Endoscopic identification of urinary stone composition: A study of South Eastern Group for Urolithiasis Research (SEGUR 2)

Management of urinary stones: state of the art and future perspectives by experts in stone disease

AIM

To present state of the art on the management of urinary stones from a panel of globally recognized urolithiasis experts who met during the Experts in Stone Disease Congress in Valencia in January 2024. Options of treatment: The surgical treatment modalities of renal and ureteral stones are well defined by the guidelines of international societies, although for some index cases more alternative options are possible. For 1.5 cm renal stones, both m-PCNL and RIRS have proven to be valid treatment alternatives with comparable stone-free rates. The m-PCNL has proven to be more cost effective and requires a shorter operative time, while the RIRS has demonstrated lower morbidity in terms of blood loss and shorter recovery times. SWL has proven to be less effective at least for lower calyceal stones but has the highest safety profile. For a 6mm obstructing stone of the pelviureteric junction (PUJ) stone, SWL should be the first choice for a stone less than 1 cm, due to less invasiveness and lower risk of complications although it has a lower stone free-rate. RIRS has advantages in certain conditions such as anticoagulant treatment, obesity, or body deformity. Technical issues of the surgical procedures for stone removal: In patients receiving antithrombotic therapy, SWL, PCN and open surgery are at elevated risk of hemorrhage or perinephric hematoma. URS, is associated with less morbidity in these cases. An individualized combined evaluation of risks of bleeding and thromboembolism should determine the perioperative thromboprophylactic strategy. Pre-interventional urine culture and antibiotic therapy are mandatory although UTI treatment is becoming more challenging due to increasing resistance to routinely applied antibiotics. The use of an intrarenal urine culture and stone culture is recommended to adapt antibiotic therapy in case of postoperative infectious complications. Measurements of temperature and pressure during RIRS are vital for ensuring patient safety and optimizing surgical outcomes although techniques of measurements and methods for data analysis are still to be refined. Ureteral stents were improved by the development of new biomaterials, new coatings, and new stent designs. Topics of current research are the development of drug eluting and bioresorbable stents. Complications of endoscopic treatment: PCNL is considered the most invasive surgical option. Fever and sepsis were observed in 11 and 0.5% and need for transfusion and embolization for bleeding in 7 and 0.4%. Major complications, as colonic, splenic, liver, gall bladder and bowel injuries are quite rare but are associated with significant morbidity. Ureteroscopy causes less complications, although some of them can be severe. They depend on high pressure in the urinary tract (sepsis or renal bleeding) or application of excessive force to the urinary tract (ureteral avulsion or stricture). Diagnostic work up:  Genetic testing consents the diagnosis of monogenetic conditions causing stones. It should be carried out in children and in selected adults. In adults, monogenetic diseases can be diagnosed by systematic genetic testing in no more than 4%, when cystinuria, APRT deficiency, and xanthinuria are excluded. A reliable stone analysis by infrared spectroscopy or X-ray diffraction is mandatory and should be associated to examination of the stone under a stereomicroscope. The analysis of digital images of stones by deep convolutional neural networks in dry laboratory or during endoscopic examination could allow the classification of stones based on their color and texture. Scanning electron microscopy (SEM) in association with energy dispersive spectrometry (EDS) is another fundamental research tool for the study of kidney stones. The combination of metagenomic analysis using Next Generation Sequencing (NGS) techniques and the enhanced quantitative urine culture (EQUC) protocol can be used to evaluate the urobiome of renal stone formers. Twenty-four hour urine analysis has a place during patient evaluation together with repeated measurements of urinary pH with a digital pH meter. Urinary supersaturation is the most comprehensive physicochemical risk factor employed in urolithiasis research. Urinary macromolecules can act as both promoters or inhibitors of stone formation depending on the chemical composition of urine in which they are operating. At the moment, there are no clinical applications of macromolecules in stone management or prophylaxis. Patients should be evaluated for the association with systemic pathologies.

