PET in radiotherapyGradient-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
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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2020, Cancer/RadiotherapieCitation Excerpt :Nonetheless, combinations of thresholds could lead to overestimation or underestimation of overlaps and other PET segmentation methods, such as automatic approaches, should also be tested in the near future. In fact, several studies have suggested that gradient-based method [35] best estimates the true tumour volume in non-small-cell lung cancer or head and neck squamous cell carcinoma, compared to standardised uptake value-based method [36,37]. The fuzzy locally adaptive Bayesian (FLAB) method is also an interesting model that has shown an improvement of metabolic tumour volume delineation of lung or oesophageal lesions [38–40].