ArticleInfectious agents and cancer2024
Assessing the risk of high-grade squamous intraepithelial lesions (HSIL+) in women with LSIL biopsies: a machine learning-based study.
Article in Infectious agents and cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07306247 (Multicenter Prospective Non-randomized Controlled Study of Ella Photodynamic Therapy for Cervical Low-grade Squamous Intraepithelial Lesions With HPV16/18 Infection), which is not on this map. Cited by 2 papers.
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Multicenter Prospective Non-randomized Controlled Study of Ella Photodynamic Therapy for Cervical Low-grade Squamous Intraepithelial Lesions With HPV16/18 Infection
Who cites it
2 citing papers in PubMed.
- Clinical utility of an optoelectronic imaging tracing system for diagnosis of high-grade cervical lesions.Frontiers in oncology · 2026Article
- Machine learning in early screening for high-grade cervical intraepithelial neoplasia using blood testing.BMC medical informatics and decision making · 2025Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveThis 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+.
methodsWe 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.
resultsThe 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.
conclusionKey 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.
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