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

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Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

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 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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Abbreviations

18F-FDG:

18F-Fluorodeoxyglucose

AI:

Artificial intelligence

AUC:

Area under the ROC curve

BMI:

Body mass index

BSREM:

Block sequential regularized expectation maximization

CI:

Confidence intervals

CT:

Computed tomography

FP:

False positive

ILSVRC:

IMAGENET Large Scale Visual Recognition Challenge

OSEM:

Ordered subset expectation maximization

PanCan:

Pan-Canadian Early Detection of Lung Cancer Screening Study

PET:

Positron emission tomography

ROC:

Receiver-operator characteristic

SUVmax :

Maximum standardized uptake value

VOI:

Volume of interest

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Acknowledgments

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.

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Authors and Affiliations

  1. Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland

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

  2. Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland

    Moritz Schwyzer & Katharina Martini

  3. University of Zurich, Zurich, Switzerland

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

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  1. Moritz Schwyzer
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Correspondence to Michael Messerli.

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Guarantor

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.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all patients in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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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

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