Journal Article

Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance

Schwyzer Moritz, Burger, Irene A, Ferraro, Daniela A, Kudura Ken, von Schulthess Gustav K, Messerli, Michael, Kaufmann, Philipp A, Martini Katharina, Huellner, Martin W, Treyer Valerie, Benz, Dominik C

2020

Overview

ObjectivesTo 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.MethodsFifty-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.ResultsThe 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.ConclusionsOur 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.Key Points• 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 SUVmaxof 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.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Artificial intelligence / Computed tomography / Deep learning / Diagnostic systems / Digital imaging / Fluorine isotopes / Image reconstruction / Learning algorithms / Lung nodules / Lungs / Machine learning / Maximization / Medical diagnosis / Medical imaging / Nodules / Nuclear medicine / Optimization / Performance evaluation / Positron emission / Positron emission tomography / Sensitivity analysis / Subgroups / Tomography