Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, Shanghai, 200010, China.
BMC Med Imaging. 2024 Oct 7;24(1):266. doi: 10.1186/s12880-024-01444-9.
To investigate whether radiomics models derived from pretreatment CT could help to predict response to immunotherapy in oral squamous cell carcinoma (OSCC).
Retrospectively, a total of 40 patients with measurable OSCC were included. The patients were divided into responder group and non-responder group according to the comparison of pre-treatment and post-treatment CT findings. Radiomics features were extracted from pre-treatment CT images, and optimal features were selected by univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis. Neural network, support vector machine, random forest and logistic regression models were used to predict response to immunotherapy in OSCC, and leave-one-out cross validation was employed to assess the performance of the classifiers. The area under the curve (AUC), accuracy, sensitivity and specificity were calculated to quantify the predictive efficacy.
A total of 7 features were selected to build models upon machine learning methods. By comparing different machine learning based models, the neural network model achieved the best predictive ability, with an AUC of 0.864, an accuracy of 82.5%, a sensitivity of 82.5%, and a specificity of 82.5%.
The pretreatment CT-based radiomics model showed good performance in predicting response to immunotherapy in OSCC. Pretreatment CT-based radiomics model might provide an alternative approach for the selection of patients who benefit from immunotherapy.
为了探究基于治疗前 CT 的影像组学模型是否有助于预测口腔鳞状细胞癌（OSCC）对免疫治疗的反应。
回顾性纳入 40 例可测量的 OSCC 患者。根据治疗前和治疗后 CT 检查结果的比较，将患者分为应答组和无应答组。从治疗前 CT 图像中提取影像组学特征，通过单因素分析和最小绝对值收缩和选择算子（LASSO）回归分析选择最优特征。采用神经网络、支持向量机、随机森林和逻辑回归模型预测 OSCC 对免疫治疗的反应，采用留一法交叉验证评估分类器的性能。计算曲线下面积（AUC）、准确性、敏感度和特异度来量化预测效果。
通过机器学习方法共选择了 7 个特征来构建模型。通过比较不同的基于机器学习的模型，神经网络模型具有最佳的预测能力，AUC 为 0.864，准确性为 82.5%，敏感度为 82.5%，特异度为 82.5%。
基于治疗前 CT 的影像组学模型在预测 OSCC 对免疫治疗的反应方面表现出良好的性能。基于治疗前 CT 的影像组学模型可能为选择受益于免疫治疗的患者提供一种替代方法。
