Tian Ruxian, Li Yumei, Jia Chuanliang, Mou Yakui, Zhang Haicheng, Wu Xinxin, Li Jingjing, Yu Guohua, Mao Ning, Song Xicheng
Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China.
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
Front Oncol. 2022 Apr 28;12:823428. doi: 10.3389/fonc.2022.823428. eCollection 2022.
We aim to establish and validate computed tomography (CT)-based radiomics model for predicting TP53 status in patients with laryngeal squamous cell carcinoma (LSCC).
We divided all patients into a training set 1 (n=66) and a testing set 1 (n=30) to establish and validate radiomics model to predict TP53. Radiomics features were selected by analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (Lasso) regression analysis. Five radiomics models were established by using K-Nearest Neighbor, logistics regressive, linear-support vector machine (SVM), gaussian-SVM, and polynomial-SVM in training set 1. We also divided all patients into a training set 2 and a testing set 2 according to different CT equipment to establish and evaluate the stability of the radiomics models.
After ANOVA and subsequent Lasso regression analysis, 22 radiomics features were selected to build the radiomics model in training set 1. The radiomics model based on linear-SVM has the best predictive performance of the five models, and the area under the receiver operating characteristic curve in training set 1 and testing set 1 were 0.831(95% confidence interval [CI] 0.692-0.970) and 0.797(95% CI 0.632-0.957) respectively. The specificity, sensitivity, and accuracy were 0.971(95% CI 0.834-0.999), 0.714(95% CI 0.535-0.848), and 0.843(95% CI 0.657-0.928) in training set 1 and 0.750(95% CI 0.500-0.938), 0.786(95% CI 0.571-1.000), and 0.667(95% CI 0.467-0.720) in testing set 1, respectively. In addition, the radiomics model also achieved stable prediction results even in different CT equipment. Decision curve analysis showed that the radiomics model for predicting TP53 status could benefit LSCC patients.
We developed and validated a relatively optimal radiomics model for TP53 status prediction by trying five different machine learning methods in patients with LSCC. It shown great potential of radiomics features for predicting TP53 status preoperatively and guiding clinical treatment.
我们旨在建立并验证基于计算机断层扫描（CT）的放射组学模型，以预测喉鳞状细胞癌（LSCC）患者的TP53状态。
我们将所有患者分为训练集1（n = 66）和测试集1（n = 30），以建立并验证用于预测TP53的放射组学模型。通过方差分析（ANOVA）和最小绝对收缩和选择算子（Lasso）回归分析选择放射组学特征。在训练集1中使用K近邻、逻辑回归、线性支持向量机（SVM）、高斯SVM和多项式SVM建立了五个放射组学模型。我们还根据不同的CT设备将所有患者分为训练集2和测试集2，以建立并评估放射组学模型的稳定性。
经过方差分析和随后的Lasso回归分析，在训练集1中选择了22个放射组学特征来构建放射组学模型。基于线性SVM的放射组学模型在五个模型中具有最佳的预测性能，训练集1和测试集1中受试者操作特征曲线下面积分别为0.831（95%置信区间[CI]0.692 - 0.970）和0.797（95%CI 0.632 - 0.957）。训练集1中的特异性、敏感性和准确性分别为0.971（95%CI 0.834 - 0.999）、0.714（95%CI 0.535 - 0.848）和0.843（95%CI 0.657 - 0.928），测试集1中的分别为0.750（95%CI 0.500 - 0.938）、0.786（95%CI 0.571 - 1.000）和0.667（95%CI 0.467 - 0.720）。此外，即使在不同的CT设备中，放射组学模型也取得了稳定的预测结果。决策曲线分析表明，预测TP53状态的放射组学模型对LSCC患者有益。
我们通过在LSCC患者中尝试五种不同的机器学习方法，开发并验证了一种相对优化的用于TP53状态预测的放射组学模型。它显示出放射组学特征在术前预测TP53状态和指导临床治疗方面的巨大潜力。
