Personalized Prediction of Patient Radiation Exposure for Therapy of Urolithiasis: An Application and Comparison of Six Machine Learning Algorithms

Author:

Huettenbrink Clemens1ORCID,Hitzl Wolfgang234ORCID,Distler Florian1ORCID,Ell Jascha1,Ammon Josefin5,Pahernik Sascha1

Affiliation:

1. Department of Urology, Nuremberg General Hospital, Paracelsus Medical University, 90419 Nuremberg, Germany

2. Team Biostatistics and Publication of Clinical, Research and Innovation Management (RIM), Trial Studies, Paracelsus Medical University, 5020 Salzburg, Austria

3. Department of Ophthalmology and Optometry, Paracelsus Medical University Salzburg, 5020 Salzburg, Austria

4. Research Program Experimental Ophthalmology and Glaucoma Research, Paracelsus Medical University, 5020 Salzburg, Austria

5. Institute of Medical Physics, Nuremberg General Hospital, Paracelsus Medical University, 90419 Nuremberg, Germany

Abstract

The prediction of radiation exposure is an important tool for the choice of therapy modality and becomes, as a component of patient-informed consent, increasingly important for both surgeon and patient. The final goal is the implementation of a trained and tested machine learning model in a real-time computer system allowing the surgeon and patient to better assess patient’s personal radiation risk. In summary, 995 patients with ureterorenoscopy over a period from May 2016 to December 2019 were included. According to the suggestions based on actual literature evidence, dose area product (DAP) was categorized into ‘low doses’ ≤ 2.8 Gy·cm2 and ‘high doses’ > 2.8 Gy·cm2 for ureterorenoscopy (URS). To forecast the level of radiation exposure during treatment, six different machine learning models were trained, and 10-fold crossvalidated and their model performances evaluated in training and independent test samples. The negative predictive value for low DAP during ureterorenoscopy was 94% (95% CI: 92–96%). Factors influencing the radiation exposure were: age (p = 0.0002), gender (p = 0.011), weight (p < 0.0001), stone size (p < 0.000001), surgeon experience (p = 0.039), number of stones (p = 0.0007), stone density (p = 0.023), use of flexible endoscope (p < 0.0001) and preoperative stone position (p < 0.00001). The machine learning algorithm identified a subgroup of patients of 81% of the total sample, for which highly accurate predictions (94%) were possible allowing the surgeon to assess patient’s personal radiation risk. Patients without prediction (19%), the medical expert can make decisions as usual. Next step will be the implementation of the trained model in real-time computer systems for clinical decision processes in daily practice.

Publisher

MDPI AG

Subject

Medicine (miscellaneous)

Reference31 articles.

1. Burden of Urolithiasis: Trends in Prevalence, Treatments, and Costs;Raheem;Eur. Urol. Focus,2017

2. EAU Guidelines (2022). Edn. Presented at the EAU Annual Congress Amsterdam, EAU Guidelines Office.

3. (2018). S2k Guideline on Diagnosis, Therapy and Metaphylaxis of Urolithiasis—Version, German Society for Urology eV.

4. Radiation Exposure during the Evaluation and Management of Nephrolithiasis;Chen;J. Urol.,2015

5. Prospective randomized comparison between fluoros-copy-guided ureteroscopy versus ureteroscopy with real-time ultrasonography for the management of ureteral stones;Singh;Urol. Ann.,2016

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