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Gynecologic Imaging Reporting and Data System: A New Proposal for Classifying Adnexal Masses on the Basis of Sonographic Findings
Abstract
OBJECTIVE: The purpose of this study was to describe a new reporting system called the Gynecologic Imaging Reporting and Data System (GI-RADS) for reporting findings in adnexal masses based on transvaginal sonography. METHODS: A total of 171 women (mean age, 39 years; range, 16-77 years) suspected of having an adnexal mass were evaluated by transvaginal sonography before treatment. Pattern recognition analysis and color Doppler blood flow location were used for determining the presumptive diagnosis. Then the GI-RADS was used, with the following classifications: GI-RADS 1, definitively benign; GI-RADS 2, very probably benign; GI-RADS 3, probably benign; GI-RADS 4, probably malignant; and GI-RADS 5, very probably malignant. Patients with GI-RADS 1 and 2 tumors were treated expectantly. All GI-RADS 3, 4, and 5 tumors were removed surgically, and a definitive histologic diagnosis was obtained. The GI-RADS classification was compared with final histologic diagnosis. RESULTS: A total of 187 masses were evaluated. The prevalence rate for malignant tumors was 13.4%. Overall GI-RADS classification rates were as follows: GI-RADS 1, 4 cases (2.1%); GI-RADS 2, 52 cases (27.8%); GI-RADS 3, 90 cases (48.1%); GI-RADS 4, 13 cases (7%); and GI-RADS 5, 28 cases (15%). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 92%, 97%, 85%, 99%, and 96%, respectively. CONCLUSIONS: Our proposed reporting system showed good diagnostic performance. It is simple and could facilitate communication between sonographers/sonologists and clinicians.
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“the research group Amor et al. was the first to describe AI application in sonographic assessment using a non-specified pattern recognition analysis to classify adnexal masses in a new reporting system [184].”
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“Three studies assessed the performance of automated tumor classification [184,191,192]”
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“Of the 11 extracted articles, only two were designed as prospective studies [184,185].”
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“small sample sizes not sufficient enough to train the algorithm [184,185,187,188,192,193].”
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References 28 · Cited by 98
Research works printed but not linked: 2
- 1996, Ultrasound Obstet Gynecol, 8, 210
- 1991, Obstet Gynecol, 78, 70
Metadata
- Authors
- Amor, F., Vaccaro, H., Alcázar, J. L., León, M., Craig, J. M., & Martinez, J.
- Affiliations
- Clinica Universidad de Navarra (ES)
- Clínica Dávila (CL)
- Corresponding author
- Alcázar, Juan Luis
- Venue
- Journal of Ultrasound in Medicine
- Publisher
- Wiley
- Published
- 2009-03
- Volume
- 28
- Issue
- 3
- Pages
- 285-291
- ISSN
- 0278-4297 (ISSN-L)
- OpenAlex
- W2140420547
- Cited by
- 98
- References
- 28
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 12 · 2023: 9 · 2022: 13 · 2021: 10 · 2020: 7 · 2019: 8 · 2018: 4 · 2017: 2 · 2016: 2 · 2015: 5 · 2014: 3 · 2013: 3 · 2012: 2
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Ultrasound in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Ovarian cancer diagnosis and treatment
- Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Keywords
- adnexal mass, transvaginal ultrasound, ovarian tumor classification, color Doppler blood flow, pattern recognition, diagnostic accuracy, benign versus malignant differentiation, histopathological correlation
- MeSH terms
- Adolescent, Adult, Artificial Intelligence, Database Management Systems, Female, Gynecology, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Middle Aged, Ovarian Neoplasms, Pattern Recognition, Automated, Radiology Information Systems, Sensitivity and Specificity, Ultrasonography, Reproducibility of Results, Information Storage and Retrieval, Young Adult
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