Molecular Imaging and Theranostics in Ovarian Cancer: The Role of Nuclear Medicine

Pillay, Venesen; Hadebe, Bawinile; Vorster, Mariza

doi:10.36255/exon-publications-ovarian-cancer-theranostics

Cited by 2 publications

(2 citation statements)

References 51 publications

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“…More accurate information can be provided by 18F-FDG PET/CT examination on surveillance and staging to detect recurrent high-grade OC. Though, surgical staging is still conducted owing to its higher false negative rate for micro lesions or cystic lesions [9].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it is required to apply an accurate noninvasive follow-up method to follow-up patients with OC. One of the useful approaches in OC posttreatment surveillance is Fluorine-18 uorodeoxyglucose positron emission tomography / computed tomography (18F-FDG PET/CT) in comparison to conventional modalities, such as magnetic resonance imaging (MRI) or computed tomography (CT) [6][7][8][9]. Distant metastases, recurrent disease, and staging are detected, and the response to therapy is monitored using FDG PET/CT in OC [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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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

Sadeghi,

Sina,

Alavi

et al. 2023

Ann Nucl Med

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To create the 3D convolutional neural network (CNN)-based system that can use whole-body FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC). MethodsThis study 1224 image sets from OC patients who underwent whole-body FDG PET/CT at Kowsar hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classi cation 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 classi cation and staging of OC patients using PET/CT images. Examinations were divided into independent training (75%), validation (10%), and testing (15%) subsets. ResultsThis 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 classi cation and staging. For the test set, 170 image sets were considered for diagnostic classi cation and staging. The OCDAc-Net areas under the receiver operating characteristic curve (AUCs) and overall accuracy for diagnostic classi cation 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. ConclusionsThe 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 ndings for diagnostic classi cation and staging.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sadeghi,

Sina,

Alavi

et al. 2023

Ann Nucl Med

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

Sadeghi

Sina

Alavi

et al. 2023

Preprint

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Objective To create the 3D convolutional neural network (CNN)-based system that can use whole-body FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC). Methods This study 1224 image sets from OC patients who underwent whole-body 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 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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Molecular Imaging and Theranostics in Ovarian Cancer: The Role of Nuclear Medicine

Cited by 2 publications

References 51 publications

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

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

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

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