Should kidney volume be used as an indicator of surgical occasion for patients with autosomal dominant polycystic kidney disease?

A MRI-based radiomics nomogram for evaluation of renal function in ADPKD

OBJECTIVES

This study is aimed to establish a fusion model of radiomics-based nomogram to predict the renal function of autosomal dominant polycystic kidney disease (ADPKD).

METHODS

One hundred patients with ADPKD were randomly divided into training group (n = 69) and test group (n = 31). The radiomics features were extracted from T1-weighted fat suppression images (FS-T1WI) and T2-weighted fat suppression images (FS-T2WI). Decision tree algorithm was employed to build radiomics model to get radiomics signature. Then multivariate logistic regression analysis was used to establish the radiomics nomogram based on independent clinical factors, conventional MR imaging variables and radiomics signature. The receiver operating characteristic (ROC) analysis and Delong test were used to compare the performance of radiomics model and radiomics nomogram model, and the decision curve to evaluate the clinical application value of radiomics nomogram model in the evaluation of renal function in patients with ADPKD.

RESULTS

Fourteen radiomics features were selected to establish radiomics model. Based on FS-T1WI and FS-T2WI sequences, the radiomics model showed good discrimination ability in training group and test group [training group: (AUC) = 0.7542, test group (AUC) = 0.7417]. The performance of radiomics nomogram model was significantly better than that of radiomics model in all data sets [radiomics model (AUC) = 0.7505, radiomics nomogram model (AUC) = 0.8435, p value = 0.005]. The analysis of calibration curve and decision curve showed that radiomics nomogram model had more clinical application value.

CONCLUSION

radiomics analysis of MRI can be used for the preliminary evaluation and prediction of renal function in patients with ADPKD. The radiomics nomogram model shows better prediction effect in renal function evaluation, and can be used as a non-invasive renal function prediction tool to assist clinical decision-making. Trial registration ChiCTR, ChiCTR2100046739. Registered 27 May 2021-retrospectively registered, http://www.ChiCTR.org.cn/showproj.aspx?proj=125955.

Patient-reported outcome measures for pain in autosomal dominant polycystic kidney disease: A systematic review

Pain is a common symptom in people with autosomal dominant polycystic kidney disease (ADPKD), but it is assessed and reported inconsistently in research, and the validity of the measures remain uncertain. The aim of this study was to identify the characteristics, content, and psychometric properties of measures for pain used in ADPKD. We conducted a systematic review including all trials and observational studies that reported pain in people with ADPKD. Items from all measures were categorized into content and measurement dimensions of pain. We assessed the general characteristics and psychometric properties of all measures. 118 studies, we identified 26 measures: 12 (46%) measures were developed for a non-ADPKD population, 1 (4%) for chronic kidney disease, 2 (8%) for polycystic liver disease and 11 (42%) specifically for ADPKD. Ten anatomical sites were included, with the lower back the most common (10 measures [39%]), four measurement dimensions (intensity (23 [88%]), frequency (3 [12%]), temporality (2 [8%]), and sensory (21 [81%]), two pain types, nociceptive including visceral (15 [58%]) and somatic (5 [20%]), and neuropathic (2 [8%]), and twelve impact dimensions, where the most frequent was work (5 [31%]). The validation data for the measures were variable and only the ADPKD Impact Scale reported all psychometric domains. The measures for pain in ADPKD varied in terms of content and length, and most had not been validated in ADPKD. A standardized psychometrically robust measure that captures patient-important dimensions of pain is needed to evaluate and manage this debilitating complication of ADPKD.