Skip to main content

Advertisement

SpringerLink
Log in

AI prediction of extracorporeal shock wave lithotripsy outcomes for ureteral stones by machine learning-based analysis with a variety of stone and patient characteristics

  • Research
  • Published:
Urolithiasis Aims and scope Submit manuscript

Abstract

We propose an artificial intelligence prediction method for extracorporeal shock wave lithotripsy treatment outcomes by analysis of a wide variety of variables. We retrospectively reviewed the records of 171 patients from between January 2009 and November 2019 that underwent shock wave lithotripsy at Wakayama Medical University, Japan, for ureteral stones shown on preoperative non-contrast computed tomography. This prediction method consisted of stone area extraction, stone analyzing factor extraction from non-contrast computed tomography images, and shock wave lithotripsy treatment result prediction by a non-linear support vector machine for analysis of 15 input and automatic measurement factors. Input factors included patient age, skin-to-stone distance, and maximum ureteral wall thickness, and the automatic measurement factors included 11 non-contrast computed tomography image texture factors in the stone area and stone volume. Permutation feature importance was also applied to the artificial intelligence prediction results to analyze the importance of each factor relating to estimate decision grounds. The prediction performance was evaluated by five-fold cross-validation, it obtained 0.742 of the mean area under the receiver operating characteristic curve. The proposed method is shown by these results to have robust data diversity and effective clinical application. As a result of permutation feature importance, some factors that showed high p-values in the significant difference tests were thought to have a high contribution to the proposed prediction method. Future issues include validation using a larger volume of high-resolution clinical non-contrast computed tomography image data and the application of deep learning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

The raw data of the results analyzed during this study are available from the corresponding author or at reasonable request. The patient dataset, including the CT image dataset, is private and not publicly available due to the protection of personal information.

Abbreviations

AI:

Artificial intelligence

AUC:

Area under the ROC curve

CV:

Cross-validation

GCFs:

Gradient concentration factors

IQR:

Interquartile range

MSD:

Mean stone density

NCCT:

Non-contrast computed tomography

PCNL:

Percutaneous nephrolithotomy

PFI:

Permutation feature importance

ROC:

Receiver-operating characteristic

SD:

Standard deviation

SSD:

Skin-to-stone distance

SVM:

Support vector machine

SWL:

Shock wave lithotripsy

URS:

Ureteroscopy

UWT:

Ureteral wall thickness

UWV:

Ureteral wall volume

VCSD:

Variation coefficient of stone density

References

  1. Drake T, Grivas N, Dabestani S, Knoll T, Lam T, Maclennan S, Petrik A, Skolarikos A, Straub M, Tuerk C, Yuan CY, Sarica K (2017) What are the benefits and harms of ureteroscopy compared with shock-wave lithotripsy in the treatment of upper ureteral stones a systematic review. Eur Urol 72:772–786. https://doi.org/10.1016/j.eururo.2017.04.016

    Article  PubMed  Google Scholar 

  2. Preminger GM (2006) Management of lower pole renal calculi: shock wave lithotripsy versus percutaneous nephrolithotomy versus flexible ureteroscopy. Urol Res 34:108–111. https://doi.org/10.1007/s00240-005-0020-6

    Article  PubMed  Google Scholar 

  3. Chongruksut W, Lojanapiwat B, Ayudhya VC, Tawichasri C, Patumanond J, Paichitvichean S (2011) Prognostic factors for success in treating kidney stones by extracorporeal shock wave lithotripsy. J Med Assoc Thai 94:331–336

    PubMed  Google Scholar 

  4. Wiesenthal JD, Ghiculete D, D’A Honey RJ, Pace KT, (2010) 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 38:307–313. https://doi.org/10.1007/s00240-010-0295-0

    Article  PubMed  Google Scholar 

  5. Yamashita S, Kohjimoto Y, Iwahashi Y, Iguchi T, Nishizawa S, Kikkawa K, Hara I (2018) Noncontrast computed tomography parameters for predicting shock wave lithotripsy outcome in upper urinary tract stone cases. Biomed Res Int 2018:9253952. https://doi.org/10.1155/2018/9253952

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lee JY, Kim JH, Kang DH, Chung DY, Lee DH, Do Jung H, Kwon JK, Cho KS (2016) Stone heterogeneity index as the standard deviation of Hounsfield units: a novel predictor for shock-wave lithotripsy outcomes in ureter calculi. Sci Rep 6:23988. https://doi.org/10.1038/srep23988

