基于 CT 的瘤内异质性评分术前预测肺腺癌淋巴血管侵犯及预后：一项多中心验证与放射基因组学解读
CT-based Intratumoral Heterogeneity Score for Preoperative Prediction of Lymphovascular Invasion and Prognosis in Lung Adenocarcinoma: A Multicenter Validation and Radiogenomic Interpretation.
作者
作者单位
- Department of Radiology, The Central Hospital of Xiangtan (The Affiliated Hospital of Hunan University), Xiangtan, 411100, Hunan, China. zuo_z@smail.xtu.edu.cn.
- Department of Radiology, West China Hospital Sichuan University Jintang Hospital, Jintang First People's Hospital, Chengdu, 610400, Sichuan, China.
- Hospital Infection Control Department, West China Hospital Sichuan University Jintang Hospital, Jintang First People's Hospital, Chengdu, 610400, Sichuan, China.
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646099, Sichuan, China.
- Liuyang Traditional Chinese Medicine Hospital, Changsha, 410300, Hunan, China.
摘要
中文
淋巴血管侵犯（LVI）是肺腺癌（LUAD）复发和生存的关键决定因素，但其术前识别仍具挑战性。本研究开发并验证了一种基于 CT 的瘤内异质性（ITH）评分，用于 LVI 预测、预后分层及探索性放射基因组学解读。回顾性纳入来自 3 家机构和 1 个公共数据集的 1322 例患者。发现队列被分为训练集（n = 590）和内部验证集（n = 253）；独立队列（n = 362）用于外部验证和预后分析；Stanford 放射基因组学数据集（n = 117）用于探索性转录组学分析。使用相同的特征矩阵和队列划分训练多种机器学习算法。最终选用随机森林作为分类器，内部验证 AUC 为 0.886（95% CI 0.842-0.923），外部验证 AUC 为 0.850（95% CI 0.806-0.892）。模型在中低阈值概率范围内显示出可接受的校准和正向净获益。高 ITH 与较差的无复发生存期（HR 12.92；95% CI 3.90-42.78；P < 0.001）和总生存期（HR 26.47；95% CI 3.52-198.76；P = 0.001）显著相关。探索性放射基因组学分析显示缺氧、血管生成、上皮-间充质转化及糖酵解相关通路的富集。基于 CT 的 ITH 评分可能有助于 LUAD 中 LVI 及预后的术前风险分层。放射基因组学发现提示潜在生物学相关性，应视为假说生成性结果。
English
Lymphovascular invasion (LVI) is a key determinant of recurrence and survival in lung adenocarcinoma (LUAD), but preoperative identification remains challenging. We developed and validated a CT-based intratumoral heterogeneity (ITH) score for LVI prediction, prognostic stratification, and exploratory radiogenomic interpretation. We retrospectively included 1322 patients from three institutions and one public dataset. The discovery cohort was divided into training (n = 590) and internal validation (n = 253) sets; an independent cohort (n = 362) was used for external validation and prognostic analysis. A Stanford Radiogenomics set (n = 117) was used for exploratory transcriptomic analysis. Multiple machine learning algorithms were trained using the same feature matrix and cohort partitions. Random forest was selected as the final classifier, with AUCs of 0.886 (95% confidence interval [CI] 0.842-0.923) in internal validation and 0.850 (95% CI 0.806-0.892) in external validation. The model showed acceptable calibration and positive net benefit across low-to-intermediate threshold probabilities. High ITH remained associated with worse recurrence-free survival (hazard ratio [HR] 12.92; 95% CI 3.90-42.78; P < 0.001) and overall survival (HR 26.47; 95% CI 3.52-198.76; P = 0.001). Exploratory radiogenomic analysis showed enrichment of hypoxia-, angiogenesis-, epithelial-mesenchymal transition-, and glycolysis-related pathways. The CT-based ITH score may support preoperative risk stratification for LVI and prognosis in LUAD. Radiogenomic findings suggest potential biological correlates and should be considered hypothesis-generating.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 3.8
- 新锐分区
- 2区