Purpose: The authors aimed to develop and validate deep-learning-based radiogenomic (DLR) models and radiomic signatures to predict the EGFR mutation in patients with NSCLC, and to assess the semantic and clinical features that can contribute to detecting EGFR mutations. Methods: Using 990 patients from two NSCLC trials, we employed an end-to-end pipeline analyzing CT images without precise segmentation. Two 3D convolutional neural networks segmented lung masses and nodules. Results: The combined radiomics and DLR model achieved an AUC of 0.88 ± 0.03 in predicting EGFR mutation status, outperforming individual models. Semantic features further improved the model’s accuracy, with an AUC of 0.88 ± 0.05. CT semantic features that were found to be significantly associated with EGFR mutations were pure solid tumours with no associated ground glass component (p< 0.03), the absence of peripheral emphysema (p< 0.03), the presence of pleural retraction (p= 0.004), the presence of fissure attachment (p= 0.001), the presence of metastatic nodules in both the tumour-containing lobe (p= 0.001) and the non-tumour-containing lobe (p= 0.001), the presence of ipsilateral pleural effusion (p= 0.04), and average enhancement of the tumour mass above 54 HU (p< 0.001). Conclusions: This AI-based radiomics and DLR model demonstrated high accuracy in predicting EGFR mutation, serving as a non-invasive and user-friendly imaging biomarker for EGFR mutation status prediction.
目的：本研究旨在开发并验证基于深度学习的放射基因组学模型及放射组学特征，用于预测非小细胞肺癌患者的EGFR突变状态，并评估有助于检测EGFR突变的影像语义特征与临床特征。方法：基于两项非小细胞肺癌临床试验中990例患者的CT影像数据，我们采用无需精确分割的端到端分析流程，通过两个三维卷积神经网络对肺部肿块与结节进行自动分割。结果：融合放射组学与深度学习放射基因组学的联合模型在预测EGFR突变状态时取得0.88±0.03的曲线下面积，性能优于单一模型。结合语义特征后模型准确率进一步提升，曲线下面积达0.88±0.05。与EGFR突变显著相关的CT语义特征包括：纯实性肿瘤（无磨玻璃成分，p<0.03）、无周围肺气肿（p<0.03）、存在胸膜牵拉（p=0.004）、存在叶间裂附着（p=0.001）、瘤体所在肺叶（p=0.001）与非瘤体所在肺叶（p=0.001）均存在转移性结节、存在同侧胸腔积液（p=0.04）以及肿瘤平均强化值>54HU（p<0.001）。结论：该基于人工智能的放射组学与深度学习放射基因组学模型能高精度预测EGFR突变，可作为无创、便捷的影像学生物标志物用于EGFR突变状态评估。
肿瘤(癌症)患者之家