PROPHYLAXIS

Personalized medicine and public health interventions are complementary to prevent stone recurrence. Personalized medicine addresses a small part of stone patients with a high risk of recurrence and systemic complications requiring specific dietary and pharmacological treatment to prevent stone recurrence and complications of associated systemic diseases. The more numerous subjects who form one or a few stones during their entire lifespan should be treated by modifications of diet and lifestyle. Primary prevention by public health interventions is advisable to reduce prevalence of stones in the general population. Renal stone formers at "high-risk" for recurrence need early diagnosis to start specific treatment. Stone analysis allows the identification of most "high-risk" patients forming non-calcium stones: infection stones (struvite), uric acid and urates, cystine and other rare stones (dihydroxyadenine, xanthine). Patients at "high-risk" forming calcium stones require a more difficult diagnosis by clinical and laboratory evaluation. Particularly, patients with cystinuria and primary hyperoxaluria should be actively searched.

FUTURE RESEARCH

Application of Artificial Intelligence are promising for automated identification of ureteral stones on CT imaging, prediction of stone composition and 24-hour urinary risk factors by demographics and clinical parameters, assessment of stone composition by evaluation of endoscopic images and prediction of outcomes of stone treatments. The synergy between urologists, nephrologists, and scientists in basic kidney stone research will enhance the depth and breadth of investigations, leading to a more comprehensive understanding of kidney stone formation.

Endoscopic Stone Composition Identification: Is Accuracy Improved by Stone Appearance During Laser Lithotripsy?

OBJECTIVE

To evaluate if videos during laser lithotripsy increase accuracy and confidence of stone identification by urologists compared to still pictures.

METHODS

We obtained representative pictures and videos of 4 major stone types from 8 different patients during ureteroscopy with holmium laser lithotripsy. A REDCap survey was created and emailed to members of the Endourological Society. The survey included a picture followed by the corresponding video of each stone undergoing laser lithotripsy and additional clinical information. Each picture and video included multiple-choice questions about stone composition and response confidence level. Accuracy, confidence levels, and rates of rectification (change from incorrect to correct answer) or confounding (correct to incorrect) after watching videos were analyzed.

RESULTS

One hundred eighty-seven urologists responded to the survey. The accuracy rate of stone identification with pictures was 43.8% vs 46.1% with videos (P = .27). Accuracy for individual stones was low and highly variable. Video only improved accuracy for 1 cystine stone. After viewing videos, participants were more likely to rectify vs confound their answers. Urologists were more likely to be confident with videos than pictures alone (65.4% vs 53.7%, respectively; P <.001); however, confident answers were not more likely to yield accurate predictions with videos vs still pictures.

CONCLUSION

Stone identification by urologists is marginally improved with videos vs pictures alone. Overall, accuracy in stone identification is low irrespective of confidence level, picture, and lithotripsy video visualization. Urologists should be cautious in using endoscopic stone appearance to direct metabolic management.

On the rocks: can urologists identify stone composition based on endoscopic images alone? A worldwide survey of urologists

PURPOSE

As part of the management of nephrolithiasis, determination of chemical composition of stones is important. Our objective in this study is to assess urologists' accuracy in making visual, intraoperative determinations of stone composition.

MATERIALS AND METHODS

We conducted a REDCap survey asking urologists to predict stone composition based on intraoperative images of 10 different pure-composition kidney stones of 7 different types: calcium oxalate monohydrate (COM), calcium oxalate dihydrate (COD), calcium phosphate (CP) apatite, CP brushite, uric acid (UA), struvite (ST) and cystine (CY). To evaluate experience, we examined specific endourologic training, years of experience, and number of ureteroscopy (URS) cases/week. A self-assessment of ability to identify stone composition was also required.