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yamashita S, Kohjimoto Y, Iguchi T, Nishizawa S, Iba A, Kikkawa K, Hara I (2017) Variation coefficient of stone density: a novel predictor of the outcome of extracorporeal shockwave lithotripsy. J Endourol 31:384–390. https://doi.org/10.1089/end.2016.0719

    Article  PubMed  Google Scholar 

  8. Yamashita S, Kohjimoto Y, Iguchi T, Nishizawa S, Kikkawa K, Hara I (2019) Ureteral wall volume at ureteral stone site is a critical predictor for shock wave lithotripsy outcomes: comparison with ureteral wall thickness and area. Urolithiasis 48(4):361–368. https://doi.org/10.1007/s00240-019-01154-w

    Article  CAS  PubMed  Google Scholar 

  9. Türk C, Petřík A, Sarica K, Seitz C, Skolarikos A, Straub M, Knoll T (2016) EAU guidelines on interventional treatment for urolithiasis. Eur Urol 69:475–482. https://doi.org/10.1016/j.eururo.2015.07.041

    Article  PubMed  Google Scholar 

  10. Assimos D, Krambeck A, Miller NL, Monga M, Murad MH, Nelson CP, Pace KT, Pais VM Jr, Pearle MS, Preminger GM, Razvi H, Shah O, Matlaga BR (2016) Surgical management of stones: American Urological Association/Endourological Society Guideline, PART I. J Urol 196:1153–1160. https://doi.org/10.1016/j.juro.2016.05.090

    Article  PubMed  Google Scholar 

  11. Mannil M, von Spiczak J, Hermanns T, Poyet C, Alkadhi H, Fankhauser CD (2018) Three-dimensional texture analysis with machine learning provides incremental predictive information for successful shock wave lithotripsy in patients with kidney stones. J Urol 200:829–836. https://doi.org/10.1016/j.juro.2018.04.059

    Article  PubMed  Google Scholar 

  12. Fisher A, Rudin C, Dominici F (2019) All models are wrong, but many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously. J Mach Learn Res 20:177

    PubMed  PubMed Central  Google Scholar 

  13. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167. https://doi.org/10.1023/A:1009715923555

    Article  Google Scholar 

  14. Kumar Y, Koul A, Singla R, Ijaz MF (2023) Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Human Comput 14:8459–8486. https://doi.org/10.1007/s12652-021-03612-z

    Article  Google Scholar 

  15. Yasui T, Iguchi M, Suzuki S, Kohri K (2008) Prevalence and epidemiological characteristics of urolithiasis in Japan: national trends between 1965 and 2005. Urology 71:209–213. https://doi.org/10.1016/j.urology.2007.09.034

    Article  PubMed  Google Scholar 

  16. Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 13:252–264. https://doi.org/10.1109/34.75512

    Article  Google Scholar 

Download references

Acknowledgements

This document was proof-read and edited by Benjamin Phillis at the Clinical Study Support Center, Wakayama Medical University.

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Graduate School of Biology-Oriented Science and Technology, Kindai University, Kinokawa City, Japan

    Yukako Nakamae & Mitsutaka Nemoto

  2. Faculty of Biology-Oriented Science and Technology, Kindai University, 930 Nishimitani, Kinokawa City, Wakayama, 649-6493, Japan

    Mitsutaka Nemoto

  3. Faculty of Informatics, Kindai University, Higashiosaka, Japan

    Yuichi Kimura

  4. Department of Urology, Wakayama Medical University, Wakayama City, Japan

    Ryusuke Deguchi, Shimpei Yamashita, Yasuo Kohjimoto & Isao Hara

Authors
  1. Yukako Nakamae
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryusuke Deguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mitsutaka Nemoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuichi Kimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shimpei Yamashita
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yasuo Kohjimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Isao Hara
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.Nakamae. were responsible for all the procedures, feature extraction system construction, analysis, and writing of the manuscript. U.Kimura., and M.Nemoto. contributed for methodology creation and refinement. R.Deguchi., S.Yamashita., Y.Kohjimoto., and I.Hara. contributed project administration, clinical data collection, and lesion label annotation. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Mitsutaka Nemoto.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

All procedures were carried out in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration. This study was approved by Wakayama Medical University Ethics Committee (No. 2803).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 24 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nakamae, Y., Deguchi, R., Nemoto, M. et al. AI prediction of extracorporeal shock wave lithotripsy outcomes for ureteral stones by machine learning-based analysis with a variety of stone and patient characteristics. Urolithiasis 52, 9 (2024). https://doi.org/10.1007/s00240-023-01506-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00240-023-01506-7

Keywords

Search

Navigation