ArticleEuropean radiology experimental2021
A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125.
Article in European radiology experimental, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Artificial Intelligence Applied to Ultrasound Diagnosis of Pelvic Gynecological Tumors: A Systematic Review and Meta-Analysis.Gynecologic and obstetric investigation · 2026Pooled it
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.Biomedical engineering online · 2024Trial
- Differentiating high- and low-grade serous ovarian carcinoma using radiomics: a pilot study.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- A nomogram combining clinical features, O-RADS US, and radiomics based on ultrasound imaging for diagnosing ovarian cancer.Scientific reports · 2025Article
- Artificial Intelligence in Ultrasound-Based Diagnoses of Gynecological Tumors: A Systematic Review.Cureus · 2025Review
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid morphology on ultrasound.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Observational
- Advancements in artificial intelligence for ultrasound diagnosis of ovarian cancer: a comprehensive review.Frontiers in oncology · 2025Review
- Sensitive and Cost-Effective Tools in the Detection of Ovarian Cancer Biomarkers.Analytical science advances · 2024Article
- An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators.Cancer medicine · 2024Article
- Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans?Journal of clinical medicine · 2024Review
- Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.BMC medical imaging · 2024Article
- Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Radiomics in the evaluation of ovarian masses - a systematic review.Insights into imaging · 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
- Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review.Insights into imaging · 2023Review
- Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology.Exploration of targeted anti-tumor therapy · 2023Review
- A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.Archives of gynecology and obstetrics · 2022Observational
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate the performance of a decision support system (DSS) based on radiomics and machine learning in predicting the risk of malignancy of ovarian masses (OMs) from transvaginal ultrasonography (TUS) and serum CA-125.
methodsA total of 274 consecutive patients who underwent TUS (by different examiners and with different ultrasound machines) and surgery, with suspicious OMs and known CA-125 serum level were used to train and test a DSS. The DSS was used to predict the risk of malignancy of these masses (very low versus medium-high risk), based on the US appearance (solid, liquid, or mixed) and radiomic features (morphometry and regional texture features) within the masses, on the shadow presence (yes/no), and on the level of serum CA-125. Reproducibility of results among the examiners, and performance accuracy, sensitivity, specificity, and area under the curve were tested in a real-world clinical setting.
resultsThe DSS showed a mean 88% accuracy, 99% sensitivity, and 77% specificity for the 239 patients used for training, cross-validation, and testing, and a mean 91% accuracy, 100% sensitivity, and 80% specificity for the 35 patients used for independent testing.
conclusionsThis DSS is a promising tool in women diagnosed with OMs at TUS, allowing to predict the individual risk of malignancy, supporting clinical decision making.
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