A machine learning-based immune response signature to facilitate prognosis prediction in patients wi...
A machine learning-based immune response signature to facilitate prognosis prediction in patients with endometrial cancer
About this item
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Author / Creator
Wang, Xiaofeng , Guan, Jing , Feng, Li , Li, Qingxue , Zhao, Liwei , Li, Yue , Ma, Ruixiao , Shi, Mengnan , Han, Biaogang , Hao, Guorong , Wang, Lina , Li, Hui and Wang, Xiuli
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
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Publication information
Publisher
London: Nature Publishing Group UK
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Scope and Contents
Contents
Endometrial cancer is the most prevalent form of gynecologic malignancy, with a significant surge in incidence among youngsters. Although the advent of the immunotherapy era has profoundly improved patient outcomes, not all patients benefit from immunotherapy; some patients experience hyperprogression while on immunotherapy. Hence, there is a press...
Alternative Titles
Full title
A machine learning-based immune response signature to facilitate prognosis prediction in patients with endometrial cancer
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_d15adb9650b248b58356d8aea42aa1af
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d15adb9650b248b58356d8aea42aa1af
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-81040-7
