Here, we aimed to develop and validate a fully automated artificial intelligence (AI)-based method for the detection and quantification of suspected prostate tumour/local recurrence, lymph node metastases, and bone metastases from [18F]PSMA-1007 positron emission tomography-computed tomography (PET-CT) images. Images from 660 patients were included. Segmentations by one expert reader were ground truth. A convolutional neural network (CNN) was developed and trained on a training set, and the performance was tested on a separate test set of 120 patients. The AI method was compared with manual segmentations performed by several nuclear medicine physicians. Assessment of tumour burden (total lesion volume (TLV) and total lesion uptake (TLU)) was performed. The sensitivity of the AI method was, on average, 79% for detecting prostate tumour/recurrence, 79% for lymph node metastases, and 62% for bone metastases. On average, nuclear medicine physicians' corresponding sensitivities were 78%, 78%, and 59%, respectively. The correlations of TLV and TLU between AI and nuclear medicine physicians were all statistically significant and ranged from R = 0.53 to R = 0.83. In conclusion, the development of an AI-based method for prostate cancer detection with sensitivity on par with nuclear medicine physicians was possible. The developed AI tool is freely available for researchers.
Diagnostics (Basel, Switzerland). 2022 Aug 30*** epublish ***
Elin Trägårdh, Olof Enqvist, Johannes Ulén, Jonas Jögi, Ulrika Bitzén, Fredrik Hedeer, Kristian Valind, Sabine Garpered, Erland Hvittfeldt, Pablo Borrelli, Lars Edenbrandt
Department of Translational Medicine, Wallenberg Centre for Molecular Medicine, Lund University, 205 02 Malmö, Sweden., Eigenvision AB, 211 30 Malmö, Sweden., Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, 221 85 Lund, Sweden., Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, 205 02 Malmö, Sweden., Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden.