Optimal (68)Ga-PSMA and (18)F-PSMA PET window levelling for gross tumour volume delineation in primary prostate cancer

Fulltext:
235315.pdf
Embargo:
until further notice
Size:
2.079Mb
Format:
PDF
Description:
Publisher’s version
Publication year
2021Source
European Journal of Nuclear Medicine and Molecular Imaging, 48, 4, (2021), pp. 1211-1218ISSN
Publication type
Article / Letter to editor

Display more detailsDisplay less details
Organization
Radiation Oncology
Medical Imaging
Journal title
European Journal of Nuclear Medicine and Molecular Imaging
Volume
vol. 48
Issue
iss. 4
Page start
p. 1211
Page end
p. 1218
Subject
Radboudumc 14: Tumours of the digestive tract RIMLS: Radboud Institute for Molecular Life Sciences; Radboudumc 15: Urological cancers RIHS: Radboud Institute for Health Sciences; Radboudumc 9: Rare cancers RIHS: Radboud Institute for Health SciencesAbstract
PURPOSE: This study proposes optimal tracer-specific threshold-based window levels for PSMA PET-based intraprostatic gross tumour volume (GTV) contouring to reduce interobserver delineation variability. METHODS: Nine (68)Ga-PSMA-11 and nine (18)F-PSMA-1007 PET scans including GTV delineations of four expert teams (GTV(manual)) and a majority-voted GTV (GTV(majority)) were assessed with respect to a registered histopathological GTV (GTV(histo)) as the gold standard reference. The standard uptake values (SUVs) per voxel were converted to a percentage (SUV%) relative to the SUV(max). The statistically optimised SUV% threshold (SOST) was defined as those that maximises accuracy for threshold-based contouring. A leave-one-out cross-validation receiver operating characteristic (ROC) curve analysis was performed to determine the SOST for each tracer. The SOST analysis was performed twice, first using the GTV(histo) contour as training structure (GTV(SOST-H)) and second using the GTV(majority) contour as training structure (GTV(SOST-MA)) to correct for any limited misregistration. The accuracy of both GTV(SOST-H) and GTV(SOST-MA) was calculated relative to GTV(histo) in the 'leave-one-out' patient of each fold and compared with the accuracy of GTV(manual). RESULTS: ROC curve analysis for (68)Ga-PSMA-11 PET revealed a median threshold of 25 SUV% (range, 22-27 SUV%) and 41 SUV% (40-43 SUV%) for GTV(SOST-H) and GTV(SOST-MA), respectively. For (18)F-PSMA-1007 PET, a median threshold of 42 SUV% (39-45 SUV%) for GTV(SOST-H) and 44 SUV% (42-45 SUV%) for GTV(SOST-MA) was found. A significant pairwise difference was observed when comparing the accuracy of the GTV(SOST-H) contours with the median accuracy of the GTV(manual) contours (median, - 2.5%; IQR, - 26.5-0.2%; p = 0.020), whereas no significant pairwise difference was found for the GTV(SOST-MA) contours (median, - 0.3%; IQR, - 4.4-0.6%; p = 0.199). CONCLUSIONS: Threshold-based contouring using GTV(majority)-trained SOSTs achieves an accuracy comparable with manual contours in delineating GTV(histo). The median SOSTs of 41 SUV% for (68)Ga-PSMA-11 PET and 44 SUV% for (18)F-PSMA-1007 PET form a base for tracer-specific window levelling. TRIAL REGISTRATION: Clinicaltrials.gov ; NCT03327675; 31-10-2017.
This item appears in the following Collection(s)
- Academic publications [227693]
- Electronic publications [107311]
- Faculty of Medical Sciences [86198]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.