Comparison of SUVmax and SUVpeak based segmentation to determine primary lung tumour volume on FDG PET-CT correlated with pathology data

Research output: Contribution to journalArticle

  • External authors:
  • Susan Mercieca
  • José Belderbos
  • Judith Van Loon
  • Kenneth Gilhuijs
  • Peter Julyan

Abstract

Purpose

The aim of the study was to compare simple SUVmax and SUVpeak based segmentation methods for calculating the lung tumour volume, compared to a pathology ground truth.

Methods

Thirty patients diagnosed with early stage Non-Small Cell lung cancer (NSCLC) underwent surgical resection in the Netherlands between 2006 and 2008. FDG PET-CT scans for these patients were acquired within a median of 20 days before surgery. The tumour volume for each percentage SUVmax and SUVpeak threshold, with and without background correction, was calculated for each patient. The percentage threshold that provided the tumour volume that corresponded best with the pathology volume was considered to be the optimal threshold. The optimal thresholds were plotted as a function of tumour volume using a power law function and cross validated using the leave-one-out technique.

Results

The mean optimal percentage threshold was 50% ± 10% and 62% ± 15% for the SUVmax and SUVpeak without background correction respectively and 47% ± 10% and 60 ± 15% for the SUVmax and SUVpeak with background correction respectively. The optimal threshold curves could be fitted well with power law function. After cross validation the correlation between the effective tumour diameter in pathology and autosegmentation was 0.900 and 0.905 for the SUVmax and SUVpeak without background correction respectively and 0.913 and 0.908 for the SUVmax and SUVpeak with background correction respectively.

Conclusion

No benefit was shown on clinical data for the SUVpeak based segmentation method over a SUVmax based one. Both methods can be used to determine the tumour volumes in resected NSCLC tumours

Bibliographical metadata

Original languageEnglish
JournalRadiotherapy and Oncology
Early online date5 Jul 2018
DOIs
Publication statusPublished - 1 Aug 2018