Predicting the Efficacy of Repeated Shockwave Lithotripsy for Treating Patients with Upper Urinary Tract Calculi Using an Artificial Neural Network Model
Urology Journal,
Vol. 21 No. 04 (2024),
9 June 2024
,
Page 234-241
https://doi.org/10.22037/uj.v20i.8006
Abstract
Purpose: To establish a prediction model for repeated shockwave lithotripsy (SWL) efficacy to help choose an
appropriate treatment plan for patients with a single failed lithotripsy, reducing their treatment burden.
Patients and Methods: The clinical records and imaging data of 304 patients who underwent repeat SWL for upper
urinary tract calculi (UUTC) at the Urology Centre of Shiyan People’s Hospital between April 2019 and April
2023 were retrospectively collected. This dataset was divided into training (N = 217; 146 males [67.3%] and 71
females [32.7%]) and validation (N = 87; 66 males [75.9%] and 21 females [24.1%]) sets. The overall predictive
accuracy of the models was calculated separately for the training and validation. Receiver operating characteristic
(ROC) curves were plotted, and the area under the ROC curve (AUC) was calculated. The normalized importance
of each independent variable (derived from the one-way analyses) in the input layer of the artificial neural network
(ANN) model for the dependent variable (success or failure in repeat SWL) in the output layer was plotted as a
bar chart.
Results: This study included 304 patients, of whom 154 (50.7%) underwent successful repeat SWL. Predictive
models were constructed in the training set and assessed in the validation set. Fourteen influencing factors were
selected as input variables to build an ANN model: age, alcohol, body mass index, sex, hydronephrosis, hematuria,
mean stone density (MSD), skin-to-stone distance (SSD), stone heterogeneity index (SHI), stone volume (SV),
stone retention time, smoking, stone location, and urinary irritation symptom. The model’s AUC was 0.852 (95%
confidence interval (CI): 0.8–0.9), and its predictive accuracy for stone clearance in the validation group was
83.3%. The order of importance of the independent variables was MSD > SV > SSD > stone retention time > SHI.
Conclusion: Establishing an ANN model for repeated SWL of UUTC is crucial for optimizing patient care. This
model will be pivotal in providing accurate treatment plans for patients with an initial unsuccessful SWL treatment.
Moreover, it can significantly enhance the success rate of subsequent SWL treatments, ultimately alleviating patients’ treatment burden.
- repeated Extracorporeal shock wave lithotripsy; Artificial neural network model; Upper urinary tract calculi
How to Cite
References
Sorokin I, Mamoulakis C, Miyazawa K, Rodgers A, Talati J, Lotan Y. Epidemiology of stone disease across the world. World J Urol, 2017, 35(9): 1301-1320.
Xia K, Xu Y, Qi Q, et al. Establishment of a new predictive model for the recurrence of upper urinary tract stones. Int Urol Nephrol, 2023, 55(10): 2411-2420.
Veser J, Jahrreiss V, Seitz C. Curr Opin Urol. Current opinion in urology, 2021, Publish Ahead of Print(2).
Peng C, Chen Z, Xu J. Risk factors for urinary infection after retrograde upper urinary lithotripsy: Implication for nursing. Medicine, 2021, 100(31): e26172.
Wagenius M, Oddason K, Utter M, et al. Factors influencing stone-free rate of Extracorporeal Shock Wave Lithotripsy (ESWL); a cohort study. Scand J Urol, 2022, 56(3): 237-243.
Chung D Y, Kang D H, Cho K S, et al. Comparison of stone-free rates following shock wave lithotripsy, percutaneous nephrolithotomy, and retrograde intrarenal surgery for treatment of renal stones: A systematic review and network meta-analysis. PloS one, 2019, 14(2): e0211316.
Lee J Y, Andonian S, Bhojani N, et al. Canadian Urological Association guideline: Management of ureteral calculi - Abridged version. Can Urol Assoc J, 2021, 15(12): 383-393.
Hevia M, García Á, Ancizu F J, et al. Predicting the effectiveness of extracorporeal shock wave lithotripsy on urinary tract stones. Risk groups for accurate retreatment. Actas Urol Esp, 2017, 41(7): 451-457.
Xu Z H, Zhou S, Jia C P, Lv J L. Prediction of Proximal Ureteral Stones Clearance after Shock Wave Lithotripsy Using an Artificial Neural Network. Urol J, 2021, 18(5): 491-496.
Garg M, Johnson H, Lee S M, Rai B P, Somani B, Philip J. Role of Hounsfield Unit in Predicting Outcomes of Shock Wave Lithotripsy for Renal Calculi: Outcomes of a Systematic Review. Curr Urol Rep, 2023, 24(4): 173-185.
Ramdani F, Setiawan B, Rusydi A, Furqon M. An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia. Preprints, 2021.
Kobayashi M, Ishioka J, Matsuoka Y, Fukuda Y, Fujii Y. Computer-aided Diagnosis with a Convolutional Neural Network Algorithm for Automated Detection of Urinary Tract Stones on Plain X-ray. BMC Urol, 2021, 21(1).
