RT Journal Article
SR Electronic
T1 Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP 823
OP 828
DO 10.2967/jnumed.120.254623
VO 62
IS 6
A1 Dejan Kostyszyn
A1 Tobias Fechter
A1 Nico Bartl
A1 Anca L. Grosu
A1 Christian Gratzke
A1 August Sigle
A1 Michael Mix
A1 Juri Ruf
A1 Thomas F. Fassbender
A1 Selina Kiefer
A1 Alisa S. Bettermann
A1 Nils H. Nicolay
A1 Simon Spohn
A1 Maria U. Kramer
A1 Peter Bronsert
A1 Hongqian Guo
A1 Xuefeng Qiu
A1 Feng Wang
A1 Christoph Henkenberens
A1 Rudolf A. Werner
A1 Dimos Baltas
A1 Philipp T. Meyer
A1 Thorsten Derlin
A1 Mengxia Chen
A1 Constantinos Zamboglou
YR 2021
UL http://jnm.snmjournals.org/content/62/6/823.abstract
AB Accurate delineation of the intraprostatic gross tumor volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen PET (PSMA PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumor (GTV-CNN) in PSMA PET. Methods: The CNN (3D U-Net) was trained on the 68Ga-PSMA PET images of 152 patients from 2 different institutions, and the training labels were generated manually using a validated technique. The CNN was tested on 2 independent internal (cohort 1: 68Ga-PSMA PET, n = 18 and cohort 2: 18F-PSMA PET, n = 19) and 1 external (cohort 3: 68Ga-PSMA PET, n = 20) test datasets. Accordance between manual contours and GTV-CNN was assessed with the Dice-Sørensen coefficient (DSC). Sensitivity and specificity were calculated for the 2 internal test datasets (cohort 1: n = 18, cohort 2: n = 11) using whole-mount histology. Results: The median DSCs for cohorts 1–3 were 0.84 (range: 0.32–0.95), 0.81 (range: 0.28–0.93), and 0.83 (range: 0.32–0.93), respectively. Sensitivities and specificities for the GTV-CNN were comparable with manual expert contours: 0.98 and 0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was around 6 s for a standard dataset. Conclusion: The application of a CNN for automated contouring of intraprostatic GTV in 68Ga-PSMA and 18F-PSMA PET images resulted in a high concordance with expert contours and in high sensitivities and specificities in comparison with histology as a reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary prostate cancer. The trained model and the study’s source code are available in an open source repository.