基于磁共振成像的纹理分析用于评估局部晚期头颈部鳞状细胞癌新辅助化疗免疫治疗的疗效
MRI-based texture analysis for the evaluation of the response to neoadjuvant chemoimmunotherapy in locally advanced head and neck squamous cell carcinoma.
作者信息
Wei Haoran, Wang Kai, Yang Fan, Li Xiaolu, Yu Xiaoduo, Zhao Yanfeng, Li Lin, Xie Lizhi, Wang Xiaolei, Lin Meng
机构信息
Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, China.
Department of Head and Neck Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, China.
出版信息
BMC Med Imaging. 2025 Jul 15;25(1):282. doi: 10.1186/s12880-025-01806-x.
BACKGROUND
Neoadjuvant chemoimmunotherapy (NCIT) has emerged as a promising approach for patients with locally advanced head and neck squamous cell carcinoma (LA-HNSCC). However, the risk of immune-related adverse events (irAEs) should be taken seriously. And subsequent treatment strategies are determined on the basis of the neoadjuvant effect. Therefore, identifying a robust and effective method to recognize sensitive patients and monitor treatment response is highly important. The study investigated the ability of texture analysis of MRI to predict treatment response after NCIT in patients with LA-HNSCC, and compared it with several clinical indicators.
METHODS
This retrospective study included 49 LA-HNSCC patients who received NCIT followed by surgery. Texture features were extracted from MR images taken before and after NCIT. Delta features were defined as the percentage change from pre- to posttreatment features. Features that were significantly different between the pathological complete response (pCR) and non pCR groups were selected. Then the features with high diagnostic efficiency and low correlation were subsequently included in logistic regression analysis. Various diagnostic models were constructed via logistic regression, support vector machine (SVM), random forest (RF), and AdaBoost. Several clinical indicators, including tumor stage, combined positive score (CPS) derived from pretreatment lesions, and RECIST 1.1 evaluations by clinicians, were analyzed. ROC analysis and the Delong test were used to assess the performance of various models.
RESULTS
A total of 24 (49.0%) patients achieved pCR, and 25 (51.0%) did not. The Pre_model, Post_model, and Delta_model demonstrated AUCs of 0.678, 0.795, and 0.805, respectively. Compared with the T stage (AUC 0.635), CPS (AUC 0.576), and RECIST1.1 criteria (AUC 0.670) (all p < 0.005), the Combined_model showed better performance, with an AUC of 0.868, a F1-score of 0.824.
CONCLUSION
Texture analysis based on pre- and posttreatment MR images outperformed the T stage, CPS, and RECIST 1.1 criteria in predicting pathological response following NCIT in patients with LA-HNSCC.
CLINICAL TRIAL NUMBER
Not applicable.
背景
新辅助化疗免疫疗法（NCIT）已成为局部晚期头颈部鳞状细胞癌（LA-HNSCC）患者的一种有前景的治疗方法。然而，免疫相关不良事件（irAEs）的风险应予以重视。并且后续治疗策略是根据新辅助治疗效果来确定的。因此，确定一种可靠且有效的方法来识别敏感患者并监测治疗反应非常重要。本研究调查了MRI纹理分析预测LA-HNSCC患者接受NCIT后治疗反应的能力，并将其与几个临床指标进行比较。
方法
这项回顾性研究纳入了49例接受NCIT后行手术治疗的LA-HNSCC患者。从NCIT治疗前后的MR图像中提取纹理特征。增量特征定义为治疗前到治疗后特征的百分比变化。选择病理完全缓解（pCR）组和非pCR组之间有显著差异的特征。然后将诊断效率高且相关性低的特征纳入逻辑回归分析。通过逻辑回归、支持向量机（SVM）、随机森林（RF）和AdaBoost构建各种诊断模型。分析了几个临床指标，包括肿瘤分期、治疗前病变的综合阳性评分（CPS）以及临床医生的RECIST 1.1评估。采用ROC分析和德龙检验评估各种模型的性能。
结果
共有24例（49.0%）患者达到pCR，25例（51.0%）未达到。Pre_model、Post_model和Delta_model的AUC分别为0.678、0.795和0.805。与T分期（AUC 0.635）、CPS（AUC 0.576）和RECIST1.1标准（AUC 0.67）（所有p < 0.005）相比，Combined_model表现更好，AUC为0.868，F1评分为0.824。
结论
基于治疗前后MR图像的纹理分析在预测LA-HNSCC患者接受NCIT后的病理反应方面优于T分期、CPS和RECIST 1.1标准。
临床试验编号
不适用。
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