ReviewInsights into imaging2023
Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review.
Review in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 of them syntheses that pooled it.
What it found
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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
14 citing papers in PubMed, 4 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Diagnostic accuracy of ultrasound models for assessment of ovarian tumors: systematic review and meta-analysis.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 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 Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Transformer-based ultrasound radiomics for novel multi-task automated segmentation and classification of ovarian tumors.Abdominal radiology (New York) · 2026Article
- Ovarian cancer recurrence prediction: comparing confirmatory to real-world predictors with machine learning.ESMO real world data and digital oncology · 2026Article
- Evaluating Performance of a Deep Learning-based Artificial Intelligence Model for Ovarian Tumor Classification Using a Multicenter CT Dataset.Radiology. Imaging cancer · 2026Article
- A Deep Learning Approach for Classifying Benign, Malignant, and Borderline Ovarian Tumors Using Convolutional Neural Networks and Generative Adversarial Networks.Medical sciences (Basel, Switzerland) · 2026Article
- An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images.Scientific reports · 2025Article
- Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024.Discover oncology · 2025Article
- International multicenter validation of AI-driven ultrasound detection of ovarian cancer.Nature medicine · 2025Article
- Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer.iScience · 2024Article
- Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.Frontiers in medicine · 2024Article
- Ultrasound coupled RES-loaded ultrasound microbubble inhibits the proliferation of ovarian cancer cells by expression of long non-coding RNA (lncRNA) involved in apoptosis using real-time PCR.American journal of cancer research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDifferent noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors.
methodsWe searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards.
resultsIn thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6-100% and 66.7-100% and specificities ranged from 76.3-100%; 69-100% and 77.8-100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set.
conclusionBased on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.
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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.