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781 results for “Endometriosis”
Urinary biomarkers for the non-invasive diagnosis of endometriosis: a systematic literature review
<p>Raw dataset from a systematic literature review performed until August 2021 in which the research question was 'Are there urine biomarkers sensitive and specific enough for endometriosis detection?' and the Medical Subject Heading (MeSH) terms were (endometrios*) AND (urin*). </p>
Transcriptomic characterisation of human menstrual fluid at homeostasis and in endometriosis
Open the record for dataset details and reuse information.
TRANSFORM video - First-person experiences of endometriosis in Catalonia (health pilot)
<p>Endometriosis affects 10% of girls, women (and sex-gender diversities) of reproductive age worldwide. Its diagnosis takes between 8 and 10 years from the onset of symptoms, a time during which these people suffer serious physical, psychological, social and professional consequences. Participatory research on endometriosis has resulted in the co-creation of 6 general and 28 specific recommendations, written in the first person by patients, with the aim of informing public health policies and contributing to improving the clinical approach and care services to patients.</p> <p>Results: https://zenodo.org/record/7248108#.Y-IiEC0w2X1</p> <p>TRANSFORM project has received funding from the European Union's Horizon2020 research and innovation programme under grant agreement No 872687. This video reflects only the author's view and the funding organisations are not responsible for any use that may be made of the information it contains.</p>
Pre-IVF Treatment With a GnRH Antagonist in Women With Endometriosis
ClinicalTrials.gov study NCT04173169. IPD Sharing: YES. Countries: 1. Publications: 2.
UTHealth - Endometriosis MRI Dataset (UT-EndoMRI)
<h2>Introduction</h2> <p>Magnetic Resonance Imaging (MRI) is widely recommended as a primary non-invasive diagnostic tool for endometriosis. Endometriomas affect 17–44% of women diagnosed with the condition. Accurate MRI-based ovary segmentation in endometriosis patients is essential for detecting endometriomas, guiding surgery, and predicting post-operative complications. However, ovary segmentation becomes challenging when the ovary is deformed or absent, often due to surgical resection, emphasizing the need for highly experienced clinicians. An automatic segmentation pipeline for pelvic MRI in endometriosis patients could greatly reduce the manual workload for clinicians and help standardize ovary segmentation.</p> <p>The UTHealth Endometriosis MRI Dataset (UT-EndoMRI) includes multi-sequence MRI scans and structural labels collected from two clinical institutions, Memorial Hermann Hospital System and Texas Children’s Hospital Pavilion for Women. The first dataset comprises MRI scans and labels from 51 patients collected before 2022, featuring T2-weighted and T1-weighted fat-suppressed MRI sequences. The uterus, ovaries, endometriomas, cysts, and cul-de-sac structures were manually segmented by three raters. The second dataset, collected in 2022, consists of MRI scans and labels from 82 endometriosis patients. These sequences include T1-weighted, T1-weighted fat suppression, T2-weighted, and T2-weighted fat suppression MRI. In this dataset, the uterus, ovaries, and endometriomas were manually contoured by a single rater. Using these datasets, we investigated interrater agreement and developed an automatic ovary segmentation pipeline, RAovSeg, for endometriosis.</p> <p>The study and the data sharing were approved by the Committee for the Protection of Human Subjects at UTHealth (protocol no. HSC-SBMI-22-0184). The UT-EndoMRI dataset is available for free use exclusively in non-commercial scientific research. </p> <p> </p> <p> </p> <h2>Endometriosis MRI</h2> <p>This dataset includes MRI scans and labels from two clinical institutions. The data from the first institution can be found in the ```D1_MHS/ ```directory, while the data from the second institution are located in the ```D2_TCPW/``` directory. Each subfolder contains MRI scans and corresponding labels from different raters.</p> <p>The naming conventions for the files are as follows:</p> <p>MRI scans:<br>D[dataset ID]- [patient ID] _ [MRI sequence].nii.gz</p> <p>Anatomical structure labels:<br>D[dataset ID]- [patient ID] _ [structure name] _ r[rater ID].nii.gz</p> <p>For the labels in the ```D2_TCPW/ ```directory, since they were generated by a single rater, there is no rater ID included in the file names.