MicroRNome analysis generates a blood-based signature for endometriosis

Bendifallah, Sofiane; Dabi, Yohann; Suisse, Stéphane; Jornéa, Ludmila; Bouteiller, Delphine; Touboul, Cyril; Puchar, Anne; Daraı̈, Émile

doi:10.1038/s41598-022-07771-7

Cited by 29 publications

(30 citation statements)

References 63 publications

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“…AI interventions that were built using biomarker inputs included diagnostic and predictive models for ultrasound-negative endometriosis 34 , and ovarian endometriomas 33 . Biomarker inputs for these models included plasma biomarkers collected in all phases of the menstrual cycle 34 , lipidomic profiling of endometrial fluid 33 , and serum miRNA markers 35 . AI interventions built using metabolite spectra as their primary input included detecting endometriosis in serum samples 43,44 , screening for biomarkers in eutopic endometrium 26 , diagnosing ultrasound-negative endometriosis 40 , diagnosing endometriosis using messenger RNA expression in endometrium biopsies 41 , identifying predictive serum biomarkers 42 , diagnosing and staging endometriosis using peptide profiling 39 , determining classifier metabolites for early prediction risk 38 , and diagnosing stage 3 and stage 4 endometriosis in infertile patients 36 .…”

Section: Discussionmentioning

confidence: 99%

“…It should be noted that among the four studies that were examined, there were no commonalities in the specific biomarker inputs used; thus, it is difficult to compare the accuracy of each AI model given the differences in the inputs used. The pooled SE and SP for each study's most accurate model were 85.6% and 85%, respectively [33][34][35] .…”

Section: Study Characteristicsmentioning

confidence: 97%

“…Diagnostic or predictive models for endometriosis using biomarkers Four different studies [31][32][33][34][35] examined the use of biomarkers as inputs to create diagnostic or predictive AI models in endometriosis populations. As seen in Table 2, the type of biomarkers used differed among the studies.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Knific et al 31 was the only study that used protein ratios while others used metabolites 33 , miRNAs 35 and other biomarkers 34 . Knific et al 31 and Bendifallah et al 35 were the only studies in this category to use the random-forest method to develop a diagnostic model for endometriosis and the accuracy of Knific et al's 31 model was reported to be 59% 31 -the lowest accuracy for all the models in this category-while the clinical accuracy of Bendifallah et al's 35 model was significantly higher with a sensitivity and specificity of 96.8 and 100%. One study used LSSVMs 34 and the accuracy of this method was deemed to be 79% with a sensitivity and specificity of 82% and 75%, respectively.…”

Section: Study Characteristicsmentioning

confidence: 99%

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Clinical use of artificial intelligence in endometriosis: a scoping review

et al. 2022

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Endometriosis is a chronic, debilitating, gynecologic condition with a non-specific clinical presentation. Globally, patients can experience diagnostic delays of ~6 to 12 years, which significantly hinders adequate management and places a significant financial burden on patients and the healthcare system. Through artificial intelligence (AI), it is possible to create models that can extract data patterns to act as inputs for developing interventions with predictive and diagnostic accuracies that are superior to conventional methods and current tools used in standards of care. This literature review explored the use of AI methods to address different clinical problems in endometriosis. Approximately 1309 unique records were found across four databases; among those, 36 studies met the inclusion criteria. Studies were eligible if they involved an AI approach or model to explore endometriosis pathology, diagnostics, prediction, or management and if they reported evaluation metrics (sensitivity and specificity) after validating their models. Only articles accessible in English were included in this review. Logistic regression was the most popular machine learning method, followed by decision tree algorithms, random forest, and support vector machines. Approximately 44.4% (n = 16) of the studies analyzed the predictive capabilities of AI approaches in patients with endometriosis, while 47.2% (n = 17) explored diagnostic capabilities, and 8.33% (n = 3) used AI to improve disease understanding. Models were built using different data types, including biomarkers, clinical variables, metabolite spectra, genetic variables, imaging data, mixed methods, and lesion characteristics. Regardless of the AI-based endometriosis application (either diagnostic or predictive), pooled sensitivities ranged from 81.7 to 96.7%, and pooled specificities ranged between 70.7 and 91.6%. Overall, AI models displayed good diagnostic and predictive capacity in detecting endometriosis using simple classification scenarios (i.e., differentiating between cases and controls), showing promising directions for AI in assessing endometriosis in the near future. This timely review highlighted an emerging area of interest in endometriosis and AI. It also provided recommendations for future research in this field to improve the reproducibility of results and comparability between models, and further test the capacity of these models to enhance diagnosis, prediction, and management in endometriosis patients.

