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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Women's Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12905-023-02790-6 |
| _version_ | 1797414145921908736 |
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| author | Rina Masadah Aries Maulana Berti Julian Nelwan Mahmud Ghaznawie Upik Anderiani Miskad Suryani Tawali Syahrul Rauf Bumi Herman |
| author_facet | Rina Masadah Aries Maulana Berti Julian Nelwan Mahmud Ghaznawie Upik Anderiani Miskad Suryani Tawali Syahrul Rauf Bumi Herman |
| author_sort | Rina Masadah |
| collection | DOAJ |
| description | 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 endometrial hyperplasia and carcinoma. Methods This cross-sectional study was conducted from January 2023 to July 2023 at two teaching hospitals in Makassar Indonesia. Patients (< 70 years old) with suggestive symptoms of endometrial hyperplasia or carcinoma or being referred with disease code N.85 who underwent curettage and/or surgery for pathology assessment except those receiving radiotherapy, or chemotherapy, presence of another carcinoma, coagulation disorder, and history of anti-inflammatory drug use and unreadable samples. Demographic, and clinical symptoms were collected from medical records. Immunohistochemistry staining using mouse-monoclonal antibodies determined the β-catenin expression (percentage, intensity, and H-score) in endometrial tissues. Ordinal and Binary Logistic regression identified the potential predictors to be included in neural networks and decision tree models of histopathological grading according to the World Health Organization/WHO grading classification. Results Abdominal enlargement was associated with worse pathological grading (adjusted odds ratio/aOR 6.7 95% CI 1.8–24.8). Increasing age (aOR 1.1 95% CI 1.03–1.2) and uterus bleeding (aOR 5.3 95% CI 1.3–21.6) were associated with carcinoma but not with %β-catenin and H-Score. However, adjusted by vaginal bleeding and body mass index, lower %β-catenin (aOR 1.03 95% 1.01–1.05) was associated with non-atypical hyperplasia, as well as H-Score (aOR 1.01 95% CI 1.01–1.02). Neural networks and Decision tree risk stratification showed a sensitivity of 80-94.8% and a specificity of 40.6–60% in differentiating non-atypical from atypical and carcinoma. A cutoff of 55% β-catenin area and H-Score of 110, along with other predictors could distinguish non-atypical samples from atypical and carcinoma. Conclusion Risk stratification based on demographics, clinical symptoms, and β-catenin possesses a good performance in differentiating non-atypical hyperplasia with later stages. |
| first_indexed | 2024-03-09T05:28:53Z |
| format | Article |
| id | doaj.art-ff554e01c4044808a57d672a4bf7de02 |
| institution | Directory Open Access Journal |
| issn | 1472-6874 |
| language | English |
| last_indexed | 2024-03-09T05:28:53Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Women's Health |
| spelling | doaj.art-ff554e01c4044808a57d672a4bf7de022023-12-03T12:34:45ZengBMCBMC Women's Health1472-68742023-11-0123111110.1186/s12905-023-02790-6Risk-stratification machine learning model using demographic factors, gynaecological symptoms and β-catenin for endometrial hyperplasia and carcinoma: a cross-sectional studyRina Masadah0Aries Maulana1Berti Julian Nelwan2Mahmud Ghaznawie3Upik Anderiani Miskad4Suryani Tawali5Syahrul Rauf6Bumi Herman7Department of Pathology Anatomy, Faculty of Medicine, Hasanuddin UniversityDepartment of Pathology Anatomy, Faculty of Medicine, Hasanuddin UniversityDepartment of Pathology Anatomy, Faculty of Medicine, Hasanuddin UniversityDepartment of Pathology Anatomy, Faculty of Medicine, Hasanuddin UniversityDepartment of Pathology Anatomy, Faculty of Medicine, Hasanuddin UniversityDepartment of Family Medicine and Preventive Medicine, Faculty of Medicine, Hasanuddin UniversityDepartement of Obstetric and Gynecology, Faculty of Medicine, Hasanuddin UniversityCollege of Public Health Science, Chulalongkorn UniversityAbstract 