ArticleJournal of ultrasound2021
The Adoption of Radiomics and machine learning improves the diagnostic processes of women with Ovarian MAsses (the AROMA pilot study).
Article in Journal of ultrasound, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 4 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 4 syntheses or guidelines pooled it, 56 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
- Performance of radiomics analysis in ultrasound imaging for differentiating benign from malignant adnexal masses: A systematic review and meta-analysis.Acta obstetricia et gynecologica Scandinavica · 2025Pooled it
- Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Pooled it
- Artificial intelligence for ovarian cancer diagnosis via ultrasound: a systematic review and quantitative assessment of model performance.Frontiers in artificial intelligence · 2025Pooled it
- Large Language Models Meet Gynecologic Ultrasound: Advancing the Characterization of ADNEXal Masses.Journal of imaging · 2026Article
- Early Vascular and Morphological Response After Transvaginal Radiofrequency Ablation of Uterine Fibroids: A Doppler-Based Retrospective Study.Journal of clinical medicine · 2026Article
- Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.NPJ digital medicine · 2026Article
- Artificial intelligence potential in ovarian endometriosis imaging: a comparative meta-analysis of transvaginal ultrasound-based AI models and human readers.Abdominal radiology (New York) · 2026Review
- Clinical and ultrasound features of ovarian sclerosing stromal tumors: a large single-center case series.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics-based ultrasOund Model for differentiating Uterine Sarcomas from leiomyomas (ROMUS): a retrospective pilot Multicenter Italian Trials in Ovarian Cancer (MITO) study.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- Developing and validating ultrasound-based machine-learning models incorporating radiomics features to predict malignancy in adnexal masses.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- Development and validation of an artificial intelligence-based model for diagnosing benign, borderline, and malignant adnexal masses.NPJ precision oncology · 2026Article
- A review of ovarian-adnexal reporting and data system and radiomics in ovarian cancer: focus on ultrasound applications.Frontiers in oncology · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Radiomics-based MRI models for predicting breast cancer axillary lymph node involvement in comparison with Node-RADS: a proof-of-concept study.European radiology experimental · 2025Article
- Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound.Medical physics · 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
- Ultrasonic radiomics-based nomogram for preoperative prediction of residual tumor in advanced epithelial ovarian cancer: a multicenter retrospective study.Frontiers in oncology · 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
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and evaluate the performance of a radiomic and machine learning model applied to ultrasound images in predicting the risk of malignancy of ovarian masses (OMs).
methodsSingle-center retrospective evaluation of consecutive patients who underwent transvaginal ultrasound (US) with images storage and surgery for ovarian masses. Radiomics methodology was applied to US images according to the International Biomarker Standardization Initiative guidelines. OMs were divided into three homogeneous groups: solid, cystic and motley. TRACE4
resultsA total of 241 women were recruited. OMs were divided in the three groups: 95 (39.5%) solid, 66 (27.5%) cystic, 80 (33%) motley. For solid OMs, 269 radiomic features were used for the training-validation-testing of the model with accuracy 80%, sensitivity 78%, specificity 83%, AUC 87%. For cystic OMs, 278 radiomic features were used for the training-validation-testing of the model with accuracy 87%, sensitivity 75%, specificity 90%, AUC 88%. For mixed OMs, 306 radiomic features were used for the training-validation-testing of the model with accuracy 81%, sensitivity 81%, specificity 81%, AUC 89%.
conclusionRadiomics is a promising tool in improving preoeprative work-up of women diagnosed with OMs. Even in the absence of the subjective impression of expert ultrasound examiner, radiomics allows to easily identify patients with ovarian cancer. Future validation studies on larger series are needed.
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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.