e16548 Background: Early characterization of renal cell carcinoma (RCC) is pertinent for cancer prognosis and patient treatment. However, characterization of renal masses as benign or malignant on computed tomography (CT) remains a difficult clinical challenge. Angiogenesis plays a major role in RCC progression and assessing neovascularity in tumor-burdened kidneys with Re-VASC scoring has been shown to aptly predict tumor pathology and staging. In the current study, we evaluated if Re-VASC can reliably differentiate between malignant T1a ( < 4cm) RCC and benign T1a renal masses. Methods: All eligible patients had a diagnosis of T1a from pathologic or clinical evaluation. The inclusion criteria for malignant masses included a diagnosis of RCC by pathology after partial or radical nephrectomy. Our T1a benign group was comprised of patients with a diagnosis of a non-malignant renal mass (angiomyolipoma or oncocytoma) established via histopathology or CT imaging (fat containing lesions). Re-VASC score was calculated on axial preoperative contrast enhanced CT utilizing the system described below: 0 = No visible neovascularization 1 = visualized single new vessel < 3mm in diameter 2 = visualized single new vessel ≥3mm in diameter 3 = visualized multiple new vessels < 3mm in diameter 4 = visualized multiple new vessels and at least one vessel ≥3mm in diameter. Results: A total of 64 benign and 69 malignant T1a tumors were included. Comparison of the two groups demonstrated no significant differences with regards to age, sex, ASA, and BMI. The average Re- VASC scores was 0.159 and 0.753 for benign and malignant masses respectively (p < 0.001). Additionally, the tumor size for benign masses was 2.373 and for malignant masses was 2.546 (p = 0.210). There was no significant difference in Re-VASC score between the contralateral kidneys of malignant and benign kidney masses (p < 0.508). Additionally, the sensitivity, specificity and positive predictive values were 26%, 86% and 74% respectively, when utilizing a Re-VASC score of 1 as a cutoff for malignancy. Conclusions: Our results show a significantly higher Re-VASC score in T1a malignant tumors and T1a benign tumors. While biopsy remains the gold standard for confirmation of malignant tumors such as RCC, radiological evaluation with tumor scoring systems like Re-VASC may offer a useful clinical decision-making tool in a less-invasive manner. Because renal biopsy carries non-negligible risk of complications, reducing volume of biopsies among low-risk patients may lead to better overall care. The Re-VASC score shows potential as a confirmatory test with a specificity of 86%. We would like to apply the Re-VASC score to an expanded multi-center trial, in order to assess its translatability to the general population.
Gastrointestinal stromal tumours (GIST) constitute a heterogeneous group of neoplasms which, although rare (around 1% of the total number of malignant tumours), are the most common mesenchymal tumours of the gastrointestinal tract. In the past they were not very well known, whereas today, thanks to the remarkable progress made in the immunohistochemical and molecular fields, considerable knowledge has been acquired, offering new opportunities for classification and, above all, for a more adequate multidisciplinary treatment of this pathology. In this study, the authors report a case of a bleeding GIST of the stomach which they recently observed and discuss it in the light of recent reflections on the aetiopathogenesis, diagnosis and therapy of these tumours in the literature.
The appearance of acute cholecystitis can make to complicate a natural history of cholelitiasis or post-operating time of patients that have concomitant predisposition factors. The best therapy is the cholecystectomy but somewhere for the critical general conditions is too much dangerous to make a surgical procedure. However we need to stabilize patients conditions, also for a short time. Our experience suggest us that percutaneous transhepatic cholecystostomy is a simple method without any complications, efficacious to resolve the acute sepsis in patients with cholecystitis that not be able to tolerate a surgical procedure.
Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.
