Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT

Elin Trägårdh, Olof Enqvist, Johannes Ulén, Jonas Jögi, Ulrika Bitzén, Fredrik Hedeer, Kristian Valind, Sabine Garpered, Erland Hvittfeldt, Pablo Borrelli, Lars Edenbrandt

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Article number2101
JournalDiagnostics
Volume12
Issue number9
DOIs
Publication statusPublished - 2022 Aug 30

Subject classification (UKÄ)

  • Radiology, Nuclear Medicine and Medical Imaging
  • Cancer and Oncology

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