Characterizing T2 iso- and hypo-intense renal masses on MRI: Can templated algorithms improve accuracy? Academic Article uri icon

Overview

abstract

  • PURPOSE: To assess if a templated algorithm can improve the diagnostic performance of MRI for characterization of T2 isointense and hypointense renal masses. METHODS: In this retrospective study, 60 renal masses with histopathologic diagnoses that were also confirmed as T2 iso- or hypointense on MRI were identified (mean ± standard deviation, range: 3.9 ± 2.5, 1.0-13.7 cm). Two semi-quantitative diagnostic algorithms were created based on MRI features of renal masses reported in the literature. Three body-MRI trained radiologists provided clinical diagnoses based on their experience and separately provided semiquantitative data for each components of the two algorithms. The algorithms were applied separately by a radiology trainee without additional interpretive input. Logistic regression was used to compare the accuracy of the three methods in distinguishing malignant versus benign lesions and in diagnosing the exact histopathology. Inter-reader agreement for each method was calculated using Fleiss' kappa statistics. RESULTS: The accuracy of the two algorithms and clinical experience were similar (70%, 69%, and 64%, respectively, p = 0.22-0.32), with fair to moderate inter-reader agreement (Fleiss's kappa: r = 0.375, r = 0.308, r = 0.375, respectively, all p < 0.0001). The accuracy of the two algorithms and clinical experience in diagnosing specific histopathology were also no different from each other (34%, 29%, and 32%, respectively, p = 0.49-0.74), with fair to moderate inter-reader agreement (Fleiss's kappa: r = 0.20, r = 0.28, r = 0.375, respectively, all p < 0.0001). CONCLUSION: Semi-quantitative templated algorithms based on MRI features of renal masses did not improve the ability to diagnose T2 iso- and hypointense renal masses when compared to unassisted interpretation by body MR trained subspecialists.

authors

  • Xu, Helen
  • Balcacer, Patricia
  • Zhang, Zheng
  • Zhang, Liang
  • Yee, Eric U
  • Sun, Maryellen R
  • Tsai, Leo L

publication date

  • November 5, 2020

Research

keywords

  • Carcinoma, Renal Cell
  • Kidney Neoplasms

Identity

Scopus Document Identifier

  • 85096196979

Digital Object Identifier (DOI)

  • 10.1016/j.clinimag.2020.10.051

PubMed ID

  • 33217669

Additional Document Info

volume

  • 72