To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small 18F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compared to ordered subset expectation maximization (OSEM) reconstruction.
Fifty-seven patients with 92 18F-FDG-avid pulmonary nodules (all ≤ 2 cm) undergoing PET/CT for oncological (re-)staging were retrospectively included and a total of 8824 PET images of the lungs were extracted using OSEM and BSREM reconstruction. Per-slice and per-nodule sensitivity of a deep learning algorithm was assessed, with an expert readout by a radiologist/nuclear medicine physician serving as standard of reference. Receiver-operator characteristic (ROC) curve of OSEM and BSREM were assessed and the areas under the ROC curve (AUC) were compared. A maximum standardized uptake value (SUVmax)–based sensitivity analysis and a size-based sensitivity analysis with subgroups defined by nodule size was performed.
The AUC of the deep learning algorithm for nodule detection using OSEM reconstruction was 0.796 (CI 95%; 0.772–0.869), and 0.848 (CI 95%; 0.828–0.869) using BSREM reconstruction. The AUC was significantly higher for BSREM compared to OSEM (p = 0.001). On a per-slice analysis, sensitivity and specificity were 66.7% and 79.0% for OSEM, and 69.2% and 84.5% for BSREM. On a per-nodule analysis, the overall sensitivity of OSEM was 81.5% compared to 87.0% for BSREM.
Our results suggest that machine learning algorithms may aid detection of small 18F-FDG-avid pulmonary nodules in clinical PET/CT. AI performed significantly better on images with BSREM than OSEM.
• The diagnostic value of deep learning for detecting small lung nodules (≤ 2 cm) in PET images using BSREM and OSEM reconstruction was assessed.
• BSREM yields higher SUV max of small pulmonary nodules as compared to OSEM reconstruction.
• The use of BSREM translates into a higher detectability of small pulmonary nodules in PET images as assessed with artificial intelligence.
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Area under the ROC curve
Body mass index
Block sequential regularized expectation maximization
IMAGENET Large Scale Visual Recognition Challenge
Ordered subset expectation maximization
Pan-Canadian Early Detection of Lung Cancer Screening Study
Positron emission tomography
- SUVmax :
Maximum standardized uptake value
Volume of interest
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Dr. Michael Messerli and Dr. Irene Burger received a research grant from the Iten-Kohaut Foundation, Switzerland. Martin W. Huellner received grants from GE Healthcare and a fund by the Alfred and Annemarie von Sick Grant for translational and clinical cardiac and oncological research. The authors would like to thank Josephine Trinckauf, Corina Weyermann, Michèle Hug, and Juliana Koller for their excellent technical support. Further, we thank Fotis Kotasidis (PhD) for his invaluable comments and suggestions regarding this work.
The scientific guarantor of this publication is Dr. Michael Messerli.
Conflict of interest
The authors of this manuscript declare relationships with the following companies: Gustav K. von Schulthess is a Consultant to GE Healthcare and a Co-Director of IDKD, an educational organization that receives funds from multiple companies. Martin W. Huellner, Irene A. Burger, and Michael Messerli received speaker’s fees from GE Healthcare. Apart from that, the other authors of this manuscript declare no personal relationships with any companies, whose products or services may be related with the subject matter of the article. The University Hospital Zurich holds a research agreement with GE Healthcare. No further specific grants from funding agencies in the commercial sectors were received for this study.
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Dr. Michael Messerli and Dr. Martin W. Huellner share last authorship
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Schwyzer, M., Martini, K., Benz, D.C. et al. Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance. Eur Radiol 30, 2031–2040 (2020). https://doi.org/10.1007/s00330-019-06498-w
- Artificial intelligence
- Deep learning
- Diagnostic imaging
- Positron-emission tomography
- Neoplasm Metastasis