Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer
Abstract
Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen positron emission tomography (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 tumour (GTV-CNN) in PSMA-PET. Methods: The CNN (3D U-Net) was trained on [68Ga]PSMA-PET images of 152 patients from two different institutions and the training labels were generated manually using a validated technique. The CNN was tested on two independent internal (cohort 1: [68Ga]PSMA-PET, n=18 and cohort 2: [18F]PSMA-PET, n=19) and one external (cohort 3: [68Ga]PSMA-PET, n=20) test-datasets. Accordance between manual contours and GTV-CNN was assessed with Dice-S{\o}rensen coefficient (DSC). Sensitivity and specificity were calculated for the two internal test-datasets by using whole-mount histology. Results: 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 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 seconds 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 reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary PCa. The trained model and the study's source code are available in an open source repository.
Keywords
Cite
@article{arxiv.2008.03201,
title = {Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer},
author = {Dejan Kostyszyn and Tobias Fechter and Nico Bartl and Anca L. Grosu and Christian Gratzke and August Sigle and Michael Mix and Juri Ruf and Thomas F. Fassbender and Selina Kiefer and Alisa S. Bettermann and Nils H. Nicolay and Simon Spohn and Maria U. Kramer and Peter Bronsert and Hongqian Guo and Xuefeng Qiu and Feng Wang and Christoph Henkenberens and Rudolf A. Werner and Dimos Baltas and Philipp T. Meyer and Thorsten Derlin and Mengxia Chen and Constantinos Zamboglou},
journal= {arXiv preprint arXiv:2008.03201},
year = {2020}
}