Automated radiomic analysis of CT images to predict likelihood of spontaneous passage of symptomatic renal stones

Abstract

Purpose-To evaluate the ability of a semi-automated radiomic analysis software in predicting the likelihood of spontaneous passage of urinary stones compared with manual measurements.Methods-Symptomatic patients visiting the emergency department with suspected stones in either kidney or ureters who underwent a CT scan were included. Patients were followed for up to 6 months for the outcome of a trial of passage. Maximum stone diameters in axial and coronal images were measured manually. Stone length, width, height, max diameter, volume, the mean and standard deviation of the Hounsfield units, and morphologic features were also measured using automated radiomic analysis software. Multivariate models were developed using these data to predict subsequent spontaneous stone passage, with results expressed as the area under a receiver operating curve (AUC).Results-One hundred eighty-four patients (69 females) with a median age of 56 years were included. Spontaneous stone passage occurred in 114 patients (62%). Univariate analysis demonstrated an AUC of 0.83 and 0.82 for the maximum stone diameter determined manually in the axial and coronal planes, respectively. Multivariate models demonstrated an AUC of 0.82 for a model including manual measurement of maximum stone diameter in axial and coronal planes. The same AUC was found for a model including automatic measurement of maximum height and diameter of the stone. Further addition of morphological parameters measured automatically did not increase AUC beyond 0.83. Conclusion-The semi-automated radiomic analysis of urinary stones shows similar accuracy compared with manual measurements for predicting urinary stone passage. Further studies are

Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions

“…Mohammadinejad et al compared the ability of a semi-automated radiomics analysis software in predicting the likelihood of spontaneous stone passage with manual measurements. Stone characteristics including length, width, height, maximal diameter, volume, the mean and standard deviation of the Hounsfield units, and morphologic features were extracted from CT images using automated radiomics analysis software [ 26 ]. Univariate analysis and multivariate analysis showed AUC of 0.82 and 0.83, respectively, for maximum stone diameter measured manually.…”

Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith

“…The combination of GLCM_inverse difference moment normalized, NGTDM_exponential of coarseness and GLRLM_3D_log_sigma of short-run low gray-level emphasis showed better diagnostic performance (AUC = 0.9 and 95% CI = 0.85–0.93). Furthermore, Mohammadinejad et al 4 has reported that the semiautomated radiomic analysis of urinary stones is able to provide similar accuracy compared with manual measurements for predicting urinary stone passage. Therefore, studies in this line of research present the potential to, conservatively, improve the quality of life of patients with calculus.…”

“…Ureteral calculus is one of the most common diseases in the urinary system and is also a common disease-causing acute abdomen clinically, with a high incidence and recurrence rate, which adversely afects human health and life [1][2][3][4][5]. Patients with ureteral calculus need timely intervention and treatment.…”

“…Currently, the radiomics model is being widely applied for medical imaging and is included in studying the images obtained by various examination equipment and various diseases, including those of tumors, such as malignancy evaluation, histological classifcation and grading [8,[11][12][13][14][15], wettability prediction, efcacy evaluation, and prognosis prediction [16][17][18]. Additionally, radiomics has shown promising results and a clinical application potential in predicting the prognosis of COVID-19 pneumonia patients [19], calculus component analysis, and evaluation of treatment efects [1,3,4,20,21]. In this study, we analyzed the features of CT image texture parameters between ureteral calculus and phlebolith through radiomics.…”

Imaging in stone diagnosis and surgical planning

“…Stone volume has been used to predict stone behavior with some success according to recent investigations. Three studies in the past 2 years have evaluated machine learning models to predict spontaneous ureteral stone passage based on volume, with areas under the curve of 0.83–0.85 [91,93,94 ▪ ]. This may also be applied to predict surgical efficacy.…”