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 endometrial hyperplasia and carcinoma. Methods This cross-sectional study was conducted from January 2023 to July 2023 at two teaching hospitals in Makassar Indonesia. Patients (< 70 years old) with suggestive symptoms of endometrial hyperplasia or carcinoma or being referred with disease code N.85 who underwent curettage and/or surgery for pathology assessment except those receiving radiotherapy, or chemotherapy, presence of another carcinoma, coagulation disorder, and history of anti-inflammatory drug use and unreadable samples. Demographic, and clinical symptoms were collected from medical records. Immunohistochemistry staining using mouse-monoclonal antibodies determined the β-catenin expression (percentage, intensity, and H-score) in endometrial tissues. Ordinal and Binary Logistic regression identified the potential predictors to be included in neural networks and decision tree models of histopathological grading according to the World Health Organization/WHO grading classification. Results Abdominal enlargement was associated with worse pathological grading (adjusted odds ratio/aOR 6.7 95% CI 1.8–24.8). Increasing age (aOR 1.1 95% CI 1.03–1.2) and uterus bleeding (aOR 5.3 95% CI 1.3–21.6) were associated with carcinoma but not with %β-catenin and H-Score. However, adjusted by vaginal bleeding and body mass index, lower %β-catenin (aOR 1.03 95% 1.01–1.05) was associated with non-atypical hyperplasia, as well as H-Score (aOR 1.01 95% CI 1.01–1.02). Neural networks and Decision tree risk stratification showed a sensitivity of 80-94.8% and a specificity of 40.6–60% in differentiating non-atypical from atypical and carcinoma. A cutoff of 55% β-catenin area and H-Score of 110, along with other predictors could distinguish non-atypical samples from atypical and carcinoma. Conclusion Risk stratification based on demographics, clinical symptoms, and β-catenin possesses a good performance in differentiating non-atypical hyperplasia with later stages.https://doi.org/10.1186/s12905-023-02790-6β-cateninEndometrial hyperplasiaEndometrial carcinomaGynecological symptomsImmunohistochemistry stainingRisk-stratification |
| spellingShingle | Rina Masadah Aries Maulana Berti Julian Nelwan Mahmud Ghaznawie Upik Anderiani Miskad Suryani Tawali Syahrul Rauf Bumi Herman Risk-stratification machine learning model using demographic factors, gynaecological symptoms and β-catenin for endometrial hyperplasia and carcinoma: a cross-sectional study BMC Women's Health β-catenin Endometrial hyperplasia Endometrial carcinoma Gynecological symptoms Immunohistochemistry staining Risk-stratification |
| title | Risk-stratification machine learning model using demographic factors, gynaecological symptoms and β-catenin for endometrial hyperplasia and carcinoma: a cross-sectional study |
| title_full | Risk-stratification machine learning model using demographic factors, gynaecological symptoms and β-catenin for endometrial hyperplasia and carcinoma: a cross-sectional study |
| title_fullStr | Risk-stratification machine learning model using demographic factors, gynaecological symptoms and β-catenin for endometrial hyperplasia and carcinoma: a cross-sectional study |
| title_full_unstemmed | Risk-stratification machine learning model using demographic factors, gynaecological symptoms and β-catenin for endometrial hyperplasia and carcinoma: a cross-sectional study |
| title_short | Risk-stratification machine learning model using demographic factors, gynaecological symptoms and β-catenin for endometrial hyperplasia and carcinoma: a cross-sectional study |
| title_sort | risk stratification machine learning model using demographic factors gynaecological symptoms and β catenin for endometrial hyperplasia and carcinoma a cross sectional study |
| topic | β-catenin Endometrial hyperplasia Endometrial carcinoma Gynecological symptoms Immunohistochemistry staining Risk-stratification |
| url | https://doi.org/10.1186/s12905-023-02790-6 |
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