RESULTS

With a response rate of 26% (366 completed surveys out of 1,370 deliveries), the overall accuracy of our cohort was 44%. COM, ST, and COD obtained the most successful identification rates (65.9%, 55.7%, and 52.0%, respectively). The most frequent misidentified stones were CP apatite (10.7%) and CY (14.2%). Predictors of increased overall accuracy included self-perceived ability to determine composition and number of ureteroscopies per week, while years of experience did not show a positive correlation.

CONCLUSIONS

Although endoscopic stone recognition can be an important tool for surgeons, it is not reliable enough to be utilized as a single method for stone identification, suggesting that urologists need to refine their ability to successfully recognize specific stone compositions intraoperatively.

How reliable is endoscopic stone recognition? A comparison between visual stone identification and formal stone analysis

OBJECTIVE

To assess the diagnostic accuracy and intra-observer agreement of endoscopic stone recognition compared with formal stone analysis.

INTRODUCTION

Stone analysis is a corner stone in the prevention of stone recurrence. Although X-ray diffraction and infrared spectroscopy are the recommended techniques for reliable formal stone analysis, this is not always possible, and the process takes time and is costly. Endoscopic stone recognition could be an alternative as it would give immediate information on stone composition.

MATERIAL AND METHODS

Fifteen endourologists predicted stone composition based on 100 videos from ureterorenoscopy. Diagnostic accuracy was evaluated by comparing the prediction from visual assessment with stone analysis by X-ray diffraction. After 30 days, the videos were reviewed again in a random order to assess intra-observer agreement.

RESULTS

The median diagnostic accuracy for calcium oxalate monohydrate was of 54% in questionnaire 1 (Q1) and 59% in questionnaire 2 (Q2), whereas calcium oxalate dihydrate had a median diagnostic accuracy of 75% in Q1 and 50% in Q2. The diagnostic accuracy for calcium hydroxyphosphate was 10% in Q1 and 13% in Q2. The median diagnostic accuracy for calcium hydrogen phosphate dihydrate and calcium magnesium phosphate was 0% in both questionnaires. The median diagnostic accuracy for magnesium ammonium phosphate was in 20% in Q1 and 40% in Q2. The median diagnostic accuracy for uric acid was 22% in both questionnaires. Finally, there was a diagnostic accuracy of 60% in Q1 and 80% in Q2 for cystine. The intra-observer agreement ranged between 45-72%.

CONCLUSION

Diagnostic accuracy of endoscopic stone recognition is limited and intra-observer agreement is below the threshold of acceptable agreement.

Prediction of the composition of urinary stones using deep learning

PURPOSE

This study aimed to predict the composition of urolithiasis using deep learning from urinary stone images.

MATERIALS AND METHODS

We classified 1,332 stones into 31 classes according to the stone composition. The top 4 classes with a frequency of 110 or more (class 1: calcium oxalate monohydrate [COM] 100%, class 2: COM 80%+struvite 20%, class 3: COM 60%+calcium oxalate dihydrate [COD] 40%, class 4: uric acid 100%) were selected. With the 965 stone images of the top 4 classes, we used the seven convolutional neural networks (CNN) to classify urinary stones and compared their classification performances.

RESULTS

Among the seven models, Xception_Ir0.001 showed the highest accuracy, precision, and recall and was selected as the CNN model to predict the stone composition. The sensitivity and specificity for the 4 classes by Xception_Ir0.001 were as follows: class 1 (94.24%, 91.73%), class 2 (85.42%, 96.14%), class 3 (86.86%, 99.59%), and class 4 (94.96%, 98.82%). The sensitivity and specificity of the individual components of the stones were as follows. COM (98.82%, 94.96%), COD (86.86%, 99.64%), struvite (85.42%, 95.59%), and uric acid (94.96%, 98.82%). The area under the curves for class 1, 2, 3, and 4 were 0.98, 0.97, 1.00, and 1.00, respectively.

CONCLUSIONS

This study showed the feasibility of deep learning for the diagnostic ability to assess urinary stone composition from images. It can be an alternative tool for conventional stone analysis and provide decision support to urologists, improving the effectiveness of diagnosis and treatment.