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

Payam Mohammadinejad, Andrea Ferrero, David J. Bartlett, Ashish Khandelwal, Roy Marcus, John C. Lieske, Taylor R. Moen, Kristin C. Mara, Felicity T. Enders, Cynthia H. McCollough, Joel G. Fletcher

Research output: Contribution to journalArticlepeer-review

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 needed to predict clinical impacts of reporting the likelihood of urinary stone passage and improving inter-observer variation using automatic radiomic analysis software.

Original languageEnglish (US)
JournalEmergency Radiology
DOIs
StateAccepted/In press - 2021

Keywords

  • Kidney calculi
  • Radiomics
  • Renal colic
  • Tomography
  • X-Ray computed

ASJC Scopus subject areas

  • Emergency Medicine
  • Radiology Nuclear Medicine and imaging

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