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Cancer Imaging

Open Access

Potential influence of automated volumetry on treatment response classifications in lung cancer lesions

  • Kaveh Akbari1Email author,
  • Alexandra Barthol1,
  • Irene Schnauder1,
  • v Wunn1,
  • Elmar Brehm1,
  • Bernd Lamprecht1 and
  • Franz Fellner1
Cancer Imaging201414(Suppl 1):P30

Published: 9 October 2014


To evaluate the potential influence of automated volumetry on treatment response classifications in lung cancer in comparison to manual unidimensional measurements.

Material and methods

  1. 60

    patients (41 men, 19 women, mean age 61.8 ± 9.4 years) with histopathologically verified lung cancer were included in this retrospective study.


For each patient, up to 2 target lesions were quantitatively evaluated in a baseline and two follow-up CT scans (77 lesions, 154 response classifications) by two independent radiologists. Hilar and mediastinal masses, as well as lesions surrounded by atelectasis were excluded.

For each lesion a unidimensional diameter measurement, as well as an automated CT-volumetry was performed. In the follow-up studies, the response evaluation was assessed using RECIST compared to volume equivalents of RECIST with converted thresholds (-65/+73%).


The results of the manual one-dimensional measurements varied between the two observers by 6.34 ± 17.12%, affecting the volume to the power of 3, whereas the volumetric measurements varied only by 3.33 ± 6.66%.

In 16.9% (26/154) of the cases the volumetric assessment led to a different response classification.

In 13% (20/154) of the cases the different response classification would have an effect on therapeutic decisions.


The volumetric assessment of lung cancer lesions can reflect the tumour burden more appropriately and therefore has a significant effect on response classifications and therapeutic decisions.

Authors’ Affiliations

Department of Radiology, General Hospital Linz, Linz, Austria


© Kaveh et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.