</p> <p>The abbreviations used for naming:<br>T1: T1-weighted MRI<br>T1FS: T1-weighted fat suppression MRI<br>T2: T2-weighted MRI<br>T2FS: T2-weighted fat suppression MRI<br>ov: ovary<br>ut: uterus<br>em: endometrioma<br>cy: cyst<br>cds: cul de sac</p> <p>For example, the file located at ```UT-EndoMRI/D1_MHS/D1-000/D1-000_T1FS.nii.gz```represents the T1-weighted fat suppression MRI for subject 000 in dataset 1. The file at ```UT-EndoMRI/D1_MHS/D1-000/D1-000_ ut_r1.nii.gz``` is the uterus segmentation manually contoured by rater 1 for subject 000 in dataset 1. The file at```UT-EndoMRI/ D2_TCPW/D2-006/D2-006_ cy.nii.gz``` is the cyst segmentation manually contoured for subject 006 in dataset 2.</p> <p>MRI sequences may be missing due to a lack of acquisition.</p> <p>The MR Scanner information for Dataset 1 is available in 'SiteScannerInfo.csv'.</p> <p> </p> <h2>Train/Validation/Test Replication</h2> <p>The data split for RAovSeg training, validation, and testing is provided as follows:<br>- Training/validation subjects IDs: D2-000 – D2-007<br>- Testing subjects IDs: D2-008 – D2-037<br>All data in dataset 1, as well as other data in dataset 2, are not used in RAovSeg development.</p> <p> </p> <h2>Data Acquisition</h2> <p>This dataset was acquired at the Texas Medical Center, within the Memorial Hermann Hospital System and the Texas Children’s Hospital Pavilion for Women. The study and the data sharing were approved by the Committee for the Protection of Human Subjects at UTHealth (protocol no. HSC-SBMI-22-0184).</p> <p> </p> <h2>User Agreement</h2> <p>The UT-EndoMRI dataset is available for free use exclusively in non-commercial scientific research. Any publications resulting from its use must cite the following paper.<br><br><a href="https://www.nature.com/articles/s41597-025-05623-3" target="_blank" rel="noopener">Liang, X., Alpuing Radilla, L.A., Khalaj, K. <em>et al.</em> A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis. <em>Sci Data</em> <strong>12</strong>, 1292 (2025). https://doi.org/10.1038/s41597-025-05623-3</a></p> <h2>Funding</h2> <p>This work has been supported by the Robert and Janice McNair Foundation.</p> <p> </p> <h2>Research Team</h2> <p>Here are the people behind this data acquisition effort:<br>Xiaomin Liang, Linda A Alpuing Radilla, Kamand Khalaj, Haaniya Dawoodally, Chinmay Mokashi, Xiaoming Guan, Kirk E Roberts, Sunil A Sheth, Varaha S Tammisetti, Luca Giancardo</p> <h2>Acknowledgements</h2> <p>We would also like to acknowledge for their support: Memorial Hermann Hospital System and Texas Children’s Hospital Pavilion for Women.</p>
Supplemental information for: Inhibition of CSF1R and KIT with pexidartinib reduces inflammatory signaling and cell viability in endometriosis
<p>Endometriosis is a common and debilitating disease, affecting ~170 million women worldwide. Affected patients have limited therapeutic options such as hormonal suppression or surgical excision of the lesions, though therapies are often not completely curative. Targeting receptor tyrosine-kinases (RTKs) could provide a nonhormonal treatment option for endometriosis. We determined that two RTKs, Macrophage colony stimulating factor receptor (CSF1R) and Mast/stem cell growth factor receptor KIT (KIT), are overexpressed in endometriotic lesions and could be novel nonhormonal therapeutic targets for endometriosis. The kinase activity of CSF1R and KIT is suppressed by pexidartinib, a small molecule inhibitor that was recently approved by the US Food and Drug Administration (FDA). Using immunohistochemistry, we detected CSF1R and KIT in endometriotic tissues obtained from peritoneal lesions, colorectal lesions, and endometriomas. Specifically, we show that KIT is localized to the epithelium of the lesions, while CSF1R is expressed in the stroma and macrophages of the endometriotic lesions. Given the high epithelial expression of CSF1R and KIT, 12Z endometriotic epithelial cells were used to evaluate the efficacy of dual CSF1R and KIT inhibition with pexidartinib. We found that pexidartinib suppressed activation in 12Z cells of JNK, STAT3 and AKT signaling pathways, which control key pro-inflammatory and survival networks within the cell. Using quantitative real time PCR, we determined that pexidartinib suppressed interleukin 8 (<em>IL8) </em>and cyclin D1 (<em>CCND1) </em>expression<em>.</em> Lastly, we demonstrated that pexidartinib decreased cell growth and viability.<em> </em>Overall, these results indicate that pexidartinib-mediated CSF1R and KIT inhibition reduces pro-inflammatory signaling and cell viability in endometriosis.</p>
