Percutaneous nephrostomy versus retrograde ureteral stent for acute upper urinary tract obstruction with urosepsis.

PURPOSE: We aimed to compare the efficacy of percutaneous nephrostomy (PCN) versus retrograde ureteric stent (RUS) for acute upper urinary tract obstruction with urosepsis. MATERIALS AND METHODS: We performed a random study, comparing PCN to RUS, for the treatment of patients requiring emergency drainage due to acute upper urinary tract obstruction with urosepsis between January 2019 to March 2020. Data collected included patient characteristics, stone material, microbiological characteristics, and laboratory data. Statistical analysis was performed by the student's t-test or Mann-Whitney U test or chi-squared test and Fisher exact test. RESULTS: At first, a total of 75 patients were eligibly assessed for enrollment. Among them, 3 cases were excluded for declining to participate and 7 cases were failed treated with RUS. At last, 35 PCN (53.85%) and 30 RUS (46.15%) patients were analyzed. There were 24 (36.92%) men and 41 (63.08%) women. The median age was 65 years. Emergency decompression was achieved by PCN in 35 (53.85%) patients and by RUS in 30 (46.15%). Urine culture was positive in 32 (49.23%) patients, of which 17 (53.13%) had E. coli. Postoperative C-reactive protein value and normal temperature recovery time in the PCN group were significantly lower than in the RUS group(P < .05). CONCLUSION: PCN had a better outcome than RUS in emergency drainage with urosepsis, especially for patients with severe inflammation and fever.

Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists.

OBJECTIVE. The objective of our study was to compare the performance of radiologicradiomic machine learning (ML) models and expert-level radiologists for differentiation of benign and malignant solid renal masses using contrast-enhanced CT examinations. MATERIALS AND METHODS. This retrospective study included a cohort of 254 renal cell carcinomas (RCCs) (190 clear cell RCCs [ccRCCs], 38 chromophobe RCCs [chrRCCs], and 26 papillary RCCs [pRCCs]), 26 fat-poor angioleiomyolipomas, and 10 oncocytomas with preoperative CT examinations. Lesions identified by four expert-level radiologists (> 3000 genitourinary CT and MRI studies) were manually segmented for radiologicradiomic analysis. Disease-specific support vector machine radiologic-radiomic ML models for classification of renal masses were trained and validated using a 10-fold cross-validation. Performance values for the expert-level radiologists and radiologic-radiomic ML models were compared using the McNemar test. RESULTS. The performance values for the four radiologists were as follows: sensitivity of 73.7-96.8% (median, 84.5%; variance, 122.7%) and specificity of 48.4-71.9% (median, 61.8%; variance, 161.6%) for differentiating ccRCCs from pRCCs and chrRCCs; sensitivity of 73.7-96.8% (median, 84.5%; variance, 122.7%) and specificity of 52.8-88.9% for differentiating ccRCCs from fat-poor angioleiomyolipomas and oncocytomas (median, 80.6%; variance, 269.1%); and sensitivity of 28.1-60.9% (median, 84.5%; variance, 122.7%) and specificity of 75.0-88.9% for differentiating pRCCs and chrRCCs from fat-poor angioleiomyolipomas and oncocytomas (median, 50.0%; variance, 191.1%). After a 10-fold cross-validation, the radiologic-radiomic ML model yielded the following performance values for differentiating ccRCCs from pRCCs and chrRCCs, ccRCCs from fat-poor angioleiomyolipomas and oncocytomas, and pRCCs and chrRCCs from fat-poor angioleiomyolipomas and oncocytomas: a sensitivity of 90.0%, 86.3%, and 73.4% and a specificity of 89.1%, 83.3%, and 91.7%, respectively. CONCLUSION. Expert-level radiologists had obviously large variances in performance for differentiating benign from malignant solid renal masses. Radiologic-radiomic ML can be a potential way to improve interreader concordance and performance.