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The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [18F]FDG PET/CT examinations

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Abstract

Objective

To create the 3D convolutional neural network (CNN)-based system that can use whole-body [18F]FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC).

Methods

In this study, 1224 image sets from OC patients who underwent whole-body [18F]FDG PET/CT at Kowsar Hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classification as cancerous, and non-cancerous and staging as stage III, and stage IV were determined by pathological diagnosis and specialists’ interpretation. New deep neural network algorithms, the OCDAc-Net, and the OCDAs-Net were developed for diagnostic classification and staging of OC patients using [18F]FDG PET/CT images. Examinations were divided into independent training (75%), validation (10%), and testing (15%) subsets.

Results

This study included 37 women (mean age 56.3 years; age range 36–83 years). Data augmentation techniques were applied to the images in two phases. There were 1224 image sets for diagnostic classification and staging. For the test set, 170 image sets were considered for diagnostic classification and staging. The OCDAc-Net areas under the receiver operating characteristic curve (AUCs) and overall accuracy for diagnostic classification were 0.990 and 0.92, respectively. The OCDAs-Net achieved areas under the receiver operating characteristic curve (AUCs) of 0.995 and overall accuracy of 0.94 for staging.

Conclusions

The proposed 3D CNN-based models provide potential tools for recurrence/post-therapy surveillance in OC. The OCDAc-Net and the OCDAs-Net model provide a new prognostic analysis method that can utilize PET images without pathological findings for diagnostic classification and staging.

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Acknowledgements

This work was supported by the International Atomic Energy Agency (IAEA) under the coordinated research project (CRP) E13050—the International Multicenter Trial on FDG PET/CT in Ovarian Cancer (POCA). The authors would like to acknowledge the instrumental and technical support of the Nuclear Medicine Department, Kowsar Hospital.

Funding

This work was supported by Shiraz University (Grant numbers 1GBC1M148325). In addition, the work was funded by the International Atomic Energy Agency (IAEA) under the coordinated research project (CRP) E13050—the International Multicenter Trial on FDG PET/CT in Ovarian Cancer (POCA).

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Authors and Affiliations

  1. Nuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran

    Mohammad Hossein Sadeghi & Sedigheh Sina

  2. Radiation Research Center, School of Mechanical Engineering, Shiraz University, Shiraz, Iran

    Sedigheh Sina

  3. Department of Nuclear Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

    Mehrosadat Alavi

  4. Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, International Atomic Energy Agency, Vienna, Austria

    Francesco Giammarile

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  1. Mohammad Hossein Sadeghi
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Correspondence to Sedigheh Sina.

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Sadeghi, M.H., Sina, S., Alavi, M. et al. The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [18F]FDG PET/CT examinations. Ann Nucl Med 37, 645–654 (2023). https://doi.org/10.1007/s12149-023-01867-4

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