Purpose: The exact detection and delineation of the intraprostatic tumour burden is crucial for treatment planning in primary prostate cancer (PCa). We compared ⁶⁸Ga-HBED-CC-PSMA PET/CT with multiparametric MRI (mpMRI) for diagnosis and tumour delineation in patients with primary PCa based on slice by slice correlation with histopathological reference material.

Purpose: To evaluate the performance of radiomic features (RF) derived from PSMA PET for intraprostatic tumor discrimination and non-invasive characterization of Gleason score (GS) and pelvic lymph node status.

Purpose: We performed a voxel-wise comparison of ⁶⁸Ga-HBED-CC-PSMA PET/CT with prostate histopathology to evaluate the performance of ⁶⁸Ga-HBED-CC-PSMA for the detection and delineation of primary prostate cancer (PCa).

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.

Automatic prostate tumor segmentation is often unable to identify the lesion even if multi-parametric MRI data is used as input, and the segmentation output is difficult to verify due to the lack of clinically established ground truth images. In this work we use an explainable deep learning model to interpret the predictions of a convolutional neural network (CNN) for prostate tumor segmentation. The CNN uses a U-Net architecture which was trained on multi-parametric MRI data from 122 patients to automatically segment the prostate gland and prostate tumor lesions. In addition, co-registered ground truth data from whole mount histopathology images were available in 15 patients that were used as a test set during CNN testing. To be able to interpret the segmentation results of the CNN, heat maps were generated using the Gradient Weighted Class Activation Map (Grad-CAM) method. The CNN achieved a mean Dice Sorensen Coefficient 0.62 and 0.31 for the prostate gland and the tumor lesions -with the radiologist drawn ground truth and 0.32 with whole-mount histology ground truth for tumor lesions. Dice Sorensen Coefficient between CNN predictions and manual segmentations from MRI and histology data were not significantly different. In the prostate the Grad-CAM heat maps could differentiate between tumor and healthy prostate tissue, which indicates that the image information in the tumor was essential for the CNN segmentation.