Suppr超能文献

与肿瘤微环境相关的放射组学模型预测局部晚期鼻咽癌患者的免疫治疗反应和预后

Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

作者信息

Sun Jie, Wu Xuewei, Zhang Xiao, Huang Weiyuan, Zhong Xi, Li Xueyan, Xue Kaiming, Liu Shuyi, Chen Xianjie, Li Wenzhu, Liu Xin, Shen Hui, You Jingjing, He Wenle, Jin Zhe, Yu Lijuan, Li Yuange, Zhang Shuixing, Zhang Bin

机构信息

Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.

Medical AI Lab, The First Hospital of Hebei Medical University, Hebei Medical University, Shijiazhuang, Hebei, China.

出版信息

Research (Wash D C). 2025 Jun 24;8:0749. doi: 10.34133/research.0749. eCollection 2025.

Abstract

No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. This study included 246 LANPC patients (training cohort, = 117; external test cohort, = 129) from 10 centers. The best-performing machine learning classifier was employed to create the radiomic models. A combined model was constructed by integrating clinical and radiomic data. A radiomic interpretability study was performed with whole slide images (WSIs) stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC). A total of 150 patient-level nuclear morphological features (NMFs) and 12 cell spatial distribution features (CSDFs) were extracted from WSIs. The correlation between the radiomic and pathological features was assessed using Spearman correlation analysis. The radiomic model outperformed the clinical and combined models in predicting treatment response (area under the curve: 0.760 vs. 0.559 vs. 0.652). For overall survival estimation, the combined model performed comparably to the radiomic model but outperformed the clinical model (concordance index: 0.858 vs. 0.812 vs. 0.664). Six treatment response-related radiomic features correlated with 50 H&E-derived (146 pairs, ||= 0.31 to 0.46) and 2 to 26 IHC-derived NMF, particularly for CD45RO (69 pairs, ||= 0.31 to 0.48), CD8 (84, ||= 0.30 to 0.59), PD-L1 (73, ||= 0.32 to 0.48), and CD163 (53, || = 0.32 to 0.59). Eight prognostic radiomic features correlated with 11 H&E-derived (16 pairs, ||= 0.48 to 0.61) and 2 to 31 IHC-derived NMF, particularly for PD-L1 (80 pairs, ||= 0.44 to 0.64), CD45RO (65, ||= 0.42 to 0.67), CD19 (35, ||= 0.44 to 0.58), CD66b (61, || = 0.42 to 0.67), and FOXP3 (21, || = 0.41 to 0.71). In contrast, fewer CSDFs exhibited correlations with specific radiomic features. The radiomic model and combined model are feasible in predicting immunotherapy response and outcomes in LANPC patients. The radiology-pathology correlation suggests a potential biological basis for the predictive models.

摘要

尚未发现可靠的生物标志物来预测程序性细胞死亡蛋白1(PD-1)抑制剂对局部区域晚期鼻咽癌(LANPC)患者的疗效。我们旨在利用免疫治疗前的磁共振成像(MRI)开发放射组学模型,以预测对PD-1抑制剂的反应及患者预后。本研究纳入了来自10个中心的246例LANPC患者(训练队列,n = 117;外部测试队列,n = 129)。采用表现最佳的机器学习分类器创建放射组学模型。通过整合临床和放射组学数据构建联合模型。利用苏木精和伊红(H&E)染色及免疫组化(IHC)的全玻片图像(WSIs)进行放射组学可解释性研究。从WSIs中提取了总共150个患者水平的核形态特征(NMFs)和12个细胞空间分布特征(CSDFs)。使用Spearman相关分析评估放射组学特征与病理特征之间的相关性。放射组学模型在预测治疗反应方面优于临床模型和联合模型(曲线下面积:0.760对0.559对0.652)。对于总生存估计,联合模型与放射组学模型表现相当,但优于临床模型(一致性指数:0.858对0.812对0.664)。六个与治疗反应相关的放射组学特征与50个H&E衍生的(146对,|r| = 0.31至0.46)和2至26个IHC衍生的NMF相关,特别是对于CD45RO(69对,|r| = 0.31至0.48)、CD8(84对,|r| = 0.30至0.59)、PD-L1(73对,|r| = 0.32至0.48)和CD163(53对,|r| = 0.32至0.59)。八个预后放射组学特征与11个H&E衍生的(16对,|r| = 0.48至0.61)和2至31个IHC衍生的NMF相关,特别是对于PD-L1(80对,|r| = 0.44至0.64)、CD45RO(65对,|r| = 0.42至0.67)、CD19(35对,|r| = 0.44至0.58)、CD66b(61对,|r| = 0.42至0.67)和FOXP3(21对,|r| = 0.41至0.71)。相比之下,较少的CSDFs与特定的放射组学特征表现出相关性。放射组学模型和联合模型在预测LANPC患者的免疫治疗反应和结局方面是可行的。放射学-病理学相关性为预测模型提供了潜在的生物学基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0e7/12187091/ea4b103843a6/research.0749.fig.001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验