评估影像组学作为抗程序性死亡蛋白1单克隆抗体治疗的复发性/转移性头颈部鳞状细胞癌患者疗效和肿瘤免疫微环境预测指标的作用。
Evaluation of radiomics as a predictor of efficacy and the tumor immune microenvironment in anti-PD-1 mAb treated recurrent/metastatic squamous cell carcinoma of the head and neck patients.
作者信息
Zandberg Dan P, Zenkin Serafettin, Ak Murat, Mamindla Priyadarshini, Peddagangireddy Vishal, Hsieh Ronan, Anderson Jennifer L, Delgoffe Greg M, Menk Ashely, Skinner Heath D, Duvvuri Umamaheswar, Ferris Robert L, Colen Rivka R
机构信息
UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
出版信息
Head Neck. 2025 Jan;47(1):129-138. doi: 10.1002/hed.27878. Epub 2024 Jul 30.
BACKGROUND
We retrospectively evaluated radiomics as a predictor of the tumor microenvironment (TME) and efficacy with anti-PD-1 mAb (IO) in R/M HNSCC.
METHODS
Radiomic feature extraction was performed on pre-treatment CT scans segmented using 3D slicer v4.10.2 and key features were selected using LASSO regularization method to build classification models with XGBoost algorithm by incorporating cross-validation techniques to calculate accuracy, sensitivity, and specificity. Outcome measures evaluated were disease control rate (DCR) by RECIST 1.1, PFS, and OS and hypoxia and CD8 T cells in the TME.
RESULTS
Radiomics features predicted DCR with accuracy, sensitivity, and specificity of 76%, 73%, and 83%, for OS 77%, 86%, 70%, PFS 82%, 75%, 89%, and in the TME, for high hypoxia 80%, 88%, and 72% and high CD8 T cells 91%, 83%, and 100%, respectively.
CONCLUSION
Radiomics accurately predicted the efficacy of IO and features of the TME in R/M HNSCC. Further study in a larger patient population is warranted.
背景
我们回顾性评估了影像组学作为复发/转移性头颈部鳞状细胞癌（R/M HNSCC）中肿瘤微环境（TME）的预测指标以及抗程序性死亡蛋白1单克隆抗体（IO）疗效预测指标的价值。
方法
使用3D Slicer v4.10.2对治疗前CT扫描进行影像组学特征提取，并使用套索（LASSO）正则化方法选择关键特征，通过结合交叉验证技术，采用XGBoost算法构建分类模型，以计算准确率、敏感性和特异性。评估的结局指标包括根据实体瘤疗效评价标准（RECIST）1.1评估的疾病控制率（DCR）、无进展生存期（PFS）和总生存期（OS），以及TME中的缺氧情况和CD8 T细胞。
结果
影像组学特征预测DCR的准确率、敏感性和特异性分别为76%、73%和83%，预测OS的分别为77%、86%和70%，预测PFS的分别为82%、75%和89%；在TME中，预测高缺氧情况的分别为80%、88%和72%，预测高CD8 T细胞的分别为91%、83%和100%。
结论
影像组学准确预测了R/M HNSCC中IO的疗效及TME的特征。有必要在更大的患者群体中进一步开展研究。
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