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Accurate prediction of pure uric acid urinary stones in clinical context via a combination of radiomics and machine learning

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World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

Oral chemolysis is an effective and non-invasive treatment for uric acid urinary stones. This study aimed to classify urinary stones into either pure uric acid (pUA) or other composition (Others) using non-contrast-enhanced computed tomography scans (NCCTs).

Methods

Instances managed at our institution from 2019 to 2021 were screened. They were labeled as either pUA or Others based upon composition analyses, and randomly split into training or testing data set. Several instances contained multiple NCCTs which were all collected. In each of NCCTs, individual urinary stone was treated as individual sample. From manually drawn volumes of interest, we extracted original and wavelet radiomics features for each sample. The most important features were then selected via the Least Absolute Shrinkage and Selection Operator for building the final model on a Support Vector Machine. Performance on the testing set was evaluated via accuracy, sensitivity, specificity, and area under the precision–recall curve (AUPRC).

Results

There were 302 instances, of which 118 had pUA urinary stones, generating 576 samples in total. From 851 original and wavelet radiomics features extracted for each sample, 10 most important features were ultimately selected. On the testing data set, accuracy, sensitivity, specificity, and AUPRC were 93.9%, 97.9%, 92.2%, and 0.958, respectively, for per-sample prediction, and 90.8%, 100%, 87.5%, and 0.902, respectively, for per-instance prediction.

Conclusion

The machine learning algorithm trained with radiomics features from NCCTs can accurately predict pUA urinary stones. Our work suggests a potential assisting tool for stone disease treatment selection.

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Data availability

Data are available for bona fide researchers who request it from the authors.

Abbreviations

95CI:

95% Confidence interval

AUPRC:

Area under the precision–recall curve

AUROC:

Area under the receiver operating characteristic curve

CT:

Computed tomography

CV:

Cross validation

DECT:

Dual-energy computed tomography

FTIR:

Fourier transform infrared spectroscopy

HU:

Hounsfield unit

LASSO:

Least absolute shrinkage and selection operator

NCCT:

Non-contrast-enhanced computed tomography

PRC:

Precision–recall curve

ppLapl:

Peak point Laplacian

PPV:

Positive predictive value

pUA:

Pure uric acid

VOI:

Volume of interest

SECT:

Single-energy computed tomography

SVM:

Support vector machine

UA:

Uric acid

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Acknowledgements

This study was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant funded by the Korea government (MSIT) and grants from the Ministry of Education, Republic of Korea (NRF-2022R1I1A3072856 to Ilwoo Park and NRF-2021R1I1A3060723 to Byung H. Baek) and Chonnam National University Hospital Biomedical Research Institute (BCRI22037 to Ilwoo Park).

Funding

This study was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) Grant funded by the Korea government (MSIT) and grants from the Ministry of Education, Republic of Korea (NRF-2022R1I1A3072856 to Ilwoo Park and NRF-2021R1I1A3060723 to Byung H. Baek) and Chonnam National University Hospital Biomedical Research Institute (BCRI22037 to Ilwoo Park).

Author information

Author notes

  1. Kyung-Jin Oh and Ilwoo Park have contributed equally to this work and share the corresponding authorship.

Authors and Affiliations

  1. Department of Biomedical Sciences, Chonnam National University Medical School, Gwangju, Korea

    Binh D. Le

  2. Department of Urology, Saint Paul Hospital, Hanoi, Vietnam

    Binh D. Le

  3. Department of Radiology, Chonnam National University Hospital, Gwangju, Korea

    Tien A. Nguyen

  4. Department of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Korea

    Byung H. Baek & Ilwoo Park

  5. Department of Urology, Chonnam National University Medical School and Hospital, Gwangju, Korea

    Kyung-Jin Oh

  6. Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea

    Ilwoo Park

  7. Department of Data Science, Chonnam National University, Gwangju, Korea

    Ilwoo Park

Authors
  1. Binh D. Le
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  2. Tien A. Nguyen
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Contributions

BD Le: conceptualization, methodology, formal analysis, writing—original draft, review and editing. AT Nguyen: methodology, formal analysis. BH Baek: supervision, funding acquisition. KJ Oh: resources, conceptualization, methodology, supervision, funding acquisition, writing—review and editing. I Park: conceptualization, methodology, supervision, funding acquisition, project administration, writing—original draft, review and editing.

Corresponding authors

Correspondence to Kyung-Jin Oh or Ilwoo Park.

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Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Ethics approval

Approval was obtained from the institutional review board (IRB No. 2022–434) of Chonnam National University Hospital. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Research involving human participants and/or animals

No animals or human participants were required for this study.

Informed consent

Informed consent was waived by the institutional review board as this study is a retrospective analysis of imaging.

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Le, B.D., Nguyen, T.A., Baek, B.H. et al. Accurate prediction of pure uric acid urinary stones in clinical context via a combination of radiomics and machine learning. World J Urol 42, 150 (2024). https://doi.org/10.1007/s00345-024-04818-4

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