Hu Yao, Ai Jiao
Department of Obstetrics and Gynecology, Jingzhou Hospital Affiliated to Yangtze University,Jingzhou Central Hospital, Jingzhou, Hubei, People's Republic of China.
Department of Urology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou Central Hospital, Jingzhou, Hubei, People's Republic of China.
Int J Gen Med. 2024 Sep 3;17:3813-3824. doi: 10.2147/IJGM.S478842. eCollection 2024.
To develop an early warning system that enables accurate parametrial invasion (PMI) risk prediction in cervical cancer patients with early-stage.
We retrospectively collected 218 early-stage cervical cancer patients who were treated in Jingzhou Central Hospital from January 31, 2015, to January 31, 2023, and diagnosed with early stage cervical cancer by pathology. The prediction model training is achieved by randomly dividing 70% of the training queue population, with the remaining 30% used as the testing queue. Then, a prediction model based on machine learning algorithms (including random forest, generalized linear regression, decision tree, support vector machine, and artificial neural network) is constructed to predict the risk of PMI occurrence. Ultimately, the analysis of receiver operating characteristic curve (ROC) and decision curve analysis (DCA) is used to evaluate the predictive ability of various prediction models.
We finally included radiomics-based candidate variables that can be used for PMI model. Multivariate logistic regression analysis showed that energy, correlation, sum entropy (SUE), entropy, mean sum (MES), variance of differences (DIV), and inverse difference (IND) were independent risk factors for PMI occurrence. The predictive performance AUC of five types of machine learning ranges from 0.747 to 0.895 in the training set and can also reach a high accuracy of 0.905 in the testing set, indicating that the predictive model has ideal robustness.
Our ML-based model incorporating GLCM parameters can predict PMI in cervical cancer patients with stage IB1 to IIA2, particularly the RFM, which could contribute to distinguishing PMI before surgery, especially in assisting decision-making on surgical scope.
开发一种早期预警系统，能够准确预测早期宫颈癌患者的宫旁浸润（PMI）风险。
我们回顾性收集了2015年1月31日至2023年1月31日在荆州市中心医院接受治疗且经病理诊断为早期宫颈癌的218例早期宫颈癌患者。通过随机划分70%的训练队列人群来实现预测模型训练，其余30%用作测试队列。然后，构建基于机器学习算法（包括随机森林、广义线性回归、决策树、支持向量机和人工神经网络）的预测模型，以预测PMI发生风险。最终，采用受试者工作特征曲线（ROC）分析和决策曲线分析（DCA）来评估各种预测模型的预测能力。
我们最终纳入了可用于PMI模型的基于影像组学的候选变量。多因素逻辑回归分析显示，能量、相关性、总和熵（SUE）、熵、平均和（MES）、差异方差（DIV）和逆差（IND）是PMI发生的独立危险因素。五种机器学习类型在训练集中的预测性能AUC范围为0.747至0.895，在测试集中也能达到0.905的高精度，表明该预测模型具有理想的稳健性。
我们基于机器学习并纳入灰度共生矩阵（GLCM）参数的模型可以预测IB1至IIA2期宫颈癌患者的PMI，尤其是随机森林模型（RFM），这有助于在手术前区分PMI，特别是在辅助手术范围决策方面。
