Noninvasive diagnostic imaging for endometriosis part 2: a systematic review of recent developments in magnetic resonance imaging, nuclear medicine and computed tomography

Jodie C Avery; Steven Knox; Alison Deslandes; Mathew Leonardi; Glen Lo; Hu Wang; Yuan Zhang; Sarah Jane Holdsworth-Carson; Tran Tuyet Thi Nguyen; George Stanley Condous; Gustavo Carneiro; Mary Louise Hull; Imagendo Study Group

doi:10.1016/j.fertnstert.2023.12.017

Noninvasive diagnostic imaging for endometriosis part 2: a systematic review of recent developments in magnetic resonance imaging, nuclear medicine and computed tomography

Fertil Steril. 2024 Feb;121(2):189-211. doi: 10.1016/j.fertnstert.2023.12.017. Epub 2023 Dec 16.

Authors

Collaborators

Affiliations

¹ Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia. Electronic address: jodie.avery@adelaide.edu.au.
² Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Benson Radiology, Adelaide, Australia.
³ Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
⁴ Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Department of Obstetrics and Gynecology McMaster University, Hamilton, Canada.
⁵ Curtin University Medical School Perth, Australia.
⁶ Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Australian Institute for Machine Learning, University of Adelaide, Australia.
⁷ Julia Argyrou Endometriosis Centre, Epworth HealthCare, Richmond, Australia; Department of Obstetrics and Gynaecology, University of Melbourne, Parkville, Australia.
⁸ Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Embrace Fertility, Adelaide, Australia.
⁹ Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Omni Ultrasound and Gynaecological Care, Sydney Australia, (j)Department of Obstetrics and Gynaecology, University of Melbourne, Parkville, Australia.
¹⁰ Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; University of Surrey, Guildford, United Kingdom.

PMID: 38110143
DOI: 10.1016/j.fertnstert.2023.12.017

Abstract

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.

Keywords: Endometriosis; computed tomography; diagnosis; magnetic resonance imaging; nuclear medicine.

Publication types

Systematic Review
Review
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Artificial Intelligence
Delayed Diagnosis
Endometriosis* / diagnostic imaging
Endometriosis* / pathology
Female
Humans
Magnetic Resonance Imaging
Nuclear Medicine*
Tomography, X-Ray Computed
Ultrasonography / methods