ArticleAnnals of nuclear medicine2023
The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [
Article in Annals of nuclear medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Longitudinal deep learning models for tracking disease progression in ovarian cancer using PET/CT imaging and clinical reports.Physical and engineering sciences in medicine · 2026Article
- Enhanced detection of ovarian cancer using AI-optimized 3D CNNs for PET/CT scan analysis.Physical and engineering sciences in medicine · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- AI-enhanced PET/CT image synthesis using CycleGAN for improved ovarian cancer imaging.Polish journal of radiology · 2025Article
- Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.Polish journal of radiology · 2024Review
- Enhancing Prostate Cancer Detection: Integrating Multiparametric Magnetic Resonance Imaging andJournal of medical physicsArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
objectiveTo create the 3D convolutional neural network (CNN)-based system that can use whole-body [
methodsIn this study, 1224 image sets from OC patients who underwent whole-body [
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 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.
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 findings for diagnostic classification and staging.
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Registered trials
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