目的:新辅助化疗(NACT)已成为宫颈鳞癌(CSCC)综合治疗的重要组成部分。然而,由于对化疗药物的敏感性和耐受性存在个体差异,因此并非所有患者都对化疗有反应.因此,准确预测CSCC患者对NACT的敏感性对于个体化化疗至关重要。本研究旨在构建基于磁共振成像(MRI)的机器学习影像组学模型,以评估其在CSCC患者中预测NACT易感性的功效。
方法:本研究纳入了来自两家医院的234例CSCC患者,他们被分成一个训练集(n=180),测试集(n=20),和外部验证集(n=34)。从横切面MRI图像中提取手动影像特征,并使用递归特征消除(RFE)方法进行特征选择。然后使用三种机器学习算法生成预测模型,即逻辑回归,随机森林,和支持向量机(SVM),用于预测NACT易感性。模型的性能是根据接收器工作特征曲线(AUC)下面积评估的,准确度,和敏感性。
结果:SVM方法在测试集和外部验证集上都获得了最高的分数。在测试集和外部验证集中,模型的AUC分别为0.88和0.764,准确度分别为0.90和0.853,灵敏度分别为0.93和0.962。
结论:基于MRI图像的机器学习影像组学模型在预测CSCC患者NACT敏感性方面取得了令人满意的性能,具有较高的准确性和鲁棒性,这对CSCC患者的治疗和个体化用药具有重要意义。
Neoadjuvant chemotherapy (NACT) has become an essential component of the comprehensive treatment of cervical squamous cell carcinoma (CSCC). However, not all patients respond to chemotherapy due to individual differences in sensitivity and tolerance to chemotherapy drugs. Therefore, accurately predicting the sensitivity of CSCC patients to NACT was vital for individual chemotherapy. This
study aims to construct a machine learning radiomics model based on magnetic resonance imaging (MRI) to assess its efficacy in predicting NACT susceptibility among CSCC patients.
This
study included 234 patients with CSCC from two hospitals, who were divided into a training set (n = 180), a testing set (n = 20), and an external validation set (n = 34). Manual radiomic features were extracted from transverse section MRI images, and feature selection was performed using the recursive feature elimination (RFE) method. A prediction model was then generated using three machine learning algorithms, namely logistic regression, random forest, and support vector machines (
SVM), for predicting NACT susceptibility. The model\'s performance was assessed based on the area under the receiver operating characteristic curve (AUC), accuracy, and sensitivity.
The
SVM approach achieves the highest scores on both the testing set and the external validation set. In the testing set and external validation set, the AUC of the model was 0.88 and 0.764, and the accuracy was 0.90 and 0.853, the sensitivity was 0.93 and 0.962, respectively.
Machine learning radiomics models based on MRI images have achieved satisfactory performance in predicting the sensitivity of NACT in CSCC patients with high accuracy and robustness, which has great significance for the treatment and personalized medicine of CSCC patients.