Journal
DIAGNOSTICS
Volume 12, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/diagnostics12092101
Keywords
deep learning; convolutional neural network; PSMA; artificial intelligence; prostate cancer
Categories
Funding
- Swedish Prostate Cancer Federation
- Knut and Alice Wallenberg Foundation
- Region Skane
- Lund University
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The aim of this study was to develop an artificial intelligence (AI)-based method for the detection and quantification of prostate tumours, local recurrence, lymph node metastases, and bone metastases. The AI method was compared to manual segmentations performed by nuclear medicine physicians. The results showed that the AI method had a sensitivity comparable to that of the physicians.
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 [F-18]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.
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