The DOI matches its Crossref record; 210 of the 210 references it cites were found in a registry or an index.
Real world perspectives on endometriosis disease phenotyping through surgery, omics, health data, and artificial intelligence
Open accessAbstract
Endometriosis is an enigmatic disease whose diagnosis and management are being transformed through innovative surgical, molecular, and computational technologies. Integrating single-cell and other omic disease data with clinical and surgical metadata can identify multiple disease subtypes with translation to novel diagnostics and therapeutics. Herein, we present real-world perspectives on endometriosis and the importance of multidisciplinary collaboration in informing molecular, epidemiologic, and cell-specific data in the clinical and surgical contexts.
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“9 patients (3.81%) were classified as Stage I, 28 (11.86%) as Stage II, 58 (24.58%) as Stage III, and 141 (59.75%) as Stage IV”
From Selective Appendectomy in Patients Undergoing Minimally Invasive Surgery for Endometriosis: A Retrospective Cohort Study (2025) · cites this paper as [17]
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Citations
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Metadata
- Authors
- Nezhat, C. R., Oskotsky, T. T., Robinson, J. F., Fisher, S. J., Tsuei, A., Liu, B., Irwin, J. C., Gaudilliere, B., Sirota, M., Stevenson, D. K., & Giudice, L. C.
- Affiliations
- University of California, San Francisco (US)
- Center for Special Minimally Invasive and Robotic Surgery (US)
- Stanford Medicine (US)
- Stanford University (US)
- Corresponding author
- Nezhat, Camran R.
- Giudice, Linda C.
- ORCID
- Venue
- npj Women s Health
- Published
- 2025-02-06
- Volume
- 3
- Issue
- 1
- Pages
- 8-8
- ISSN
- 2948-1716 (ISSN-L)
- PMC
- PMC11802455
- OpenAlex
- W4407167810
- Cited by
- 25
- References
- 209
- Citations by year
- 2026: 15 · 2025: 10
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- npj Women s Health (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health (HD P01 106414)
- Topics
- Health Sciences › Medicine › Reproductive Medicine › Endometriosis Research and Treatment
- Health Sciences › Medicine › Obstetrics and Gynecology › Endometrial and Cervical Cancer Treatments
- Health Sciences › Medicine › Obstetrics and Gynecology › Gynecological conditions and treatments
- Keywords
- endometriosis, disease phenotyping, endometriosis subtypes, single-cell omics, surgical data, health data, artificial intelligence, endometriosis diagnosis, multidisciplinary collaboration
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