Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables.

We aimed to develop and evaluate a statistical model, which included known pre-treatment factors and new computed tomography texture analysis (CTTA) variables, for its ability to predict the likelihood of a successful outcome after extracorporeal shockwave lithotripsy (SWL) treatment for renal and ureteric stones. Up to half of patients undergoing SWL may fail treatment. Better prediction of which cases will likely succeed SWL will help patients to make an informed decision on the most effective treatment modality for their stone. 19 pre-treatment factors for SWL success, including 6 CTTA variables, were collected from 459 SWL cases at a single centre. Univariate and multivariable analyses were performed by independent statisticians to predict the probability of a stone free (both with and without residual fragments) outcome after SWL. A multivariable model had an overall accuracy of 66% on Receiver Operator Curve (ROC) analysis to predict for successful SWL outcome. The variables most frequently chosen for the model were those which represented stone size. Although previous studies have suggested SWL can be reliably predicted using pre-treatment factors and that analysis of CT stone images may improve outcome prediction, the results from this study have not produced a useful model for SWL outcome prediction.

The Preoperative Assessment and Optimization of Patients Undergoing Major Urological Surgery.

PURPOSE OF REVIEW: Improving patient outcomes from major urological surgery requires not only advancement in surgical technique and technology, but also the practice of patient-centered, multidisciplinary, and integrated medical care of these patients from the moment of contemplation of surgery until full recovery. This review examines the evidence for recent developments in preoperative assessment and optimization that is of relevance to major urological surgery. RECENT FINDINGS: Current perioperative medicine recommendations aim to improve the short-term safety and long-term effectiveness of surgical treatments by the delivery of multidisciplinary integrated medical care. New strategies to deliver this aim include preoperative risk stratification using a frailty index and cardiopulmonary exercise testing for patients undergoing intra-abdominal surgery (including radical cystectomy), preoperative management of iron deficiency and anemia, and preoperative exercise intervention. Proof of the utility and validity for improving surgical outcomes through advances in preoperative care is still evolving. Evidence-based developments in this field are likely to benefit patients undergoing major urological surgery, but further research targeted at high-risk patients undergoing specific urological operations is required.

CT Texture Analysis of Ex Vivo Renal Stones Predicts Ease of Fragmentation with Shockwave Lithotripsy.

INTRODUCTION: Understanding the factors affecting success of extracorporeal shockwave lithotripsy (SWL) would improve informed decision-making on the most appropriate treatment modality for an individual patient. Although stone size and skin-to-stone distance do correlate with fragmentation efficacy, it has been shown that stone composition and architecture, as reflected by structural heterogeneity on CT, are also important factors. This study aims to determine if CT texture analysis (CTTA), a novel, nondestructive, and objective tool that generates statistical metrics reflecting stone heterogeneity, could have utility in predicting likelihood of SWL success. MATERIALS AND METHODS: Seven spontaneously passed, intact renal tract stones, were scanned ex vivo using standard CT KUB and micro-CT. The stones were then fragmented in vitro using a clinical lithotripter, after which, chemical composition analysis was performed. CTTA was used to generate a number of metrics that were correlated to the number of shocks needed to fragment the stone. RESULTS: CTTA metrics reflected stone characteristics and composition, and predicted ease of SWL fragmentation. The strongest correlation with number of shocks required to fragment the stone was mean Hounsfield unit (HU) density (r = 0.806, p = 0.028) and a CTTA metric measuring the entropy of the pixel distribution of the stone image (r = 0.804, p = 0.039). Using multiple linear regression analysis, the best model showed that CTTA metrics of entropy and kurtosis could predict 92% of the outcome of number of shocks needed to fragment the stone. This was superior to using stone volume or density. CONCLUSIONS: CTTA metrics entropy and kurtosis have been shown in this experimental ex vivo setting to strongly predict fragmentation by SWL. This warrants further investigation in a larger clinical study for the contribution of CT textural metrics as a measure of stone heterogeneity, along with other known clinical factors, to predict likelihood of SWL success.