ReviewPolish journal of radiology2024
Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.
Review in Polish journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 47 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
- Artificial Intelligence Applied to Ultrasound Diagnosis of Pelvic Gynecological Tumors: A Systematic Review and Meta-Analysis.Gynecologic and obstetric investigation · 2026Pooled it
- Artificial intelligence for ovarian cancer diagnosis via ultrasound: a systematic review and quantitative assessment of model performance.Frontiers in artificial intelligence · 2025Pooled it
- Can radiomics outperform CT morphological features in diagnosing ovarian clear cell carcinoma? A multicenter study.Abdominal radiology (New York) · 2026Article
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Multimodal Deep Learning Based on Ultrasound Images and Clinical Data for Better Ovarian Cancer Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Improving Ovarian Cancer Subtyping with Computer Vision Models on Tiled Histopathological Images.Journal of imaging informatics in medicine · 2026Article
- Multimodal deep learning using preoperative CT and ultrasound for recurrence risk prediction in high-grade serous ovarian carcinoma.BMC medical imaging · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Ovarian masses suggested for MRI examination: assessment of deep learning models based on non-contrast-enhanced MRI sequences for predicting malignancy.Abdominal radiology (New York) · 2025Article
- MRI-based Ovarian Lesion Classification via a Foundation Segmentation Model and Multimodal Analysis: A Multicenter Study.Radiology · 2025Article
- The Evolving Landscape of Ovarian Cancer: Innovations in Biotechnology and Artificial Intelligence- Based Screening and Treatment.Current treatment options in oncology · 2025Review
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- OVision A raspberry Pi powered portable low cost medical device framework for cancer diagnosis.Scientific reports · 2025Article
- Integrative multi-stage deep learning framework for ovarian tumor ultrasound classification with explainability and confidence estimation.Frontiers in medicine · 2025Article
- AI-enhanced PET/CT image synthesis using CycleGAN for improved ovarian cancer imaging.Polish journal of radiology · 2025Article
- Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study.Journal of cancer research and clinical oncology · 2024Article
- Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.Cancer medicine · 2024Article
- Enhancing Prostate Cancer Detection: Integrating Multiparametric Magnetic Resonance Imaging andJournal of medical physicsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies. The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.