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A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using
Junjiong Zheng1, Hao Yu1, Jesur Batur2
1Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Kidney International
|June 15, 2021
Summary
A new radiomic model can non-invasively identify urinary infection stones before surgery. This tool aids in managing urolithiasis, potentially improving patient outcomes by distinguishing infection stones from others.
Area of Science:
- Urology
- Medical Imaging
- Data Science
Background:
- Urolithiasis management varies by stone type, but accurate composition analysis requires in vitro testing.
- Identifying infection stones preoperatively remains a significant clinical challenge.
Purpose of the Study:
- To develop and validate a radiomic model for non-invasive, preoperative identification of urinary infection stones.
Main Methods:
- A multicenter study involving 1198 urolithiasis patients.
- Extraction of 1316 radiomic features from Computer Tomography (CT) images.
- Development of a radiomic model using a radiomic signature, urine parameters, and logistic regression.
Main Results:
- A radiomic signature was identified and validated across training and external datasets.
- The final radiomic model demonstrated favorable discrimination (AUCs ranging from 0.812 to 0.898).
- Decision curve analysis confirmed the clinical utility of the model.
Conclusions:
- The proposed radiomic model offers a non-invasive in vivo tool for identifying urinary infection stones.
- This may optimize treatment strategies for urolithiasis and improve patient prognosis.
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