肺腺癌脑转移中生长相关蛋白43的无创影像基因组学评估
Non-invasive radiogenomic assessment of growth-associated protein 43 in lung adenocarcinoma brain metastases.
作者
作者单位
- The Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing 100070, China.
- Department of Neuro-oncology Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- The Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing 100070, China; Beijing Neurosurgical Institute, Beijing, China.
- The Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing 100070, China; Beijing Neurosurgical Institute, Beijing, China. Electronic address: dzhangttyy@mail.ccmu.edu.cn.
摘要
中文
旨在评估生长相关蛋白43（GAP43）在肺腺癌（LUAD）脑转移中的预后作用，并开发结合MRI影像组学与转录组学的无创影像基因组学模型，用于GAP43预测和生存估计。这项回顾性研究纳入2012-2021年间308例手术切除的LUAD脑转移患者。其中56例有配对的RNA测序和对比增强T1加权MRI（CE-T1WI），用于构建预测GAP43表达的影像组学模型。另外纳入252例患者的独立队列，用于影像组学评分（RS）的探索性预后评估。使用PyRadiomics提取影像组学特征（n=1,130），并经过顺序特征选择，包括使用组内相关系数（ICC）的观察者间可重复性评估、单变量t检验、基于相关性的冗余过滤和最小绝对收缩和选择算子（LASSO）特征选择。随后开发了优化的支持向量机（SVM）模型来预测GAP43表达并生成RS。通过Kaplan-Meier和Cox回归分析生存情况。共有56名患者纳入影像基因组学队列。GAP43与较差的总生存期（OS）显著独立相关（P=0.026）。高GAP43表达与活化肥大细胞（R=0.53）、嗜酸性粒细胞（R=0.34）呈正相关。影像基因组学模型在重复嵌套交叉验证中预测GAP43表达表现出良好性能，AUC为0.825（95% CI，0.791-0.858）。在252例独立预后队列中，较高的影像组学评分（RS）与较差的OS独立相关（P<0.001）。基于RS的列线图显示出可接受的预后性能，3、6、12个月OS的随时间变化AUC分别为0.661、0.666和0.631。GAP43是LUAD脑转移的不良预后生物标志物。CE-T1WI影像基因组学模型可准确预测GAP43表达，有助于个体化短期生存分层。
English
To evaluate the prognostic role of growth-associated protein 43 (GAP43) in lung adenocarcinoma (LUAD) brain metastases and develop a non-invasive radiogenomic model combining MRI radiomics with transcriptomics for GAP43 prediction and survival estimation. This retrospective study included 308 patients with surgically resected LUAD brain metastases (2012-2021). Fifty-six patients with paired RNA sequencing and contrast-enhanced T1-weighted MRI (CE-T1WI) were used to build the radiomics model predicting GAP43 expression. An additional independent cohort of 252 patients was included for exploratory prognostic evaluation of the imaging-derived radiomics score (RS). Radiomic features (n = 1,130) were extracted using PyRadiomics and underwent sequential feature selection, including inter-observer reproducibility assessment using intraclass correlation coefficients (ICCs), univariate t-tests, correlation-based redundancy filtering, and least absolute shrinkage and selection operator (LASSO)-based feature selection. An optimized support vector machine (SVM) model was subsequently developed to predict GAP43 expression and generate the RS. Survival was analyzed via Kaplan-Meier and Cox regression. A total of 56 patients were enrolled in the radiogenomics cohort. GAP43 was significantly independently associated with poorer overall survival (OS; P = 0.026). High GAP43 expression correlated positively with activated mast cells (R = 0.53), eosinophils (R = 0.34). The radiogenomic model showed favorable performance for predicting GAP43 expression in repeated nested cross-validation, with an AUC of 0.825 (95% CI, 0.791-0.858). In an independent prognostic cohort of 252 patients, a higher imaging-derived radiomics score (RS) was independently associated with poorer OS (P < 0.001). The RS-based nomogram showed acceptable prognostic performance, with time-dependent AUCs of 0.661, 0.666, and 0.631 for 3-, 6-, and 12-month OS, respectively. GAP43 is an adverse prognostic biomarker in LUAD brain metastases. A CE-T1WI radiogenomic model accurately predicts GAP43 expression, facilitating individualized short-term survival stratification.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 3.9
- 新锐分区
- 2区