RaFiST：一种用于非小细胞肺癌肿瘤纤维化无创分层和预后预测的影像组学模型
RaFiST: a radiomics model for non-invasive stratification of tumor fibrosis and prognostic prediction in non-small cell lung cancer.
作者
作者单位
- Department of Radiology, Jinling Clinical Medical College, Nanjing Medical University, Nanjing, China.
- Department of Radiology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China.
- Department of Radiology, Jinling Hospital, School of Medical Imaging, Nanjing Medical University, Nanjing, China.
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
- Center of Medical Imaging, Chenzhou First People's Hospital, Chenzhou, China.
- Department of Pathology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.
- Department of Pathology, Qinhuai Medical Treatment Area, Eastern Theater General Hospital, Nanjing, China.
- Department of Radiology, Jinling Clinical Medical College, Nanjing Medical University, Nanjing, China. cjr.luguangming@vip.163.com.
摘要
中文
肿瘤纤维化在驱动治疗异质性和耐药中起关键作用。然而，关于非小细胞肺癌（NSCLC）的相关研究仍然有限。本研究旨在确定肿瘤纤维化的预后价值，并开发一种新的影像组学纤维化分层工具（RaFiST）用于无创患者分层。在这项纳入532例手术切除NSCLC患者的多中心回顾性研究中，通过胶原蛋白组分对肿瘤纤维化进行组织病理学定量。RaFiST基于治疗前增强CT扫描在训练和外部测试队列中开发。随后比较临床、直接影像组学和组合模型的预后性能。转录组分析研究了模型的潜在分子机制。多变量Cox回归显示，纤维化评分是两个中心无病生存期（DFS）和总生存期（OS）的独立危险因素。11.12%的最佳临界值将患者分为高纤维化组和低纤维化组。RaFiST在预测肿瘤纤维化方面表现出色，训练队列的曲线下面积（AUC）为0.879，测试队列为0.813。RaFiST-高组患者在两个队列中的生存期均显著较差（均p < 0.01）。此外，Clinical-RaFiST组合模型优于基线临床和直接影像组学模型，5年DFS AUC为0.827。转录组分析将RaFiST-高组与细胞外基质重塑、缺氧和免疫抑制通路相关联。肿瘤纤维化是NSCLC的独立预后危险因素。RaFiST为肿瘤纤维化分层和预后预测提供了稳健的无创影像生物标志物。
English
Tumor fibrosis plays a critical role in driving therapeutic heterogeneity and drug resistance. However, relevant research in non-small cell lung cancer (NSCLC) remains limited. This study aimed to determine the prognostic value of tumor fibrosis and develop a novel radiomics fibrosis stratification tool (RaFiST) for non-invasive patient stratification. In this multicenter retrospective study of 532 patients with resected NSCLC, tumor fibrosis was histopathologically quantified via collagen fraction. RaFiST was developed using pre-treatment contrast-enhanced CT scans from training and external test cohorts. Prognostic performance was subsequently compared among clinical, direct radiomics, and combined models. Transcriptomic analysis investigated the model's underlying molecular mechanisms. Multivariable Cox regression revealed that the fibrosis score was an independent risk factor for disease-free survival (DFS) and overall survival (OS) at both centers. An optimal cutoff of 11.12% stratified patients into high- and low-fibrosis groups. RaFiST demonstrated excellent performance in predicting tumor fibrosis, achieving an area under the curve (AUC) of 0.879 in the training cohort and 0.813 in the test cohort. RaFiST-High patients exhibited significantly worse survival across both cohorts (all p < 0.01). Furthermore, the combined Clinical-RaFiST model outperformed the baseline clinical and direct radiomics models, yielding a 5-year DFS AUC of 0.827. Transcriptomic analysis associated the RaFiST-High group with pathways for extracellular matrix remodeling, hypoxia, and immune suppression. Tumor fibrosis is an independent prognostic risk factor in NSCLC. RaFiST provides a robust, non-invasive imaging biomarker for tumor fibrosis stratification and prognostic prediction. Question Tumor fibrosis plays a critical role in tumor therapeutic heterogeneity and drug resistance. However, relevant research in non-small cell lung cancer (NSCLC) remains relatively limited. Findings Tumor fibrosis is an independent prognostic factor in NSCLC, and radiomics fibrosis stratification tool (RaFiST), a CT-based radiomics model provides a robust non-invasive biomarker for fibrosis stratification and outcome prediction. Clinical relevance RaFiST offers a non-invasive, reproducible framework to evaluate tumor fibrosis in NSCLC, thereby enabling the earlier identification of postoperative patients with poor prognoses for prompt adjuvant therapy.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 6.0
- 新锐分区
- 2区