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Background: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC). Purpose: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel–Lindau (VHL)‐associated ccRCC, aiming to improve non‐invasive diagnosis and personalized treatment. Study Type: Retrospective analysis of a prospectively maintained cohort. Population: One hundred and thirty‐six patients, 84 (61%) males and 52 (39%) females with pathology‐proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023. Field Strength and Sequences: 1.5 and 3 T MRIs. Segmentations were performed on the T1‐weighted 3‐minute delayed sequence and then registered on pre‐contrast, T1‐weighted arterial and venous sequences. Assessment: A total of 404 lesions, 345 low‐grade tumors, and 59 high‐grade tumors were segmented using ITK‐SNAP on a T1‐weighted 3‐minute delayed sequence of MRI. Radiomics features were extracted from pre‐contrast, T1‐weighted arterial, venous, and delayed post‐contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures. Statistical Tests: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported. Results: The best model displayed an accuracy of 0.79. For low‐grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high‐grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported. Data Conclusion: Radiomics analysis shows promise in classifying pathology grades non‐invasively for patients with VHL‐associated ccRCC, potentially leading to better diagnosis and personalized treatment. Level of Evidence: 1 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has posed a major challenge to the diagnosis and management of ischemic heart disease (IHD). Myocardial perfusion imaging (MPI) is a non-invasive technique that can assess myocardial ischemia and viability, as well as provide prognostic information for patients with IHD. However, the role of MPI in COVID-19 patients is not well established. Methods: A comprehensive search of PubMed, Embase, Cochrane Library, and Web of Science was conducted to identify relevant studies published until Aprile 24th, 2024. The inclusion criteria were: (1) studies that performed MPI in COVID- 19 patients; (2) studies that reported the results of MPI in terms of ischemia, infarction, or perfusion defects; and (3) studies that compared MPI with other diagnostic modalities or clinical outcomes. The quality of the studies was assessed using the QUADAS-2 tool for diagnostic accuracy studies or the ROBIS tool for systematic reviews. A narrative synthesis was performed to summarize the main findings and recommendations. Results: A total of twenty-two studies met the inclusion criteria. The included study information was categorized into the following aspects: Change in the crude number of MPI scans performed, accidental and definite findings in MPI, and the prognostic outcome of COVID patients who underwent MPI. Conclusion: During Covid-19 pandemic, a reduction in the number of MPI studies was seen. Individuals with a history of COVID-19 infection have higher likelihood of developing ischemic heart disease (IHD). SARS-CoV-2 is an independent risk factor for the development of IHD. [ABSTRACT FROM AUTHOR]

The editorial discusses the importance of early detection and evaluation of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). It highlights the limitations of current diagnostic methods and the need for more advanced, noninvasive approaches to monitor DKD progression. The study by Yang et al. in the Journal of Magnetic Resonance Imaging introduces a combined radiomics model based on diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) for a more detailed assessment of kidney microstructural changes in T2DM patients. While the study shows promising results, it also acknowledges limitations such as the single-center nature of data collection and the need for future research to consider diverse demographic factors for more precise diagnostic tools. [Extracted from the article]

Introduction: Dysfunction or malpositioning of the aortic valve prosthesis is an uncommon but a known complication. Deployment of a second transcatheter aortic valve within previously implanted transcatheter aortic valve prosthesis is referred to as TAV-in-TAV. This procedure is often performed to treat aortic regurgitation occurring shortly after transcatheter aortic valve replacement (TAVR). Objective: The purpose of our study was to see if TAVR approach (transfemoral vs. transapical) impacted the incidence of periprocedural TAVin- TAV. Methods: A retrospective record review of consecutive patients was performed at a single center. The incidence of periprocedural TAVin- TAV was recorded and compared between patients' undergoing TAVR via transfemoral and transapical approach. Periprocedural complications and imaging findings were also recorded. Results: Out of 489 patients undergoing TAVR, only 10 patients (2%) required urgent periprocedural TAV-in-TAV, 5 patients each from the transfemoral and transapical groups (1.6% vs. 2.7%, respectively, p = 0.51). TAV-in-TAV deployment was technically successful in 8 patients (80%) and unsuccessful in 2 patients (20%; 1 transfemoral and 1 transapical), who required conversion to openheart surgery. The primary cause of TAVR failure requiring periprocedural TAV-in-TAV in both transfemoral and transapical patients was prosthesis malposition, frequently complicated by moderate to severe paravalvular aortic insufficiency. Statistically, there was no difference between the periprocedural morbidity and 30- day mortality for patients undergoing TAV-in-TAV via transfemoral versus transapical approach. Conclusion: Periprocedural TAV-in-TAV was uncommon in our cohort, primarily resulting from prosthesis malposition causing paravalvular aortic insufficiency. We found no difference in the number of patients requiring TAV-in-TAV in relation to TAVR approach. [ABSTRACT FROM AUTHOR]

Introduction: This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats., Methods: Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP)., Results: The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01., Conclusion: Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.