Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, China.
Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Ministry of Education, Taiyuan, 030001, China.
J Transl Med. 2024 Jul 26;22(1):686. doi: 10.1186/s12967-024-05417-y.
During the prolonged period from Human Papillomavirus (HPV) infection to cervical cancer development, Low-Grade Squamous Intraepithelial Lesion (LSIL) stage provides a critical opportunity for cervical cancer prevention, giving the high potential for reversal in this stage. However, there is few research and a lack of clear guidelines on appropriate intervention strategies at this stage, underscoring the need for real-time prognostic predictions and personalized treatments to promote lesion reversal.
We have established a prospective cohort. Since 2018, we have been collecting clinical data and pathological images of HPV-infected patients, followed by tracking the progression of their cervical lesions. In constructing our predictive models, we applied logistic regression and six machine learning models, evaluating each model's predictive performance using metrics such as the Area Under the Curve (AUC). We also employed the SHAP method for interpretative analysis of the prediction results. Additionally, the model identifies key factors influencing the progression of the lesions.
Model comparisons highlighted the superior performance of Random Forests (RF) and Support Vector Machines (SVM), both in clinical parameter and pathological image-based predictions. Notably, the RF model, which integrates pathological images and clinical multi-parameters, achieved the highest AUC of 0.866. Another significant finding was the substantial impact of sleep quality on the spontaneous clearance of HPV and regression of LSIL.
In contrast to current cervical cancer prediction models, our model's prognostic capabilities extend to the spontaneous regression stage of cervical cancer. This model aids clinicians in real-time monitoring of lesions and in developing personalized treatment or follow-up plans by assessing individual risk factors, thus fostering lesion spontaneous reversal and aiding in cervical cancer prevention and reduction.
从人乳头瘤病毒（HPV）感染到宫颈癌发展的漫长过程中，低级别鳞状上皮内病变（LSIL）阶段为宫颈癌预防提供了一个关键机会，在这个阶段病变具有高度逆转的潜力。然而，对于这个阶段的适当干预策略，研究较少，也缺乏明确的指南，这凸显了实时预后预测和个性化治疗的必要性，以促进病变逆转。
我们建立了一个前瞻性队列。自 2018 年以来，我们一直在收集 HPV 感染患者的临床数据和病理图像，并对其宫颈病变的进展进行跟踪。在构建预测模型时，我们应用了逻辑回归和六种机器学习模型，使用曲线下面积（AUC）等指标来评估每个模型的预测性能。我们还采用了 SHAP 方法对预测结果进行解释性分析。此外，该模型确定了影响病变进展的关键因素。
模型比较突出了随机森林（RF）和支持向量机（SVM）的性能优势，无论是在临床参数还是病理图像预测方面。值得注意的是，RF 模型整合了病理图像和临床多参数，达到了 0.866 的最高 AUC。另一个重要发现是睡眠质量对 HPV 自然清除和 LSIL 消退的影响很大。
与当前的宫颈癌预测模型相比，我们的模型的预后能力扩展到了宫颈癌的自发消退阶段。该模型通过评估个体风险因素，帮助临床医生实时监测病变，并制定个性化的治疗或随访计划，从而促进病变的自发逆转，有助于宫颈癌的预防和减少。
