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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EP24.14: External validation study of preoperative ultrasound‐based AAGL staging system in prediction of AAGL surgical stage

C. Uzuner, G. Condous, S. Reid, Mercedes Espada et al.
Ultrasound in Obstetrics and Gynecology
Radiomics and Machine Learning in Medical Imaging
article

EP24.14: External validation study of preoperative ultrasound‐based AAGL staging system in prediction of AAGL surgical stage

C. Uzuner, G. Condous, S. Reid, Mercedes Espada, Jason Mak, Allie Eathorne, P. McClenahan
article en

Abstract

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.

Ultrasound in Obstetrics and GynecologyVol. 64(S1)
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)
Openalex Percentile: Top 75%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.