Metabolically active tumour volume segmentation from dynamic [18F]FLT PET studies in non-small cell lung cancer

• Published in: EJNMMI research Vol. 5; no. 1; p. 26
• Main Authors: Hoyng, Lieke L, Frings, Virginie, Hoekstra, Otto S, Kenny, Laura M, Aboagye, Eric O, Boellaard, Ronald
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 23.04.2015; Springer Nature B.V
• Subjects: Cardiac Imaging; Imaging; Lung cancer; Medicine; Medicine & Public Health; Nuclear Medicine; Oncology; Orthopedics; Radiology; Research Article; F; 18F-FLT; Kinetic filtering; Dynamic; NSCLC; Segmentation; PET
• ISSN: 2191-219X; 2191-219X
• DOI: 10.1186/s13550-015-0102-6

Background Positron emission tomography (PET) with 18 F-3′-deoxy-3′-fluorothymidine ([ 18 F]FLT) can be used to assess tumour proliferation. A kinetic-filtering (KF) classification algorithm has been suggested for segmentation of tumours in dynamic [ 18 F]FLT PET data. The aim of the present study was to evaluate KF segmentation and its test-retest performance in [ 18 F]FLT PET in non-small cell lung cancer (NSCLC) patients. Methods Nine NSCLC patients underwent two 60-min dynamic [ 18 F]FLT PET scans within 7 days prior to treatment. Dynamic scans were reconstructed with filtered back projection (FBP) as well as with ordered subsets expectation maximisation (OSEM). Twenty-eight lesions were identified by an experienced physician. Segmentation was performed using KF applied to the dynamic data set and a source-to-background corrected 50% threshold (A50%) was applied to the sum image of the last three frames (45- to 60-min p.i.). Furthermore, several adaptations of KF were tested. Both for KF and A50% test-retest (TRT) variability of metabolically active tumour volume and standard uptake value (SUV) were evaluated. Results KF performed better on OSEM- than on FBP-reconstructed PET images. The original KF implementation segmented 15 out of 28 lesions, whereas A50% segmented each lesion. Adapted KF versions, however, were able to segment 26 out of 28 lesions. In the best performing adapted versions, metabolically active tumour volume and SUV TRT variability was similar to those of A50%. KF misclassified certain tumour areas as vertebrae or liver tissue, which was shown to be related to heterogeneous [ 18 F]FLT uptake areas within the tumour. Conclusions For [ 18 F]FLT PET studies in NSCLC patients, KF and A50% show comparable tumour volume segmentation performance. The KF method needs, however, a site-specific optimisation. The A50% is therefore a good alternative for tumour segmentation in NSCLC [ 18 F]FLT PET studies in multicentre studies. Yet, it was observed that KF has the potential to subsegment lesions in high and low proliferative areas.