Literature DB >> 25082480

Assessment of colorectal liver metastases using MRI and CT: impact of observer experience on diagnostic performance and inter-observer reproducibility with histopathological correlation.

Moritz H Albrecht1, Julian L Wichmann2, Cindy Müller2, Theresa Schreckenbach3, Sreekanth Sakthibalan4, Renate Hammerstingl2, Wolf O Bechstein3, Stephan Zangos2, Hanns Ackermann5, Thomas J Vogl2.   

Abstract

INTRODUCTION: To compare the diagnostic performance and inter-observer reproducibility of CT and MRI in detecting colorectal liver metastases (CRLM) of observers with different levels of experience.
MATERIALS AND METHODS: Data from 51 CT and 54 MRI examinations of 105 patients with CRLM were analysed. Intraoperative and histopathological findings served as the reference standard. Analyses were performed by four observers with varying levels of experience regarding imaging of CRLM (reviewers A, B, C and D with respectively >20, >5, <1 and 0 years of experience). Per-segment sensitivity, specificity, Cohen's kappa (κ) for diagnosed segments and Intra-class Correlation Coefficients (ICC) for reported number of lesions were calculated.
RESULTS: CT sensitivity and specificity was for reviewer A 89.71%/94.41%, B 78.50%/88.37%, C 63.55%/85.58%, D 84.11%/78.60% and regarding MRI A 90.40%/95.43%, B 74.40%/90.04%, C 60.00%/85.89% and D 65.60%/75.90%. The overall inter-observer agreement was higher for CT (κ=0.43, p<0.001; ICC=0.75, p<0.001) than MRI (κ=0.38, p<0.001; ICC=0.65, p<0.001). The experienced reviewers A and B achieved better agreement for MRI (κ=0.54, p<0.001; ICC=0.77, p<0.001) than CT (κ=0.52, p<0.00; ICC=0.76, p<0.001) unlike the less experienced C and D (MRI κ=0.38, ICC=0.63 and CT κ=0.41, ICC=0.74, respectively, p<0.001).
CONCLUSIONS: The proficiency in detection of CRLM is significantly influenced by observer experience, although CT interpretation is less affected than MRI analysis.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  CT; Diagnostic performance; Experience; Liver imaging; MRI

Mesh:

Year:  2014        PMID: 25082480     DOI: 10.1016/j.ejrad.2014.07.005

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  1 in total

1.  Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases.

Authors:  Eugene Vorontsov; Milena Cerny; Philippe Régnier; Lisa Di Jorio; Christopher J Pal; Réal Lapointe; Franck Vandenbroucke-Menu; Simon Turcotte; Samuel Kadoury; An Tang
Journal:  Radiol Artif Intell       Date:  2019-03-13
  1 in total

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