Risk-stratification machine learning model using demographic factors, gynaecological symptoms and β-catenin for endometrial hyperplasia and carcinoma: a cross-sectional study

Risk-stratification machine learning model using demographic factors, gynaecological symptoms and β-catenin for endometrial hyperplasia and carcinoma: a cross-sectional study

Abstract Background Demographic features, suggestive gynaecological symptoms, and immunohistochemical expression of endometrial β-catenin have a prognostic capacity for endometrial hyperplasia and carcinoma. This study assessed the interaction of all variables and developed risk stratification for e...

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Bibliographic Details
Main Authors: Rina Masadah, Aries Maulana, Berti Julian Nelwan, Mahmud Ghaznawie, Upik Anderiani Miskad, Suryani Tawali, Syahrul Rauf, Bumi Herman
Format: Article
Language:English
Published: BMC 2023-11-01
Series:BMC Women's Health
Subjects:
β-catenin
Endometrial hyperplasia
Endometrial carcinoma
Gynecological symptoms
Immunohistochemistry staining
Risk-stratification
Online Access:https://doi.org/10.1186/s12905-023-02790-6

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