Elsevier

Radiotherapy and Oncology

Volume 98, Issue 1, January 2011, Pages 117-125
Radiotherapy and Oncology

PET in radiotherapy
Gradient-based delineation of the primary GTV on FDG-PET in non-small cell lung cancer: A comparison with threshold-based approaches, CT and surgical specimens

https://doi.org/10.1016/j.radonc.2010.10.006Get rights and content

Abstract

Purpose

The aim of this study was to validate a gradient-based segmentation method for GTV delineation on FDG-PET in NSCLC through surgical specimen, in comparison with threshold-based approaches and CT.

Materials and methods

Ten patients with stage I–II NSCLC were prospectively enrolled. Before lobectomy, all patients underwent contrast enhanced CT and gated FDG-PET. Next, the surgical specimen was removed, inflated with gelatin, frozen and sliced. The digitized slices were used to reconstruct the 3D macroscopic specimen. GTVs were manually delineated on the macroscopic specimen and on CT images. GTVs were automatically segmented on PET images using a gradient-based method, a source to background ratio method and fixed threshold values at 40% and 50% of SUVmax. All images were finally registered. Analyses of raw volumes and logarithmic differences between GTVs and GTVmacro were performed on all patients and on a subgroup excluding the poorly defined tumors. A matching analysis between the different GTVs was also conducted using Dice’s similarity index.

Results

Considering all patients, both lung and mediastinal windowed CT overestimated the macroscopy, while FDG-PET provided closer values. Among various PET segmentation methods, the gradient-based technique best estimated the true tumor volume. When analysis was restricted to well defined tumors without lung fibrosis or atelectasis, the mediastinal windowed CT accurately assessed the macroscopic specimen. Finally, the matching analysis did not reveal significant difference between the different imaging modalities.

Conclusions

FDG-PET improved the GTV definition in NSCLC including when the primary tumor was surrounded by modifications of the lung parenchyma. In this context, the gradient-based method outperformed the threshold-based ones in terms of accuracy and robustness. In other cases, the conventional mediastinal windowed CT remained appropriate.

Section snippets

Patient selection

Ten patients (mean age 66 years; range 54–85) with histologically proven NSCLC stage I–II were prospectively enrolled in this study between October 2008 and February 2010. From these 10 patients, 6 had squamous cell carcinoma (SCC) and 4 had an adenocarcinoma (ADC). All patients were exclusively treated by lobectomy, excluding thus atypical resections and pneumonectomy. One patient had pre-operative chemotherapy. The patients and their primary tumor characteristics are summarized in Table 1.

Results

The GTVs delineated with the considered imaging modalities are reported in Table 2. The mean and standard deviations of the raw GTVs are provided as well. As detailed in the statistical analysis section, they were computed after logarithmic transformation of the volumes in order to process data distributions that were closer to normality (Fig. 1). Means and standard deviations of transformed volumes are illustrated in Fig. 1 with error bars (mean ± 1SD).

With all patients taken into consideration,

Discussion

Overall, we showed that FDG-PET outperformed CT for the delineation of primary tumor volumes in NSCLC, as previously observed in HNSCC patients [44]. We also confirmed the superiority of the gradient-based segmentation, compared to usual threshold-based delineation, both in terms of raw volumes and logarithmically transformed ones. However, the added value of FDG-PET was more pronounced in cases of tumors surrounded by densifications of the lung parenchyma (atelectasis, BOOP). In other cases,

Acknowledgements

This research program was supported by a grant from the Belgian National Fund for Scientific Research (FNRS Télévie, Grant No. 7.4537.09). Xavier Geets is a postdoctoral researcher partially funded by the FNRS (Grant No. 3.4600.08). John A. Lee is a research associate funded by the FNRS.

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