School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui, China.
Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China.
BMC Med Imaging. 2024 Feb 15;24(1):45. doi: 10.1186/s12880-024-01221-8.
Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC.
Pre-operative chest CT images of 127 patients with NSCLC were retrospectively studied. The 3D-Slicer software was used to outline the region of interest and extract features from the CT images. Radiomics prediction model was constructed by LASSO and multiple logistic regression in a training dataset. The model was validated by receiver operating characteristic (ROC) curves and calibration curves using external datasets. Decision curve analysis was used to assess the value of the model for clinical application.
A total of 1037 radiomic features were extracted from the CT images of NSCLC patients from TCGA. LASSO regression selected three radiomics features (Flatness, Autocorrelation and Minimum), which were associated with TMB level in NSCLC. A TMB prediction model consisting of 3 radiomic features was constructed by multiple logistic regression. The area under the curve (AUC) value in the TCGA training dataset was 0.816 (95% CI: 0.7109-0.9203) for predicting TMB level in NSCLC. The AUC value in external validation dataset I was 0.775 (95% CI: 0.5528-0.9972) for predicting TMB level in NSCLC, and the AUC value in external validation dataset II was 0.762 (95% CI: 0.5669-0.9569) for predicting the efficacy of immunotherapy in NSCLC.
The model based on CT radiomic features helps to achieve cost effective improvement in TMB classification and precise immunotherapy treatment of NSCLC patients.
肿瘤突变负荷（TMB）是预测非小细胞肺癌（NSCLC）免疫治疗疗效的最重要的预测生物标志物之一。放射组学允许高通量提取和分析先进的定量医学成像特征。本研究开发并验证了一种基于 CT 特征的 NSCLC 患者 TMB 水平和免疫治疗反应预测的放射组学模型。
回顾性研究了 127 例 NSCLC 患者的术前胸部 CT 图像。使用 3D-Slicer 软件对感兴趣区域进行轮廓描绘，并从 CT 图像中提取特征。通过 LASSO 和多元逻辑回归在训练数据集构建放射组学预测模型。使用外部数据集通过接受者操作特征（ROC）曲线和校准曲线对模型进行验证。决策曲线分析用于评估模型在临床应用中的价值。
从 TCGA 的 NSCLC 患者 CT 图像中提取了 1037 个放射组学特征。LASSO 回归选择了三个与 NSCLC 中的 TMB 水平相关的放射组学特征（平坦度、自相关和最小值）。由三个放射组学特征组成的 TMB 预测模型通过多元逻辑回归构建。在 TCGA 训练数据集，用于预测 NSCLC 中的 TMB 水平的曲线下面积（AUC）值为 0.816（95%CI：0.7109-0.9203）。在外部验证数据集 I 中，用于预测 NSCLC 中的 TMB 水平的 AUC 值为 0.775（95%CI：0.5528-0.9972），在外部验证数据集 II 中，用于预测 NSCLC 免疫治疗疗效的 AUC 值为 0.762（95%CI：0.5669-0.9569）。
基于 CT 放射组学特征的模型有助于实现 TMB 分类的成本效益提高，并为 NSCLC 患者的精确免疫治疗提供帮助。