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Section: Discussionmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 97%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

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Clinical use of artificial intelligence in endometriosis: a scoping review

et al. 2022

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“…At present, there is no validated miRNA-base diagnosis test for the non-invasive detection of endometriosis. However, the potential use of circulating miRNAs as non-invasive biomarkers for endometriosis has been considered as an ongoing research area and its diagnostic and therapeutic implications are being extensively studied [ 39 , 41 , 43 , 55 , 56 ].…”

Section: Micrornas As a New Diagnostic Biomarker For Endometriosismentioning

confidence: 99%

MicroRNAs Dysregulation as Potential Biomarkers for Early Diagnosis of Endometriosis

et al. 2022

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Endometriosis is a benign chronic disease in women that is characterized by the presence of active foci of the endometrium or endometrial tissue occurring outside of the uterus. The disease causes disabling symptoms such as pelvic pain and infertility, which negatively affect a patient’s quality of life. In addition, endometriosis imposes an immense financial burden on the healthcare system. At present, laparoscopy is the gold standard for diagnosing the disease because other non-invasive diagnostic tests have less accuracy. In addition, other diagnostic tests have low accuracy. Therefore, there is an urgent need for the development of a highly sensitive, more specific, and non-invasive test for the early diagnosis of endometriosis. Numerous researchers have suggested miRNAs as potential biomarkers for endometriosis diagnosis due to their specificity and stability. However, the greatest prognostic force is the determination of several miRNAs, the expression of which varies in a given disease. Despite the identification of several miRNAs, the studies are investigatory in nature, and there is no consensus on them. In the present review, we first provide an introduction to the dysregulation of miRNAs in patients with endometriosis and the potential use of miRNAs as biomarkers in the detection of endometriosis. Then we will describe the role of the mir-200 family in endometriosis. Several studies have shown that the expression of the mir-200 family changes in endometriosis patients, suggesting that they could be used as a diagnostic biomarker and therapeutic target for endometriosis.

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Clues to revising the conventional diagnostic algorithm for endometriosis

Spiers,

Roman,

Wasson

et al. 2024

Intl J Gynecology & Obste

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Endometriosis is a complex gynecologic disorder characterized primarily by symptoms of pelvic pain, infertility, and altered quality of life. National and international guidelines highlight the diagnostic difficulties and lack of conclusive diagnostic tools for endometriosis. Furthermore, guidelines are becoming questionable at an increasingly rapid rate as new diagnostic techniques emerge. This work aims to provide a knowledge synthesis of the relevance of various diagnostic tools and to assess areas of improvement of conventional algorithms. MEDLINE and Cochrane Library databases were searched from January 2021 to December 2023 using relevant key words. Articles evaluating the diagnostic relevance and performance of various tools were included and independently reviewed by the authors for eligibility. Included studies were assessed using the GRADE and QUADAS‐2 tools. Of the 4204 retrieved articles, 26 were included. While anamnesis and clinical examination do contribute to diagnostic accuracy, their level of evidence and impact on the diagnostic process remains limited. Although imaging techniques are recommended to investigate endometriosis, ultrasonography remains highly operator dependent. Magnetic resonance imaging appears to exhibit higher sensitivities than ultrasound. However, concerns persist with regards to the terminology, anatomical definition of lesions, and accuracies of both ultrasound and magnetic resonance imaging. Recently, several biological markers have been studied and cumulative evidence supports the contribution of noncoding RNAs to the diagnosis of endometriosis. Marginal improvements have been suggested for anamnesis, clinical examination, and imaging examinations. Conversely, some biomarkers, including the saliva microRNA signature for endometriosis, have emerged as diagnostic tools which inspire reflection on the revision of conventional diagnostic algorithms.

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MicroRNome analysis generates a blood-based signature for endometriosis

Cited by 29 publications

References 63 publications

Clinical use of artificial intelligence in endometriosis: a scoping review

Clinical use of artificial intelligence in endometriosis: a scoping review

MicroRNAs Dysregulation as Potential Biomarkers for Early Diagnosis of Endometriosis

Clues to revising the conventional diagnostic algorithm for endometriosis

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