients with hypertension, obesity, dyslipidemia, diabetes, and with history of early cardiovascular disease were excluded. Arterial stiffness, carotid-femoral pulse wave velocity (PWV), augmentation index (AIx), and carotid artery intima-media thickness (cIMT) were measured for each patient. These measurements were compared with 50 healthy children with similar age and sex as controls. The mean age of patients in this study was 12.24 ± 2.69 years, and the mean age of controls was 11.32 ± 4.52 years. PWV and AIx values were higher in the patient group (p = 0.04, p = 0.03). cIMT levels were not different in patient and control groups. When patients were grouped as having uveitis for more than 5 years or not, patients with longer duration of uveitis had higher PWV, AIx, and cIMT levels (p values were 0.01, 0.02, and 0.04 respectively). Vascular functions deteriorate first with endothelial damage in children with uveitis and as disease continues, increase in cIMT is added. We think that for follow-up of the disease and evaluation of the treatment, non-invasive subclinical atherosclerosis markers should be used along with activation criteria of primary diseases.