Innovations in endometriosis diagnostics

Endometriosis is a chronic condition in which tissue similar to the lining of the uterus grows outside the uterine cavity, most commonly on the ovaries, fallopian tubes, and pelvic peritoneum, although endometriosis lesions can also be found in the chest cavity. These ectopic lesions can cause pain, inflammation, and the formation of scar tissue, and are frequently associated with symptoms such as severe dysmenorrhea, chronic pelvic pain and infertility. It is estimated to affect approximately 1 in 10 women of reproductive age, equating to around 190 million people globally. Despite this, diagnosis takes on average of 8-10 years in the UK due to the non-specific nature of symptoms and limitations in existing diagnostic methods. The delay in diagnosis results in a delay in accessing treatment which may result in the disease progressing, leading to worsening symptoms and increasing the risk of permanent organ damage.

Currently, the gold standard for definitive diagnosis is laparoscopic surgery, an invasive procedure in which suspected lesions are visualised and biopsied. However, in clinical practice, diagnosis is usually preceded by a combination of symptom assessment, pelvic examination, imaging techniques such as ultrasound or MRI, and sometimes blood-based investigations, none of which alone can reliably confirm the disease. There is therefore a need to develop more accurate, non-invasive diagnostic tools to allow for earlier diagnosis.

Using Biomarkers for Detection of Endometriosis

Researchers at Yale School of Medicine have been developing a non-invasive diagnostic approach for endometriosis based on circulating microRNAs (miRNAs) in blood, with the aim of replacing or at least reducing reliance on diagnostic laparoscopy. The team at Yale conducted research which showed a six-miRNA serum panel could distinguish women with endometriosis from women who do not have the condition. More recently, the team reported a distinct microRNA signature in adolescents and young adults, supporting the idea that early-stage disease may have its own molecular profile. Further trials will be needed to develop a clinical test that can be used for diagnosis. This research is an important development as many women with endometriosis first experience symptoms during adolescence and earlier diagnosis means treatment can begin before symptoms worsen, and the risk of permanent organ damage may be reduced.

Yale School of Medicine has applied for a number of patent applications related to its research into use of biomarkers for diagnosis of endometriosis. See for instance WO2015148919A2  and WO2018044979A1

Use of Radiotracers in Diagnostic Imaging

A recent Phase II clinical study conducted by Serac Healthcare Ltd in collaboration with the Nuffield Department of Women’s & Reproductive Health has investigated a molecular imaging approach for the detection of endometriosis. In this study, patients with suspected or confirmed endometriosis underwent SPECT-CT imaging following administration of technetium-99m-maraciclatide, a gamma-emitting radiotracer designed to bind to αvβ3 integrins that are overexpressed during angiogenesis, which is the formation of new blood vessels from pre-existing ones.

The study showed a high correlation between imaging findings and surgical outcomes, with the technique accurately identifying disease in 16 out of 19 cases and showing no false positives. Notably, the method was able to visualise superficial peritoneal endometriosis, a common yet difficult-to-detect form of the disease that typically requires laparoscopy for confirmation. These findings suggest that molecular imaging targeting angiogenesis could provide a non-invasive alternative for both diagnosis and disease monitoring, although further validation in larger Phase III trials will be required before clinical adoption.

Serac Healthcare Ltd has filed a patent application WO2023166305A1  titled “Methods for Imaging Integrin Expression and for Imaging Sites of Endometriosis” in relation to their imaging technique.

Artificial Intelligence in Endometriosis Imaging

Matricis.ai is developing a platform known as EndomAI, which uses artificial intelligence to assist clinicians in interpreting pelvic MRI scans. The technology is trained on annotated imaging datasets and is designed to improve the detection and characterisation of endometriotic lesions, which are easily missed in conventional assessment of MRI scans. By providing outputs such as lesion mapping, the system aims to enhance both the sensitivity and consistency of MRI-based diagnosis, while also supporting clinical decision-making and surgical planning. This approach addresses the limitations of current imaging techniques, such as the variability in interpretation between radiologists, and has the potential to improve the reliability of MRIs as a diagnostic tool. Platforms such as EndomAI may aid clinicians in making earlier diagnoses of endometriosis without needing to rely on more invasive procedures.

Matricis.ai has filed a patent application FR3160879A1 titled “Device and Method for Aid in the Diagnosis of Pelvic Pathologies” in relation to this work.

As new diagnostic tools are developed, it is hoped that the average time women with endometriosis have to wait for a diagnosis will be reduced, leading to better health outcomes as treatments can begin before the condition spreads and causes irreversible damage.