Delineation of Biological Tumor Volume from Positron Emission Tomography Images in Nasopharyngeal Carcinoma

Ka-Wai Wong, Dora Lai-Wan Kwong, Pek-Lan Khong, Victor Ho-Fun Lee, Sherry Chor-Yi Ng, Martin Wai-Ming Law

The Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China.
The Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China.
The Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China.
The Department of Diagnostic Radiology, Queen Mary Hospital, Hong Kong, China.
The Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.

DOI: 10.4236/jbise.2014.711085                

Abstract

Objective: To develop an auto-segmentation method for delineating the biological tumor volume of nasopharyngeal carcinoma from positron emission tomography images. Methods: A phantom consisting of a water tank with fixed background fluorodeoxyglucose [18F-FDG] activity and spheres with diameters ranging from 2.1 to 5 cm with varying activities of FDG were used to simulate tumors of different sizes and FDG uptake. The phantom was scanned with a PET/CT scan at different sphere to background intensity ratios. An optimum fixed percentage threshold (FT) approach and a signal-to-background ratio (SBR) approach were developed to estimate the true size of the spheres from the PET images. Both approaches were further evaluated in patient images for validation. Twenty-two patients with NPC from stage T1 to T4 were included. The PET based biological tumor volumes (BTV) were delineated with both FT (BTV_FT) and SBR (BTV_SBR) approaches and compared with the gross tumor volume localized from MRI (GTV_MR). The mean volumes of BTV_FT and BTV_SBR were compared and the degree of overlap between GTV_MR and both BTVs was evaluated. Paired t-tests were used for statistical analysis. Results: The optimal FT value was 36.5% of maximal intensity, and SBR approach was represented by an inverse linear regression model. The estimated volume of spheres segmented by both approaches shows no significant difference from the true volume of spheres (p > 0.05), but the average absolute errors were smaller from SBR approach than FT approach (p = 0.008). GTV_MR was larger than both BTV_FT (p = 0.003) and BTV_SBR (p < 0.009). The overlapping volume of BTV_SBR with GTV_MR is significantly larger than with BTV_FT (0.52 and 0.42 respectively, p < 0.0005). Conclusions: SBR approach is more feasible than FT approach to localize the BTV of NPC from FDG-PET image, and BTV might help to modify the extent of GTV_MR for radiotherapy planning purpose.

Keywords

Segmentation Approach, SBR, Fixed Threshold, NPC, PET

Conflicts of Interest

The authors declare no conflicts of interest.

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