EP24.14: External validation study of preoperative ultrasound‐based AAGL staging system in prediction of AAGL surgical stage
To externally validate the AAGL-US in prediction of AAGL surgical stage (AAGL-SS) following previously published data in 2022. A retrospective multicentre study conducted from August 2018-November 2019 recruited patients with possible endometriosis with data presented previously in separate analysis. Patients underwent a deep endometriosis ultrasound conducted in accordance with the IDEA consensus prior to surgical assessment and management. AAGL-US stage (1-4) was documented retrospectively while AAGL-SS (1-4) and surgical complexity (A-D) was assessed based on findings at time of surgery. A total of 236 patients were included in the analysis. The weighted kappa score for performance of AAGL-US in predicting AAGL-SS was 0.5 demonstrating moderate agreement. Weighted kappa score of performance of AAGL-US in predicting of surgical complexity A-D was 0.38 demonstrating fair agreement. Intraclass correlation has 'good' reliability for right ovary, left ovary, bladder and rectum with ICC scores 0.83, 0.84, 0.80 and 0.76. Pouch of douglas obliteration demonstrated moderate reliability at 0.53. Accuracy statistics (sensitivity, specificity, positive predictive value, and negative predictive value) for stage 1 predicting level A was 87%, 81%, 47% and 97%, stage 2 predicting level B was 32%, 88%, 64% and 65%, stage 3 predicting level C was 22%, 85%, 26% and 81% and stage 4 predicting level D was 61%, 75%, 43% and 86%. Diagnostic accuracy of AAGL-US predicting presence of any endometriosis gave sensitivity 0.56, specificity 0.60, positive predictive value 0.97, negative predictive value 0.23. There is moderate agreement between AAGL-US and AALG-SS with weighted kappa 0.52 lower than the previously reported 0.76. While there was fair agreement (weighted kappa 0.38) between AAGL-US and surgical complexity. ICC was similar in assessment of ovaries, bladder and recal disease to previously reported data.
Authors
- C. Uzuner (ORCID: https://orcid.org/0000-0002-9480-3095)
- G. Condous (ORCID: https://orcid.org/0000-0003-3858-3080)
- S. Reid
- Mercedes Espada (ORCID: https://orcid.org/0000-0002-4330-6144)
- Jason Mak
- Allie Eathorne (ORCID: https://orcid.org/0000-0002-0706-044X)
- P. McClenahan (ORCID: https://orcid.org/0009-0009-8229-8210)
Institutions
- Camden and Campbelltown Hospitals (AU)
- Nepean Hospital (AU)
- Australian Federation of Graduate Women New South Wales (AU)
- Medical Research Institute of New Zealand (NZ)
- Central Coast Local Health District (AU)
- Western Sydney University (AU)
Publication Details
- Journal
- Ultrasound in Obstetrics and Gynecology
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1002/uog.29021
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00