RESUMO
BACKGROUND: Inter-observer agreement for the American Association of Gynecologic Laparoscopists (AAGL) 2021 Endometriosis Classification staging system has not been described. Its predecessor staging system, the revised American Society for Reproductive Medicine (rASRM), has historically demonstrated poor inter-observer agreement. AIMS: We aimed to determine the inter-observer agreement performance of the AAGL 2021 Endometriosis Classification staging system, and compare this with the rASRM staging system. MATERIALS AND METHODS: A database of 317 patients with coded surgical data was retrospectively analysed. Three independent observers allocated AAGL surgical stages (1-4), twice. Observers made their own interpretation of how to apply the tool in the first staging allocation. Consensus rules were then developed for a second staging allocation. RESULTS: First staging allocation: odds ratio (OR) (and 95% CI) for observer 1 to score higher than observer 2 was 8.08 (5.12-12.76). Observer 1 to score higher than observer 3 was 12.98 (7.99-21.11) and observer 2 to score higher than observer 3 was 1.61 (1.03-2.51). This represents poor agreement. Second staging allocation (after consensus): OR for observer 1 to score higher than observer 2 was 1.14 (0.64-2.03), observer 1 to score higher than observer 3 was 1.81 (0.99-3.28) and observer 2 to score higher than observer 3 was 1.59 (0.87-2.89). This represents good agreement. CONCLUSIONS: These findings suggest that in its current format the AAGL 2021 Endometriosis Classification staging system has poor inter-observer agreement, not superior to the rASRM staging system. However, performance improved when additional measures were taken to simplify and clarify areas of ambiguity in interpreting the staging system.
RESUMO
Endometriosis affects 1 in 9 women, taking 6.4 years to diagnose using conventional laparoscopy. Non-invasive imaging enables timelier diagnosis, reducing diagnostic delay, risk and expense of surgery. This review updates literature exploring the diagnostic value of specialist endometriosis magnetic resonance imaging (eMRI), nuclear medicine (NM) and computed tomography (CT). Searching after the 2016 IDEA consensus, 6192 publications were identified, with 27 studies focused on imaging for endometriosis. eMRI was the subject of 14 papers, NM and CT, 11, and artificial intelligence (AI) utilizing eMRI, 2. eMRI papers describe diagnostic accuracy for endometriosis, methodologies, and innovations. Advantages of eMRI include its: ability to diagnose endometriosis in those unable to tolerate transvaginal endometriosis ultrasound (eTVUS); a panoramic pelvic view, easy translation to surgical fields; identification of hyperintense iron in endometriotic lesions; and ability to identify super-pelvic lesions. Sequence standardization means eMRI is less operator-dependent than eTVUS, but higher costs limit its role to a secondary diagnostic modality. eMRI for deep and ovarian endometriosis has sensitivities of 91-93.5% and specificities of 86-87.5% making it reliable for surgical mapping and diagnosis. Superficial lesions too small for detection in larger capture sequences, means a negative eMRI doesn't exclude endometriosis. Combined with thin sequence capture and improved reader expertise, eMRI is poised for rapid adoption into clinical practice. NM labeling is diagnostically limited in absence of suitable unique marker for endometrial-like tissue. CT studies expose the reproductively aged to radiation. AI diagnostic tools, combining independent eMRI and eTVUS endometriosis markers, may result in powerful capability. Broader eMRI use, will optimize standards and protocols. Reporting systems correlating to surgical anatomy will facilitate interdisciplinary preoperative dialogues. eMRI endometriosis diagnosis should reduce repeat surgeries with mental and physical health benefits for patients. There is potential for early eMRI diagnoses to prevent chronic pain syndromes and protect fertility outcomes.