How to estimate stone volume and its use in stone surgery: a comprehensive review

OBJECTIVE

Current interventional guidelines refer to the cumulative stone diameter to choose the appropriate surgical modality (ureteroscopy [URS], extracorporeal shockwave lithotripsy [ESWL] and percutaneous nephrolithotomy [PCNL]). The stone volume (SV) has been introduced recently, to better estimate the stone burden. This review aimed to summarize the available methods to evaluate the SV and its use in urolithiasis treatment.

MATERIAL AND METHODS

A comprehensive review of the literature was performed in December 2022 by searching Embase, Cochrane and Pubmed databases. Articles were considered eligible if they described SV measurement or the stone free rate after different treatment modalities (SWL, URS, PCNL) or spontaneous passage, based on SV measurement. Two reviewers independently assessed the eligibility and the quality of the articles and performed the data extraction.

RESULTS

In total, 28 studies were included. All studies used different measurement techniques for stone volume. The automated volume measurement appeared to be more precise than the calculated volume. In vitro studies showed that the automated volume measurement was closer to actual stone volume, with a lower inter-observer variability. Regarding URS, stone volume was found to be more predictive of stone free rates as compared to maximum stone diameter or cumulative diameter for stones >20 mm. This was not the case for PCNL and SWL.

CONCLUSIONS

Stone volume estimation is feasible, manually or automatically and is likely a better representation of the actual stone burden. While for larger stones treated by retrograde intrarenal surgery, stone volume appears to be a better predictor of SFR, the superiority of stone volume throughout all stone burdens and for all stone treatments, remains to be proven. Automated volume acquisition is more precise and reproducible than calculated volume.

Neural network algorithm for detection of erosions and ankylosis on CT of the sacroiliac joints: multicentre development and validation of diagnostic accuracy

OBJECTIVES

To evaluate the feasibility and diagnostic accuracy of a deep learning network for detection of structural lesions of sacroiliitis on multicentre pelvic CT scans.

METHODS

Pelvic CT scans of 145 patients (81 female, 121 Ghent University/24 Alberta University, 18-87 years old, mean 40 ± 13 years, 2005-2021) with a clinical suspicion of sacroiliitis were retrospectively included. After manual sacroiliac joint (SIJ) segmentation and structural lesion annotation, a U-Net for SIJ segmentation and two separate convolutional neural networks (CNN) for erosion and ankylosis detection were trained. In-training validation and tenfold validation testing (U-Net-n = 10 × 58; CNN-n = 10 × 29) on a test dataset were performed to assess performance on a slice-by-slice and patient level (dice coefficient/accuracy/sensitivity/specificity/positive and negative predictive value/ROC AUC). Patient-level optimisation was applied to increase the performance regarding predefined statistical metrics. Gradient-weighted class activation mapping (Grad-CAM++) heatmap explainability analysis highlighted image parts with statistically important regions for algorithmic decisions.

RESULTS

Regarding SIJ segmentation, a dice coefficient of 0.75 was obtained in the test dataset. For slice-by-slice structural lesion detection, a sensitivity/specificity/ROC AUC of 95%/89%/0.92 and 93%/91%/0.91 were obtained in the test dataset for erosion and ankylosis detection, respectively. For patient-level lesion detection after pipeline optimisation for predefined statistical metrics, a sensitivity/specificity of 95%/85% and 82%/97% were obtained for erosion and ankylosis detection, respectively. Grad-CAM++  explainability analysis highlighted cortical edges as focus for pipeline decisions.

CONCLUSIONS

An optimised deep learning pipeline, including an explainability analysis, detects structural lesions of sacroiliitis on pelvic CT scans with excellent statistical performance on a slice-by-slice and patient level.

CLINICAL RELEVANCE STATEMENT

An optimised deep learning pipeline, including a robust explainability analysis, detects structural lesions of sacroiliitis on pelvic CT scans with excellent statistical metrics on a slice-by-slice and patient level.

KEY POINTS

• Structural lesions of sacroiliitis can be detected automatically in pelvic CT scans. • Both automatic segmentation and disease detection yield excellent statistical outcome metrics. • The algorithm takes decisions based on cortical edges, rendering an explainable solution.