Advanced search
Publication Cover
International Journal of Nephrology and Renovascular Disease Volume 16, 2023 - Issue
Submit an article Journal homepage
166
Views
0
CrossRef citations to date
0
Altmetric
ORIGINAL RESEARCH

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System

Rami AlAzab1 Department of General Surgery and Urology, King Abdullah University Hospital, Irbid, JordanView further author information
,
Owais Ghammaz2 Faculty of Medicine, Jordan University of Science and Technology, Irbid, JordanCorrespondence[email protected]
View further author information
,
Nabil Ardah2 Faculty of Medicine, Jordan University of Science and Technology, Irbid, JordanView further author information
,
Ayah Al-Bzour2 Faculty of Medicine, Jordan University of Science and Technology, Irbid, JordanORCID Iconhttps://orcid.org/0000-0001-8216-0400View further author information
ORCID Icon,
Layan Zeidat2 Faculty of Medicine, Jordan University of Science and Technology, Irbid, JordanView further author information
,
Zahraa Mawali2 Faculty of Medicine, Jordan University of Science and Technology, Irbid, JordanView further author information
,
Yaman B Ahmed2 Faculty of Medicine, Jordan University of Science and Technology, Irbid, JordanView further author information
,
Tha’er Abdulkareem Alguzo1 Department of General Surgery and Urology, King Abdullah University Hospital, Irbid, JordanView further author information
,
Azhar Mohanad Al-Alwani1 Department of General Surgery and Urology, King Abdullah University Hospital, Irbid, JordanView further author information
&
Mahmoud Samara1 Department of General Surgery and Urology, King Abdullah University Hospital, Irbid, JordanView further author information
 show all
Pages 197-206 | Received 24 Jun 2023, Accepted 06 Sep 2023, Published online: 11 Sep 2023

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. doi:10.1007/s00345-017-2008-6
  • Abboud IA. Prevalence of Urolithiasis in Adults due to Environmental Influences: a Case Study from Northern and Central Jordan. Jordan J Earth Environ Sci. 2018;9(1):29–38.
  • Ganpule AP, Vijayakumar M, Malpani A, Desai MR. Percutaneous nephrolithotomy (PCNL) a critical review. Int J Surgery. 2016;36(PD):660–664. doi:10.1016/j.ijsu.2016.11.028
  • Kumar U, Tomar V, Yadav SS, et al. STONE score versus Guy’s Stone Score - Prospective comparative evaluation for success rate and complications in percutaneous nephrolithotomy. Urol Ann. 2018;10(1):76–81. doi:10.4103/UA.UA_119_17
  • Wu WJ, Okeke Z. Current clinical scoring systems of percutaneous nephrolithotomy outcomes. Nat Rev Urol. 2017;14(8):459–469. doi:10.1038/nrurol.2017.71
  • Zhernovoi I, Shchukin D, Jundi M, Grabs D, Maranzano J, Nayouf A. Comparison of four transdiaphragmatic approaches to remove cavoatrial tumor thrombi: a pilot study. Cent European J Urol. 2022;75(2):145–152. doi:10.5173/ceju.2022.0277.R1
  • Jiang K, Sun F, Zhu J, et al. Evaluation of three stone-scoring systems for predicting SFR and complications after percutaneous nephrolithotomy: a systematic review and meta-analysis. BMC Urol. 2019;19:1. doi:10.1186/s12894-019-0488-y
  • Zhao H, Li W, Li J, Li L, Wang H, Guo J. Predicting the Stone-Free Status of Percutaneous Nephrolithotomy With the Machine Learning System: comparative Analysis With Guy’s Stone Score and the S.T.O.N.E Score System. Front Mol Biosci. 2022;9. doi:10.3389/fmolb.2022.880291
  • Aminsharifi A, Irani D, Pooyesh S, et al. Artificial Neural Network System to Predict the Postoperative Outcome of Percutaneous Nephrolithotomy. J Endourol. 2017;31(5):461–467. doi:10.1089/end.2016.0791
  • Shabaniyan T, Parsaei H, Aminsharifi A, et al. An artificial intelligence-based clinical decision support system for large kidney stone treatment. Australas Phys Eng Sci Med. 2019;42(3):771–779. doi:10.1007/s13246-019-00780-3
  • Aminsharifi A, Irani D, Tayebi S, Jafari Kafash T, Shabanian T, Parsaei H. Predicting the Postoperative Outcome of Percutaneous Nephrolithotomy with Machine Learning System: software Validation and Comparative Analysis with Guy’s Stone Score and the CROES Nomogram. J Endourol. 2020;34(6):692–699. doi:10.1089/end.2019.0475
  • Hameed BMZ, Shah M, Naik N, Singh Khanuja H, Paul R, Somani BK. Application of Artificial Intelligence-Based Classifiers to Predict the Outcome Measures and Stone-Free Status Following Percutaneous Nephrolithotomy for Staghorn Calculi: cross-Validation of Data and Estimation of Accuracy. J Endourol. 2021;35(9):1307–1313. doi:10.1089/end.2020.1136
  • Collins GS, Reitsma JB, Altman DG, Moons K. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med. 2015;13(1):1. doi:10.1186/s12916-014-0241-z
  • TIBCO Software. What is a Random Forest? Available from: https://www.tibco.com/reference-center/what-is-a-random-forest. Accessed August 19, 2023.
  • Pushkar Mandot. How exactly XGBoost Works? 2019. Available from: https://medium.com/@pushkarmandot/how-exactly-xgboost-works-a320d9b8aeef. Accessed August 19, 2023.
  • Ziemba JB, Matlaga BR. Epidemiology and economics of nephrolithiasis. Investig Clin Urol. 2017;58(5):299–306. doi:10.4111/icu.2017.58.5.299
  • Rais-Bahrami S, Friedlander JI, Duty BD, Okeke Z, Smith AD. Difficulties with access in percutaneous renal surgery. Ther Adv Urol. 2011;3(2):59–68. doi:10.1177/1756287211400661
  • Thomas K, Smith NC, Hegarty N, Glass JM. The Guy’s Stone Score—Grading the Complexity of Percutaneous Nephrolithotomy Procedures. Urology. 2011;78(2):277–281. doi:10.1016/j.urology.2010.12.026
  • Noureldin YA, Elkoushy MA, Andonian S. External validation of the S.T.O.N.E. nephrolithometry scoring system. J Canadian Urol Assoc. 2015;9(6):190–195. doi:10.5489/cuaj.2652
  • Ingimarsson JP, Dagrosa LM, Hyams ES, Pais VM. External validation of a preoperative renal stone grading system: reproducibility and inter-rater concordance of the Guy’s stone score using preoperative computed tomography and rigorous postoperative stone-free criteria. Urology. 2014;83(1):45–49. doi:10.1016/j.urology.2013.09.008
  • Smith A, Averch TD, Shahrour K, et al. A nephrolithometric nomogram to predict treatment success of percutaneous nephrolithotomy. J Urol. 2013;190(1):149–156. doi:10.1016/j.juro.2013.01.047
  • Srivastava A, Yadav P, Madhavan K, et al. Inter-observer variability amongst surgeons and radiologists in assessment of Guy’s Stone Score and S.T.O.N.E. nephrolithometry score: a prospective evaluation. Arab J Urol. 2020;18(2):118–123. doi:10.1080/2090598X.2019.1703278

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.