Development of a radiomics model to discriminate ammonium urate stones from uric acid stones in vivo : A remedy for the diagnostic pitfall of dual-energy computed tomography

• Published in: Chinese medical journal Vol. 137; no. 9; pp. 1095 - 1104
• Main Authors: Zheng, Junjiong, Zhang, Jie, Cai, Jinhua, Yao, Yuhui, Lu, Sihong, Wu, Zhuo, Cai, Zhaoxi, Tuerxun, Aierken, Batur, Jesur, Huang, Jian, Kong, Jianqiu, Lin, Tianxin
• Format: Journal Article
• Language: English
• Published: China Lippincott Williams & Wilkins Ovid Technologies 05-05-2024; Lippincott Williams & Wilkins; Wolters Kluwer
• Subjects: Adult; Aged; Algorithms; Female; Fourier transforms; Hospitals; Humans; Male; Medical diagnosis; Middle Aged; Original; Patients; Performance evaluation; Radiomics; Regression analysis; ROC Curve; Software; Spectrum analysis; Surgery; Tomography; Tomography, X-Ray Computed - methods; Uric acid; Uric Acid - analysis; Urinalysis; Urinary Calculi - chemistry; Urinary Calculi - diagnostic imaging; Urinary tract diseases; Urine; Urolithiasis - diagnosis; Urolithiasis - diagnostic imaging
• ISSN: 0366-6999; 2542-5641; 2542-5641
• DOI: 10.1097/CM9.0000000000002866

Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them.
This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated.
When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899-0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796-0.995) and 0.870 (95% CI, 0.769-0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model.
DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo .