Deep learning detection of prostate cancer recurrence with 18F-FACBC (fluciclovine, Axumin®) positron emission tomography

Abstract

Purpose

To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal 18F-FACBC (fluciclovine, Axumin®) PET scans based on the presence of tumor recurrence and/or metastases in patients with prostate cancer (PC) and biochemical recurrence (BCR).

Methods

A total of 251 consecutive 18F-fluciclovine PET scans were acquired between September 2017 and June 2019 in 233 PC patients with BCR (18 patients had 2 scans). PET images were labeled as normal or abnormal using clinical reports as the ground truth. Convolutional neural network (CNN) models were trained using two different architectures, a 2D-CNN (ResNet-50) using single slices (slice-based approach) and the same 2D-CNN and a 3D-CNN (ResNet-14) using a hundred slices per PET image (case-based approach). Models’ performances were evaluated on independent test datasets.

Results

For the 2D-CNN slice-based approach, 6800 and 536 slices were used for training and test datasets, respectively. The sensitivity and specificity of this model were 90.7% and 95.1%, and the area under the curve (AUC) of receiver operating characteristic curve was 0.971 (p < 0.001). For the case-based approaches using both 2D-CNN and 3D-CNN architectures, a training dataset of 100 images and a test dataset of 28 images were randomly allocated. The sensitivity, specificity, and AUC to discriminate abnormal images by the 2D-CNN and 3D-CNN case-based approaches were 85.7%, 71.4%, and 0.750 (p = 0.013) and 71.4%, 71.4%, and 0.699 (p = 0.053), respectively.

Conclusion

DL accurately classifies abnormal 18F-fluciclovine PET images of the pelvis in patients with BCR of PC. A DL classifier using single slice prediction had superior performance over case-based prediction.

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Availability of data and material

Yes and will be provided upon request.

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Authors

Corresponding author

Correspondence to Guido A. Davidzon.

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Conflict of interest

HY is an employee of Dimensional Mechanics. AI receives institutional research support from GE Healthcare, Advanced Accelerator Application, and Progenics Pharmaceuticals. None are related to this work. GAD receives institutional research support from GE Healthcare, Dimensional Mechanics, and Kheiron Medical Technologies.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

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Neopulse is a proprietary software. The model’s scripting files used with Neopulse will be provided upon request.

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This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence)

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Lee, J.J., Yang, H., Franc, B.L. et al. Deep learning detection of prostate cancer recurrence with 18F-FACBC (fluciclovine, Axumin®) positron emission tomography. Eur J Nucl Med Mol Imaging (2020). https://doi.org/10.1007/s00259-020-04912-w

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Keywords

  • Fluciclovine
  • PET
  • Prostate cancer
  • Deep learning
  • CNN