Percutaneous Metallic Stents in Malignant Biliary Obstruction: Comparison of Nitinol and Wall Stents.

INTRODUCTION: Palliation of malign biliary obstruction is important which is commonly carried out by percutaneous biliary stenting. Our primary aim with this study was assessment of performance of wall stents, and nitinol stents for the palliation of malign biliary obstruction. METHODS: The medical records of 157 patients who underwent biliary stenting in our department between January 1, 1995, and December 31, 2005, were retrospectively analyzed. Technical success, treatment success, mortality in the first 30 days, minor, and major complications were evaluated and compared among the wall stent, and the nitinol stent groups in all patients which constituted the primary study endpoints. Additionally, stent patency, and mean patient survival times after stent implantation were evaluated in patients for whom follow-up information could be obtained. RESULTS: A total of 213 metallic stents were placed in 157 patients. Wall stent was placed in 83 of the patients with mean age, and SD of 60.4 and 13.5. Nitinol stent was placed in 74 of the patients with mean age of 57.8, and SD of 15.5. Gender ratio was equal in both groups. Biliary stent dysfunction was observed in 13 patients in each of nitinol, and wall stent groups throughout the study period. There was no statistical difference among re-occlusion rates (p = 0.91). For the nitinol stent group median primary patency time was 119 days (90-185 days CI 95%), and for the wall stent group median primary patency time was 81 days (60-150 days CI 95%). CONCLUSION: Nitinol stents, and wall stents are safe options that can be safely used in the percutaneous treatment of malignant biliary obstruction with similar treatment and therapeutic success, low complication rates, and patency times that can extend beyond expected survival times.

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.

Artificial Intelligence in Stroke Imaging: A Comprehensive Review.

The aging population challenges the health-care system with chronic diseases. Cerebrovascular diseases are important components of these chronic conditions. Stroke is the acute cessation of blood in the brain, which can lead to rapid tissue loss. Therefore, fast, accurate, and reliable automatic methods are required to facilitate stroke management. The performance of artificial intelligence (AI) methods is increasing in all domains. Vision tasks, including natural images and medical images, are particularly benefiting from the skills of AI models. The AI methods that can be applied to stroke imaging have a broad range, including classical machine learning tools such as support vector machines, random forests, logistic regression, and linear discriminant analysis, as well as deep learning models, such as convolutional neural networks, recurrent neural networks, autoencoders, and U-Net. Both tools can be applied to various aspects of stroke management, including time-to-event onset determination, stroke confirmation, large vessel occlusion detection, difusion restriction, perfusion deficit, core and penumbra identification, afected region segmentation, and functional outcome prediction. While building these AI models, maximum care should be exercised in order to reduce bias and build generalizable models. One of the most important prerequisites for building unbiased models is collecting large, diverse, and quality data that reflects the underlying population well and splitting the training and testing parts in a way that both represent a similar distribution. Explainability and trustworthiness are other important properties of machine learning models that could be widely adopted in clinical practices.