ArticleEuropean radiology2021
Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 75 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
- Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.Journal of ovarian research · 2026Article
- The use of MRI in the diagnosis of ovarian cancer.Frontiers in medicine · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- MRI-based Ovarian Lesion Classification via a Foundation Segmentation Model and Multimodal Analysis: A Multicenter Study.Radiology · 2025Article
- Artificial Intelligence in Obstetric and Gynecological MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- 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
- Diagnostic accuracy of artificial intelligence-assisted radiology assessment of cancer: a systematic review.BJR artificial intelligence · 2025Article
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer.iScience · 2024Article
- Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS.Frontiers in oncology · 2024Article
- Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.Frontiers in medicine · 2024Article
- Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.Polish journal of radiology · 2024Review
- Radiomics in the evaluation of ovarian masses - a systematic review.Insights into imaging · 2023Article
- Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer.La Radiologia medica · 2023Article
- Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors.Insights into imaging · 2023Article
- Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review.Progress in biomedical engineering (Bristol, England) · 2023Article
- Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging.Scientific reports · 2023Article
- AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis.Frontiers in oncology · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
17 authors at 6 institutions in 2 countries.
Funding
Abstract
objectivesThere currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging.
methodsFive hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set.
resultsCompared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p < 0.001) and specificity (0.92 vs 0.64, p < 0.001) with comparable sensitivity (0.75 vs 0.63, p = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p = 0.033) and specificity (0.92 vs 0.70, p < 0.001) with comparable sensitivity (0.75 vs 0.83, p = 0.557). Assisted by the model's probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p = 0.097) when compared with the senior radiologists.
conclusionsThese results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance. KEY POINTS: • Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists. • Assisted by the deep learning model's probabilities, junior radiologists achieved better performance that matched those of senior radiologists.
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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.