Complementary authentication of Chinese herbal products to treat endometriosis using DNA metabarcoding and HPTLC shows a high level of variability
<p>Traditional Chinese Medicine (TCM) is popular for the treatment of endometriosis, a complex gynecological disease that affects 10% of women globally. The growing market for TCMs has yielded a significant incentive for product adulteration, and although emerging technologies show promise to improve their quality control, many challenges remain. We tested the authenticity of two traditional Chinese herbal formulae used in women’s healthcare for the treatment of endometriosis, known as <em>Gui Zhi Fu Ling Wan</em> (FL) and <em>Ge Xia Zhu Yu Tang</em> (GX). Dual-locus DNA metabarcoding analysis coupled with high-performance thin-layer chromatography (HPTLC) were used to authenticate 19 FL and six GX commercial herbal products, as well as three ad hoc prepared artificial mixtures. HPTLC was able to detect most of the expected ingredients via comparative component analysis. DNA metabarcoding was able to detect an unexpected species diversity in the products, including 38 unexpected taxa. Chromatography has resolution for all species indirectly through identification of marker compounds for the different species ingredients. Metabarcoding on the other hand yields an overview of species diversity in each sample, but interpretation of the results can be challenging. Detected species might not be present in quantities that matter, and without validated quantification some detected species can be hard to interpret. Comparative analysis of the two analytical approaches also reveals that DNA for species might be absent or too fragmented to amplify as the relevant chemical marker compounds can be detected but no amplicons assigned to the same species. Our study emphasizes that integrating DNA metabarcoding with phytochemical analysis brings valuable data for comprehensive authentication of Traditional Chinese Medicines ensuring their quality and safe use.</p>
Study of the Safety and Contraceptive Efficacy of Relugolix Combination Therapy in Women With Uterine Fibroids or Endometriosis Who Are at Risk for Pregnancy
ClinicalTrials.gov study NCT04756037. IPD Sharing: NO. Countries: 2. Publications: 1.
Efficacy of Physical Therapy Treatment in Endometriosis and Its Relationship to Adherence Rates.
ClinicalTrials.gov study NCT06795243. IPD Sharing: YES. Countries: 1. Publications: 30.
Evolution of Endometriosis Lesions Followed by Ultrasound and Quality of Life of Patients: Factors That Influence Disease Progression in a Prospective Cohort
ClinicalTrials.gov study NCT07282990. IPD Sharing: YES. Countries: 1. Publications: 15.
SPIRIT EXTENSION: Efficacy and Safety Extension Study of Relugolix in Women With Endometriosis-Associated Pain
ClinicalTrials.gov study NCT03654274. IPD Sharing: NO. Countries: 20. Publications: 2.
Dopamine Receptor Agonist Therapy for Pain Relief in Women Suffering From Endometriosis: A Pilot Study
ClinicalTrials.gov study NCT02542410. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Clinical Study to Evaluate the Safety and Efficacy of Elagolix in Participants With Moderate to Severe Endometriosis-Associated Pain
ClinicalTrials.gov study NCT03213457. IPD Sharing: YES. Countries: 3. Publications: 1.
Assess Safety and Efficacy of Vilaprisan in Subjects With Endometriosis
ClinicalTrials.gov study NCT03573336. IPD Sharing: NO. Countries: 8. Publications: 1.
Safety and Efficacy of Telapristone Acetate (Proellex®) in the Treatment of Pre-Menopausal Women With Confirmed, Symptomatic Endometriosis
ClinicalTrials.gov study NCT01728454. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Survey on Efficacy and Safety in Patients With Endometriosis
ClinicalTrials.gov study NCT02921763. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Laparoscopy vs. Robotic Surgery for Endometriosis (LAROSE): a Prospective Randomized Controlled Trial
ClinicalTrials.gov study NCT01556204. IPD Sharing: NO. Countries: 1. Publications: 1.
The Use of Low Dose Metronidazole to Decrease Postoperative Pain After Endometriosis Surgery
ClinicalTrials.gov study NCT04554693. IPD Sharing: NO. Countries: 1. Publications: 14.
A Study to Assess the Efficacy and Safety of OBE2109 in Subjects With Endometriosis
ClinicalTrials.gov study NCT02778399. IPD Sharing: Not stated. Countries: 4. Publications: 1.
The CRESCENDO Program (inCRease Physical Exercise and Sport to Combat ENDOmetriosis)
ClinicalTrials.gov study NCT05831735. IPD Sharing: YES. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.