advertisement
Abstract #108497 Published in IGR 23-4
Personalized Prediction of Patient Radiation Exposure for Therapy of Urolithiasis: An Application and Comparison of Six Machine Learning Algorithms
Huettenbrink C; Hitzl W; Distler F; Ell J; Ammon J; Pahernik SJournal of personalized medicine 2023; 13:
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·cm and 'high doses' > 2.8 Gy·cm 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 ( = 0.0002), gender ( = 0.011), weight ( < 0.0001), stone size ( < 0.000001), surgeon experience ( = 0.039), number of stones ( = 0.0007), stone density ( = 0.023), use of flexible endoscope ( < 0.0001) and preoperative stone position ( < 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.
Department of Urology, Nuremberg General Hospital, Paracelsus Medical University, 90419 Nuremberg, Germany.
Full article