Imaging in stone diagnosis and surgical planning

Tight junction and kidney stone disease

Defects of tight junction (TJ) are involved in many diseases related to epithelial cell functions, including kidney stone disease (KSD), which is a common disease affecting humans for over a thousand years. This review provides brief overviews of KSD and TJ, and summarizes the knowledge on crystal-induced defects of TJ in renal tubular epithelial cells (RTECs) in KSD. Calcium oxalate (CaOx) crystals, particularly COM, disrupt TJ via p38 MAPK and ROS/Akt/p38 MAPK signaling pathways, filamentous actin (F-actin) reorganization and α-tubulin relocalization. Stabilizing p38 MAPK signaling, reactive oxygen species (ROS) production, F-actin and α-tubulin by using SB239063, N-acetyl-L-cysteine (NAC), phalloidin and docetaxel, respectively, successfully prevent the COM-induced TJ disruption and malfunction. Additionally, genetic disorders of renal TJ, including mutations and single nucleotide polymorphisms (SNPs) of CLDN2, CLDN10b, CLDN14, CLDN16 and CLDN19, also affect KSD. Finally, the role of TJ as a potential target for KSD therapeutics and prevention is also discussed.

[Imaging for urolithiasis]

The substantial reduction of radiation exposure using (ultra-)low dose programs in native computed tomographic imaging has led to considerable changes in imaging diagnostics and treatment planning in urolithiasis in recent years. In addition, especially in Germany, ultrasound diagnostics is highly available in terms of equipment and with increasing expertise. This can largely replace the previous radiation-associated procedures in emergency and follow-up diagnostics, but also in intraoperative imaging, e.g., in percutaneous stone therapy (intraoperative fluoroscopy). This is reflected in the international guidelines, which recommend these two modalities as first-line diagnostics in all areas mentioned. Continuous technical development enables ever higher resolution imaging and thus improved diagnostics with high sensitivity and specificity. This also enables reliable imaging of particularly vulnerable patient groups, such as children or pregnant women. In addition, methods from the field of artificial intelligence (AI; machine learning, deep learning) are increasingly being used for automated stone detection and stone characterization including its composition. Furthermore, AI models can provide prognosis models as well as individually tailored treatment, follow-up, and prophyaxis. This will enable further personalization of diagnostics and therapy in the field of urolithiasis.

Theranostic roles of machine learning in clinical management of kidney stone disease

Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.

The 2023 Stone-Free CT Mandate: Addressing the Two Sides of the Debate

To the editors of the Journal of Endourology, we write to express our reservations in the recent announcement that starting in 2023 all reporting of stone-free rates be based on CT.