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Gynecology Imaging Reporting and Data System (GI-RADS): diagnostic performance and inter-reviewer agreement

  • Mohammad Abd Alkhalik BashaEmail author
  • Rania Refaat
  • Safaa A. Ibrahim
  • Nadia M. Madkour
  • Awad Mahmoud Awad
  • Elshaimaa Mohamed Mohamed
  • Ahmed A. El Sammak
  • Mohamed M. A. Zaitoun
  • Hitham A. Dawoud
  • Mai E. M. Khamis
  • Heba A. E. Mohamed
  • Ahmed Mohamed El-Maghraby
  • Ahmed A. El-Hamid M. Abdalla
  • Mostafa Mohamad Assy
  • Mohamad Gamal Nada
  • Ahmed Ali Obaya
  • Eman H. Abdelbary
Ultrasound
  • 162 Downloads

Abstract

Objective

To evaluate diagnostic performance and inter-reviewer agreement (IRA) of the Gynecologic Imaging Reporting and Data System (GI-RADS) for diagnosis of adnexal masses (AMs) by pelvic ultrasound (US).

Patients and methods

A prospective multicenter study included 308 women (mean age, 41 ± 12.5 years; range, 15–73 years) with 325 AMs detected by US. All US examinations were analyzed, and AMs were categorized into five categories according to the GI-RADS classification. We used histopathology and US follow-up as the reference standards for calculating diagnostic performance of GI-RADS for detecting malignant AMs. The Fleiss kappa (κ) tests were applied to evaluate the IRA of GI-RADS scoring results for predicting malignant AMs.

Results

A total of 325 AMs were evaluated: 127 (39.1%) were malignant and 198 (60.9%) were benign. Of 95 AMs categorized as GI-RADS 2 (GR2), none was malignant; of 94 AMs categorized as GR3, three were malignant; of 13 AMs categorized as GR4, six were malignant; and of 123 AMs categorized as GR5, 118 were malignant. On a lesion-based analysis, the GI-RADS had a sensitivity, a specificity, and an accuracy of 92.9%, 97.5%, and 95.7%, respectively, when regarding only those AMs classified as GR5 for predicting malignancy. Considering combined GR4 and GR5 as a predictor for malignancy, the sensitivity, specificity, and accuracy of GI-RADS were 97.6%, 93.9%, and 95.4%, respectively. The IRA of the GI-RADS category was very good (κ = 0.896). The best cutoff value for predicting malignant AMs was >GR3.

Conclusions

The GI-RADS is very valuable for improving US structural reports.

Key Points

• There is still a lack of a standard in the assessment of AMs.

• GI-RADS is very valuable for improving US structural reports of AMs.

• GI-RADS criteria are easy and work at least as well as IOTA.

Keywords

Adnexal diseases Neoplasms Gynecology Ultrasonography 

Abbreviations

AMs

Adnexal masses

AUC

Area under the ROC curve

BI-RADS

Breast Imaging Reporting and Data System

CI

Confidence interval

FIGO

Federation of Gynaecology and Obstetrics

GI-RADS

Gynecologic Imaging Reporting and Data System

IOTA

International Ovarian Tumor Analysis

IRA

Inter-reviewer agreement

NPV

Negative predictive value

PPV

Positive predictive value

ROC

Receiver operating characteristic

TV

Transvaginal

US

Pelvic ultrasound

Notes

Acknowledgements

The authors thank all staff members and colleagues in Radiology department-Zagazig University for their helpful cooperation.

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dr. Mohammad Abd Alkhalik Basha.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Ethical approval

Institutional Review Board approval was obtained.

Informed consent

Written informed consent was obtained from all patients.

Statistics and biometry

The corresponding author has great statistical expertise.

Methodology

• Prospective

• Diagnostic or prognostic study

• Multicenter study

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Mohammad Abd Alkhalik Basha
    • 1
    Email author
  • Rania Refaat
    • 2
  • Safaa A. Ibrahim
    • 3
  • Nadia M. Madkour
    • 3
  • Awad Mahmoud Awad
    • 4
  • Elshaimaa Mohamed Mohamed
    • 1
  • Ahmed A. El Sammak
    • 1
  • Mohamed M. A. Zaitoun
    • 1
  • Hitham A. Dawoud
    • 1
  • Mai E. M. Khamis
    • 1
  • Heba A. E. Mohamed
    • 1
  • Ahmed Mohamed El-Maghraby
    • 1
  • Ahmed A. El-Hamid M. Abdalla
    • 1
  • Mostafa Mohamad Assy
    • 1
  • Mohamad Gamal Nada
    • 1
  • Ahmed Ali Obaya
    • 5
  • Eman H. Abdelbary
    • 6
  1. 1.Department of RadiodiagnosisZagazig UniversityZagazigEgypt
  2. 2.Department of RadiodiagnosisAin Shams UniversityCairoEgypt
  3. 3.Department of Obstetrics & GynecologyZagazig UniversityZagazigEgypt
  4. 4.Department of Obstetrics & GynecologyAl-Azhar UniversityCairoEgypt
  5. 5.Department of Clinical OncologyZagazig UniversityZagazigEgypt
  6. 6.Department of PathologyZagazig UniversityZagazigEgypt

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