Semi-automatic software increases CT measurement accuracy but not response classification of colorectal liver metastases after chemotherapy

Eur J Radiol. 2012 Oct;81(10):2543-9. doi: 10.1016/j.ejrad.2011.12.026. Epub 2012 Jan 20.

Abstract

Objectives: This study evaluates intra- and interobserver variability of automatic diameter and volume measurements of colorectal liver metastases (CRLM) before and after chemotherapy and its influence on response classification.

Methods: Pre-and post-chemotherapy CT-scans of 33 patients with 138 CRLM were evaluated. Two observers measured all metastases three times on pre-and post-chemotherapy CT-scans, using three different techniques: manual diameter (MD), automatic diameter (AD) and automatic volume (AV). RECIST 1.0 criteria were used to define response classification. For each technique, we assessed intra- and interobserver reliability by determining the intraclass correlation coefficient (α-level 0.05). Intra-observer agreement was estimated by the variance coefficient (%). For inter-observer agreement the relative measurement error (%) was calculated using Bland-Altman analysis. In addition, we compared agreement in response classification by calculating kappa-scores (κ) and estimating proportions of discordance between methods (%).

Results: Intra-observer variability was 6.05%, 4.28% and 12.72% for MD, AD and AV, respectively. Inter-observer variability was 4.23%, 2.02% and 14.86% for MD, AD and AV, respectively. Chemotherapy marginally affected these estimates. Agreement in response classification did not improve using AD or AV (MD κ=0.653, AD κ=0.548, AV κ=0.548) and substantial discordance between observers was observed with all three methods (MD 17.8%, AD 22.2%, AV 22.2%).

Conclusion: Semi-automatic software allows repeatable and reproducible measurement of both diameter and volume measurements of CRLM, but does not reduce variability in response classification.

MeSH terms

  • Adult
  • Aged
  • Antineoplastic Agents / therapeutic use*
  • Colorectal Neoplasms / diagnostic imaging*
  • Colorectal Neoplasms / drug therapy*
  • Humans
  • Liver Neoplasms* / diagnostic imaging
  • Liver Neoplasms* / drug therapy
  • Liver Neoplasms* / secondary
  • Middle Aged
  • Outcome Assessment, Health Care / methods
  • Pattern Recognition, Automated / methods*
  • Prognosis
  • Radiographic Image Interpretation, Computer-Assisted / methods
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity
  • Software
  • Tomography, X-Ray Computed / methods*
  • Treatment Outcome

Substances

  • Antineoplastic Agents