Assessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based study
Abstract Objective This study aims to analyze factors associated with the missed diagnosis of high-grade squamous intraepithelial lesions (HSIL+) in patients initially diagnosed with low-grade squamous intraepithelial lesions (LSIL) through colposcopic biopsy and to develop a predictive model for as...
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BMC
2024-12-01
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| Series: | Infectious Agents and Cancer |
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| Online Access: | https://doi.org/10.1186/s13027-024-00625-z |
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| author | Dongmei Li Zhichao Wang Yan Liu Meiyuan Zhou Bo Xia Lin Zhang Keming Chen Yong Zeng |
| author_facet | Dongmei Li Zhichao Wang Yan Liu Meiyuan Zhou Bo Xia Lin Zhang Keming Chen Yong Zeng |
| author_sort | Dongmei Li |
| collection | DOAJ |
| description | Abstract Objective This study aims to analyze factors associated with the missed diagnosis of high-grade squamous intraepithelial lesions (HSIL+) in patients initially diagnosed with low-grade squamous intraepithelial lesions (LSIL) through colposcopic biopsy and to develop a predictive model for assessing the risk of missed HSIL+. Methods We conducted a retrospective analysis of 505 patients who underwent loop electrical excision procedure (LEEP) following an LSIL diagnosis by colposcopic biopsy. Logistic regression was used to identify demographic and pathological parameters associated with missed diagnoses of HSIL+. Additionally, several machine learning methods were employed to construct and assess the performance of the risk prediction models. Results The overall rate of missed diagnoses for HSIL+ was 15.2%. Independent risk factors identified were HPV16/18 infection (OR 2.071; 95% CI 1.039–4.127; p = 0.039), TCT ≥ ASC-H (OR 4.147; 95% CI 1.392–12.355; p = 0.011), TZ3 (OR 1.966; 95% CI 1.003–3.853; p = 0.049) and Colposcopic impression G2 (OR 3.627; 95% CI 1.350–9.743; p = 0.011). Among the models tested, the Decision Tree algorithm demonstrated superior performance with an accuracy of 94.7%, sensitivity of 80.0%, specificity of 96.9%, and an area under the curve (AUC) of 0.936 in the validation set. Conclusion Key independent risk factors for the missed diagnosis of HSIL in patients with LSIL include HPV16/18 infection, TCT ≥ ASC-H, TZ3, and colposcopic impression G2. The Decision Tree model offers a cost-effective, reliable, and clinically valuable tool for accurately predicting the risk of missed diagnosis of HSIL+, facilitating early intervention and management. |
| format | Article |
| id | doaj-art-0a56fa12307144bd8b35f2d069610f7d |
| institution | Kabale University |
| issn | 1750-9378 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Infectious Agents and Cancer |
| spelling | doaj-art-0a56fa12307144bd8b35f2d069610f7d2024-12-08T12:19:10ZengBMCInfectious Agents and Cancer1750-93782024-12-0119111210.1186/s13027-024-00625-zAssessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based studyDongmei Li0Zhichao Wang1Yan Liu2Meiyuan Zhou3Bo Xia4Lin Zhang5Keming Chen6Yong Zeng7Department of Obstetrics and Gynecology, The First Affiliated Hospital of Yangtze UniversityOncology Department, The First Affiliated Hospital of Yangtze UniversityNeurology Intensive Care Unit, The First Affiliated Hospital of Yangtze UniversityPathology Department, The First Affiliated Hospital of Yangtze UniversityPathology Department, The First Affiliated Hospital of Yangtze UniversityDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Yangtze UniversityDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Yangtze UniversityDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Yangtze UniversityAbstract Objective This study aims to analyze factors associated with the missed diagnosis of high-grade squamous intraepithelial lesions (HSIL+) in patients initially diagnosed with low-grade squamous intraepithelial lesions (LSIL) through colposcopic biopsy and to develop a predictive model for assessing the risk of missed HSIL+. Methods We conducted a retrospective analysis of 505 patients who underwent loop electrical excision procedure (LEEP) following an LSIL diagnosis by colposcopic biopsy. Logistic regression was used to identify demographic and pathological parameters associated with missed diagnoses of HSIL+. Additionally, several machine learning methods were employed to construct and assess the performance of the risk prediction models. Results The overall rate of missed diagnoses for HSIL+ was 15.2%. Independent risk factors identified were HPV16/18 infection (OR 2.071; 95% CI 1.039–4.127; p = 0.039), TCT ≥ ASC-H (OR 4.147; 95% CI 1.392–12.355; p = 0.011), TZ3 (OR 1.966; 95% CI 1.003–3.853; p = 0.049) and Colposcopic impression G2 (OR 3.627; 95% CI 1.350–9.743; p = 0.011). Among the models tested, the Decision Tree algorithm demonstrated superior performance with an accuracy of 94.7%, sensitivity of 80.0%, specificity of 96.9%, and an area under the curve (AUC) of 0.936 in the validation set. Conclusion Key independent risk factors for the missed diagnosis of HSIL in patients with LSIL include HPV16/18 infection, TCT ≥ ASC-H, TZ3, and colposcopic impression G2. The Decision Tree model offers a cost-effective, reliable, and clinically valuable tool for accurately predicting the risk of missed diagnosis of HSIL+, facilitating early intervention and management.https://doi.org/10.1186/s13027-024-00625-zLSILHSILMissed diagnosisPredictive modelMachine learningColposcopic biopsy |
| spellingShingle | Dongmei Li Zhichao Wang Yan Liu Meiyuan Zhou Bo Xia Lin Zhang Keming Chen Yong Zeng Assessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based study Infectious Agents and Cancer LSIL HSIL Missed diagnosis Predictive model Machine learning Colposcopic biopsy |
| title | Assessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based study |
| title_full | Assessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based study |
| title_fullStr | Assessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based study |
| title_full_unstemmed | Assessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based study |
| title_short | Assessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based study |
| title_sort | assessing the risk of high grade squamous intraepithelial lesions hsil in women with lsil biopsies a machine learning based study |
| topic | LSIL HSIL Missed diagnosis Predictive model Machine learning Colposcopic biopsy |
| url | https://doi.org/10.1186/s13027-024-00625-z |
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