Cummings J M, Boullier J A, Izenberg S D, Kitchens D M, Kothandapani R V. Prediction of spontaneous ureteral calculous passage by an artificial neural network. J Urol, 2000, 164(2): 326-328.
Wang J, Wang D, Wang Y, Wang S, Shao Y, Lu J. Predicting narrow ureters before ureteroscopic lithotripsy with a neural network: a retrospective bicenter study. Urolithiasis: 1-12.
Weiguo H, Jianxing L, Zhangqun Y. Hot Topics and Advances in the Diagnosis and Treatment of Urologic Stones at the 2019 European Society of Urology Annual Meeting. 2019, 40(4): 251-252.
Wang C H, Mo L R, Lin R C, Kuo J J, Chang K K, Wu J J. Artificial neural network model is superior to logistic regression model in predicting treatment outcomes of interferon-based combination therapy in patients with chronic hepatitis C. Intervirology, 2008, 51(1): 14-20.
Yao L, Zhong Y, Wu J, et al. Multivariable Logistic Regression And Back Propagation Artificial Neural Network To Predict Diabetic Retinopathy. Diabetes, metabolic syndrome and obesity : targets and therapy. Diabetes Metab Syndr Obes, 2019, 12: 1943-1951.
Turan T, Efioglu O, Danacioglu Y O, et al. Can intervals in extracorporeal shock wave lithotripsy sessions affect success in the treatment of upper ureteral stones? Wideochir Inne Tech Maloinwazyjne, 2018, 13(4): 507-511.
Iqbal N, Hasan A, Nazar A, et al. Role of Stone Heterogeneity Index in Determining Success of Shock Wave Lithotripsy in Urinary Calculi. J Clin Transl Res, 2021, 7(2): 241-247.
Al-Ali B M, Patzak J, Lutfi A, Pummer K, Augustin H. Impact of urinary stone volume on computed tomography stone attenuations measured in Hounsfield units in a large group of Austrian patients with urolithiasis. Cent European J Urol, 2014, 67(3): 289-295.
Chen Y, Wen Y, Yu Q, Duan X, Wu W, Zeng G. Percutaneous nephrolithotomy versus flexible ureteroscopic lithotripsy in the treatment of upper urinary tract stones: a meta-analysis comparing clinical efficacy and safety. BMC Urol, 2020, 20(1): 109.
Kang H. The prevention and handling of the missing data. Korean J Anesthesiol, 2013, 64(5): 402-406.
Lauren J, Irina B, Michael R . Multiple imputation with missing data indicators. Psychological methods, 2021, 6(4): 317-329.
Bahmer A, Gupta D, Effenberger F. Modern Artificial Neural Networks: Is Evolution Cleverer? Neural Comput, 2023, 35(5): 763-806.
Forsström J J, Dalton K J. Artificial neural networks for decision support in clinical medicine. Ann Med, 1995, 27(5): 509-517.
Qian Y, Hongtao J, Zengqin L, al e. Correlation analysis between measurement methods of kidney stone burden and operation time or result of flexible ureteroscopic lithotripsy. Chinese Journal of Urology, 2021, 42(5): 339-343.
Huixin X, Liying T, Bo L. Development and validation of a column-line graphical model for predicting the efficacy of extracorporeal shock wave lithotripsy for ureteral stones. Journal of Nanjing Medical University (Natural Sciences) 2021, 41(6): 899-902.
Ouzaid I, Al‐qahtani S, Dominique S, et al. A 970 Hounsfield units (HU) threshold of kidney stone density on non-contrast computed tomography (NCCT) improves patients' selection for extracorporeal shockwave lithotripsy (ESWL): evidence from a prospective study. BJU Int, 2013, 110(11b).
Assimos D, Krambeck A, Miller N L, et al. Surgical Management of Stones: American Urological Association/Endourological Society Guideline, PART I. J Urol, 2016: 1161-1169.
Lee J Y, Kim J H, Kang D H, et al. Stone heterogeneity index as the standard deviation of Hounsfield units: A novel predictor for shock-wave lithotripsy outcomes in ureter calculi. Sci Rep, 2016, 6: 23988.
Wiesenthal J D, Ghiculete D, RJ D A H, Pace K T. Evaluating the importance of mean stone density and skin-to-stone distance in predicting successful shock wave lithotripsy of renal and ureteric calculi.Urol Res, 2010, 38(4): 307-313.
Pareek G, Hedican S P, Lee F T, Jr., Nakada S Y. Shock wave lithotripsy success determined by skin-to-stone distance on computed tomography. Urology, 2005, 66(5): 941-944.
Yamashita S, Kohjimoto Y, Iwahashi Y, et al. Noncontrast Computed Tomography Parameters for Predicting Shock Wave Lithotripsy Outcome in Upper Urinary Tract Stone Cases. Biomed Res Int, 2018, 2018: 9253952.
Jia B, Liu J, Hu B, Chen Z. Using retroperitoneal laparoscopic ureterolithotomy in the treatment of impacted upper ureteric calculi. Transl Androl Urol, 2022, 11(1): 104-109.
Dursun M, Ozbek E, Otunctemur A, Sahin S, Cakir S S. Clinical presentation of urolithiasis in older and younger population. Arch Ital Urol Androl, 2014, 86(4): 249-252.
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