Pelvic venous pathologies in females are responsible for chronic symptoms grouped under the term pelvic congestion syndrome, which includes chronic pelvic pain, perineal heaviness, urgency, and postcoital pain, along with vulvar, perineal, and lower limb varicose veins. These conditions are also associated with ovarian and pelvic venous reflux and venous obstruction. This review aimed to explore the clinical and imaging modalities for diagnosing pelvic congestion syndrome, pelvic venous pathologies, their therapeutic management, and their outcomes.

OBJECTIVE. Three-dimensional printing is being used for surgical assistance, particularly for robot-assisted partial nephrectomy (RAPN). The objective of this study was to assess the anatomic accuracy of the 3D model used for 3D model-guided RAPN. SUBJECTS AND METHODS. The study included patients who underwent RAPN aided by 3D printing and anonymized CT. The model, based on preoperative CT with specialized software and a 3D printer, included vascular, urinary, parenchymal, and tumoral elements. It was then scanned. The major axis of the kidney and tumor and the volume of the tumor were measured blindly on the preoperative CT scan and the CT scan of the 3D model. Three comparative measurements between arterial branches were performed for each patient. The correlation of the arterial distribution between the scanner and the 3D model was evaluated on a Likert scale. RESULTS. A total of 16 patients were included between December 2017 and March 2018. The median tumor size was 4.7 cm (interquartile range [IQR], 3.9-6.5 cm). Between the initial CT scan and the 3D printed kidney, the major renal axis varied a median of 1.8% (IQR, 0.8-4.23%), major tumor axis varied 2.35% (IQR, 0.45-8.6%), and the tumor volume varied 14.7% (IQR, 11.5-25.2%). The median arterial interbranch measurement (three measurements performed on each kidney) was 1.8% (IQR, 1-3.25%). Fourteen of 16 (87.5%) 3D printed models had a score of 4 or higher by two readers and were considered valid. CONCLUSION. Three-dimensional printed models are accurate with respect to anatomic reality. The reliability of surgical assistance with 3D printed models must be evaluated.

Background: Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. Purpose: This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. Materials and methods: A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. Results: A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/− 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and “intensity mean value” was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. Conclusion: Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.