ArticleInsights into imaging2023
Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors.
Article 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 33 papers, 1 of them a synthesis 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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Computed Tomography-Based Machine Learning Models in the Classification of Adnexal Masses - A Systematic Review.F1000Research · 2026Pooled it
- A Non-enhanced CT Dataset for Differential Diagnosis of Pediatric Extracranial Germ Cell Tumors.Scientific data · 2026Article
- Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.NPJ digital medicine · 2026Article
- Two-staged CT-based radiomics model in characterising early-stage ovarian carcinoma and benign ovarian masses.Journal of ovarian research · 2026Article
- Evaluating Performance of a Deep Learning-based Artificial Intelligence Model for Ovarian Tumor Classification Using a Multicenter CT Dataset.Radiology. Imaging cancer · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Diagnostic accuracy of artificial intelligence in detection of ovarian cancer-a pilot study.Frontiers in medicine · 2026Article
- Uterine cancer classification from CT images using convolutional feature extraction and transformer-based self-attention.Frontiers in medicine · 2026Article
- Radiomics-based MRI model for differentiating ovarian cystadenoma and cystadenocarcinoma.Frontiers in oncology · 2026Article
- Machine-Learning-Assisted Analysis of Patient Clinical Biomarkers to Improve Ovarian Cancer Diagnosis.Precision chemistry · 2025Article
- Multiple perception contrastive learning for automated ovarian tumor classification in CT images.Abdominal radiology (New York) · 2025Article
- Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.Cancers · 2025Review
- The value of a radiomics model in predicting ovarian malignancy: a retrospective multi-center comparison with O-RADS and radiologists.Insights into imaging · 2025Article
- An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images.Scientific reports · 2025Article
- Integrative deep learning and radiomics analysis for ovarian tumor classification and diagnosis: a multicenter large-sample comparative study.La Radiologia medica · 2025Article
- An overview of the use of cutting-edge artificial intelligence (AI) modeling to produce synthetic medical data (SMD) in decentralized clinical machine learning (ML) for ovarian cancer(OC) and ovarian lymphoma(OL).Journal of ultrasound · 2025Review
- Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy.Scientific reports · 2025Article
- Evaluation of a novel ensemble model for preoperative ovarian cancer diagnosis: Clinical factors, O-RADS, and deep learning radiomics.Translational oncology · 2025Article
- Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnexal masses.Insights into imaging · 2025Article
- AI-powered diagnosis of ovarian conditions: insights from a newly introduced ultrasound dataset.Frontiers in physiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
backgroundTo develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors.
methodsWe enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set.
resultsAmong the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists.
conclusionsWe developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.
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.