Clinical Outcomes of Portosystemic Shunts on the Outcome of Liver Transplantation.

Spontaneous portosystemic shunts (SPSSs) have been associated with worse clinical outcomes in the pre-liver transplantation (LT) setting, but little is known about their post-LT impacts. Our aim was to compare LT candidates with and without SPSSs and assess the impact of SPSSs on patient mortality and graft survival in the post-LT setting. Patients 18 years or older with abdominal imaging done prior to LT were included. Exclusion criteria were the presence of pre-LT surgical shunts, LT indications other than cirrhosis, and combined solid organ transplantations. SPSSs were classified as absent, small, or large according to their maximum diameter (8 mm). Multiple variables that could influence the post-LT course were extracted for analysis. Patient and graft survival were estimated using the Kaplan-Meier method and were compared between groups using a log-rank test. The project received institutional review board approval. We extracted data from 326 patients. After comparing patients without SPSS or with small or large SPSSs, no statistical difference was found for overall patient survival: no SPSS (n = 8/63), reference; small SPSS (n = 18/150), hazard ratio (HR), 1.05 (95% confidence interval [CI], 0.45-2.46); and large SPSS (n = 6/113), HR, 0.60 (95% CI, 0.20-1.78); P = 0.20. Also, no difference was found for graft survival: no SPSS (n = 11/63), reference; small SPSS (n = 21/150), HR, 0.80 (95% CI, 0.38-1.70); large SPSS (n = 11/113), HR, 0.59 (95% CI, 0.25-1.40); P = 0.48. Similarly, no statistical significance was found for these variables when comparing if the graft used was procured from a donation after circulatory death donor versus a donation after brain death donor. In conclusion, the previously described association between SPSSs and worse clinical outcomes in pre-LT patients seems not to persist once patients undergo LT. This study suggests that no steps to correct SPSS intraoperatively are necessary.

Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics.

PURPOSE: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics. METHODS: A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT). RESULTS: The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. CONCLUSION: Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers.