Data_Sheet_2_CT-Based Radiomics Signature for the Preoperative Discrimination Between Head and Neck Squamous Cell Carcinoma Grades.xlsx

Background: Radiomics has been widely used to non-invasively mine quantitative information from medical images and could potentially predict tumor phenotypes. Pathologic grade is considered a predictive prognostic factor for head and neck squamous cell carcinoma (HNSCC) patients. A preoperative histological assessment can be important in the clinical management of patients. We applied radiomics analysis to devise non-invasive biomarkers and accurately differentiate between well-differentiated (WD) and moderately differentiated (MD) and poorly differentiated (PD) HNSCC.Methods: This study involved 206 consecutive HNSCC patients (training cohort: n = 137; testing cohort: n = 69). In total, we extracted 670 radiomics features from contrast-enhanced computed tomography (CT) images. Radiomics signatures were constructed with a kernel principal component analysis (KPCA), random forest classifier and a variance-threshold (VT) selection. The associations between the radiomics signatures and HNSCC histological grades were investigated. A clinical model and combined model were also constructed. Areas under the receiver operating characteristic curves (AUCs) were applied to evaluate the performances of the three models.Results: In total, 670 features were selected by the KPCA and random forest methods from the CT images. The radiomics signatures had a good performance in discriminating between the two cohorts of HNSCC grades, with an AUC of 0.96 and an accuracy of 0.92. The specificity, accuracy, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of the abovementioned method with a VT selection for determining HNSCC grades were 0.83, 0.92, 0.96, 0.94, and 0.91, respectively; without VT, the corresponding results were 0.70, 0.83, 0.88, 0.80, and 0.84. The differences in accuracy, sensitivity and NPV were significant between these approaches (p < 0.05). The AUCs with VT and without VT were 0.96 and 0.89, respectively (p < 0.05). Compared to the combined model and the radiomics signatures, The clinical model had a worse performance, and the differences were significant (p < 0.05). The combined model had the best performance, but the difference between the combined model and the radiomics signature weren't significant (p > 0.05).Conclusions: The CT-based radiomics signature could discriminate between WD and MD and PD HNSCC and might serve as a biomarker for preoperative grading.

背景：影像组学已被广泛应用于从医学图像中非侵入性地提取定量信息，并有可能预测肿瘤表型。病理分级被视为头颈部鳞状细胞癌（HNSCC）患者的预后预测因子。术前组织学评估对于患者的临床管理具有重要意义。本研究应用影像组学分析，旨在制定非侵入性生物标志物，以准确区分分化良好（WD）、中度分化（MD）和分化不良（PD）的HNSCC。方法：本研究纳入了206例连续的HNSCC患者（训练队列：n = 137；测试队列：n = 69）。共从增强CT图像中提取了670个影像组学特征。利用核主成分分析（KPCA）、随机森林分类器和方差阈值（VT）选择构建了影像组学特征集。研究探讨了影像组学特征与HNSCC组织学分级之间的关联，并构建了临床模型和综合模型。使用受试者工作特征曲线下的面积（AUCs）评估了三种模型的性能。结果：共从CT图像中通过KPCA和随机森林方法选择了670个特征。影像组学特征在区分HNSCC两个分级队列方面表现出良好的性能，AUC为0.96，准确率为0.92。使用VT选择确定HNSCC分级的方法，其特异性、准确率、灵敏度、阳性预测值（PPV）和阴性预测值（NPV）分别为0.83、0.92、0.96、0.94和0.91；未使用VT时，相应的结果为0.70、0.83、0.88、0.80和0.84。不同方法之间的准确率、灵敏度和NPV的差异具有统计学意义（p < 0.05）。使用VT和不使用VT的AUC分别为0.96和0.89（p < 0.05）。与综合模型和影像组学特征相比，临床模型的表现较差，差异具有统计学意义（p < 0.05）。综合模型表现出最佳性能，但与影像组学特征之间的差异并不显著（p > 0.05）。结论：基于CT的影像组学特征能够区分WD、MD和PD HNSCC，可能作为术前分级的生物标志物。