Guo Junjie, Feng Jiajun, Huang Yuqian, Li Xianqing, Hu Zhenbin, Zhou Quan, Xu Honggang
Department of Medical Imaging, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510030, Guangdong, China.
Department of Medical Imaging Center, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, 510600, Guangdong, China.
Heliyon. 2024 Aug 22;10(17):e36601. doi: 10.1016/j.heliyon.2024.e36601. eCollection 2024 Sep 15.
Preoperative differentiation of malignant tumors (MT), pleomorphic adenomas (PA), and other benign tumors of the parotid gland is critical to clinical strategy, this study aimed to develop and validate a T2-weighted image (T2WI) based radiomics model through machine learning approaches for the triple classification of parotid gland tumors.
We retrospectively enrolled 147 patients from January 2010 to July 2022. T2WIs were used to extract radiomics features. Max-Relevance and Min-Redundancy (mRMR) and Extreme Gradient Boosting (XGBoost) algorithms were used to select features. Using a 5-fold cross-validation strategy, radiomics models were constructed using a Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (KNN) for the triple classification of parotid tumors. The three models were evaluated and compared using the receiver operator characteristic (ROC) curve, sensitivity, specificity, and accuracy.
A total of 1057 radiomics features were extracted, and 8 features were selected to developed the radiomics model, including First-order Median, First-order Skewness, First-order Minimum, Original_shape_Flatness, Glcm Inverse Variance, Glcm Inverse Variance, Glszm Low Gray Level Zone Emphasis, and Glszm Small Area Low Gray Level Emphasis. The mean area under the curves (AUCs) for the radiomics models in training and validation sets through LR, SVM and KNN were 0.85 and 0.80, 0.85 and 0.80 and 0.83 and 0.79, respectively.
The T2WI-based radiomics models through LR, SVM and KNN demonstrated good performance in the triple classification of parotid tumors.
腮腺恶性肿瘤（MT）、多形性腺瘤（PA）及其他良性肿瘤的术前鉴别对临床策略至关重要，本研究旨在通过机器学习方法开发并验证基于T2加权图像（T2WI）的放射组学模型，用于腮腺肿瘤的三联分类。
我们回顾性纳入了2010年1月至2022年7月的147例患者。使用T2WI提取放射组学特征。采用最大相关最小冗余（mRMR）和极端梯度提升（XGBoost）算法进行特征选择。采用5折交叉验证策略，使用支持向量机（SVM）、逻辑回归（LR）和k近邻（KNN）构建放射组学模型，用于腮腺肿瘤的三联分类。使用受试者工作特征（ROC）曲线、敏感性、特异性和准确性对这三种模型进行评估和比较。
共提取了1057个放射组学特征，选择了8个特征来构建放射组学模型，包括一阶中位数、一阶偏度、一阶最小值、原始形状扁平度、灰度共生矩阵逆方差、灰度共生矩阵逆方差、灰度大小区域矩阵低灰度级区域强调和灰度大小区域矩阵小面积低灰度级强调。通过LR、SVM和KNN构建的放射组学模型在训练集和验证集上的曲线下平均面积（AUC）分别为0.85和0.80、0.85和0.80以及0.83和0.79。
基于T2WI的放射组学模型通过LR、SVM和KNN在腮腺肿瘤的三联分类中表现出良好的性能。
