The delay between endometriosis symptoms onset and diagnosis is on average 7 years, in part due to the invasive nature of a definitive diagnosis – surgical visualization of endometriotic lesions. While numerous studies have investigated non-invasive methods for diagnosing endometriosis, none have produced a valid diagnostic tool with sufficient sensitivity and specificity to replace or to triage for laparoscopic surgery. This may be due to a lack of incorporating informative heterogeneity among patient symptoms, endometriosis lesion types and endometriosis risk factors into quantification of biologic markers to develop a diagnostic tool. Once a patient is diagnosed with endometriosis, conventional treatments include the use of hormonal medications to suppress ovulation and surgical removal of endometriotic lesions. However, approximately half of endometriosis patients will undergo a second endometriosis-related surgery within 7 years of their initial surgery, due to the recurrence or persistence of endometriosis-associated symptoms. Currently, no biologic markers or algorithms exist for determining which endometriosis patients will experience recurrent symptoms and which will not.
For this Research Topic, we would like to address the incorporation of biological markers as well as informative participant characteristics into models for (1) endometriosis diagnosis and (2) response to treatment among endometriosis patients. Biological markers can be measured in any relevant source including blood, saliva, and urine, and can incorporate a wide range of biological marker types, for example - genomics, metabolomics, inflammation, and oxidative stress. Informative participant characteristics can include symptom profiles, age at presentation, anthropometric characteristics, behavioral factors, and other known or novel potentially relevant characteristics. The overarching goal is to create a collection of manuscripts that paves the way forward in finding algorithms that will be clinically useful for endometriosis diagnosis and algorithms for predicting response to different types of endometriosis-associated treatments.
Manuscripts submitted for this Research Topic can be geared towards clinical care providers, patients at different stages in their journey with endometriosis, and population and basic scientists and biomedical engineers who are attempting to apply their expertise to advance discoveries of endometriosis diagnostics and treatment prediction. In these manuscripts, it will be important for clarity as to whether the associations that are reported or methods that are recommended are inferring to diagnosis or treatment response among all people with endometriosis or sub-populations of people with endometriosis, e.g. only those with dysmenorrhea or only those with resistant acyclic pelvic pain or those with deep lesions.
The delay between endometriosis symptoms onset and diagnosis is on average 7 years, in part due to the invasive nature of a definitive diagnosis – surgical visualization of endometriotic lesions. While numerous studies have investigated non-invasive methods for diagnosing endometriosis, none have produced a valid diagnostic tool with sufficient sensitivity and specificity to replace or to triage for laparoscopic surgery. This may be due to a lack of incorporating informative heterogeneity among patient symptoms, endometriosis lesion types and endometriosis risk factors into quantification of biologic markers to develop a diagnostic tool. Once a patient is diagnosed with endometriosis, conventional treatments include the use of hormonal medications to suppress ovulation and surgical removal of endometriotic lesions. However, approximately half of endometriosis patients will undergo a second endometriosis-related surgery within 7 years of their initial surgery, due to the recurrence or persistence of endometriosis-associated symptoms. Currently, no biologic markers or algorithms exist for determining which endometriosis patients will experience recurrent symptoms and which will not.
For this Research Topic, we would like to address the incorporation of biological markers as well as informative participant characteristics into models for (1) endometriosis diagnosis and (2) response to treatment among endometriosis patients. Biological markers can be measured in any relevant source including blood, saliva, and urine, and can incorporate a wide range of biological marker types, for example - genomics, metabolomics, inflammation, and oxidative stress. Informative participant characteristics can include symptom profiles, age at presentation, anthropometric characteristics, behavioral factors, and other known or novel potentially relevant characteristics. The overarching goal is to create a collection of manuscripts that paves the way forward in finding algorithms that will be clinically useful for endometriosis diagnosis and algorithms for predicting response to different types of endometriosis-associated treatments.
Manuscripts submitted for this Research Topic can be geared towards clinical care providers, patients at different stages in their journey with endometriosis, and population and basic scientists and biomedical engineers who are attempting to apply their expertise to advance discoveries of endometriosis diagnostics and treatment prediction. In these manuscripts, it will be important for clarity as to whether the associations that are reported or methods that are recommended are inferring to diagnosis or treatment response among all people with endometriosis or sub-populations of people with endometriosis, e.g. only those with dysmenorrhea or only those with resistant acyclic pelvic pain or those with deep lesions.