Department of Radiology, Affiliated Hospital of Hebei University, Baoding, Hebei 071002, China.
College of Quality and Technical Supervision, Hebei University, Baoding 071002, Hebei, China.
Curr Med Imaging. 2023;19(13):1541-1548. doi: 10.2174/1573405619666230130124408.
To differentiate the primary small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) for patients with brain metastases (BMs) based on a deep learning (DL) model using contrast-enhanced magnetic resonance imaging (MRI) T1 weighted (T1CE) images.
Out of 711 patients with BMs of lung cancer origin (SCLC 232, NSCLC 479), the MRI datasets of 192 patients (lesions' widths and heights > 30 pixels) with BMs from lung cancer (73 SCLC and 119 NSCLC) confirmed pathologically were enrolled, retrospectively. A typical convolutional neural network ResNet18 was applied for the automatic classification of BMs lesions from lung cancer based on T1CE images, with training and testing groups randomized per patient to eliminate learning bias. A 5-fold cross-validation was performed to evaluate the classification of the model. The receiver operating characteristic (ROC) curve, accuracy, precision, recall and f1 score were calculated.
For a 5-fold cross-validation test, the DL model achieved AUCs of 0.8019 and 0.8024 for SCLC and NSCLC patients with BMs, respectively, and a mean overall accuracy of 0.7515±0.04. The DL model performed well in differentiating the primary SCLC and NSCLC with BMs.
The proposed DL model is feasible and effective in differentiating the pathological subtypes of SCLC and NSCLC causing BMs, which may be used as a new tool for oncologists to diagnose noninvasively BMs and guide therapy based on the imaging structure of tumors.
基于深度学习（DL）模型，利用对比增强磁共振成像（MRI）T1 加权（T1CE）图像，对脑转移（BM）患者的小细胞肺癌（SCLC）和非小细胞肺癌（NSCLC）进行鉴别。
在 711 例肺癌脑转移患者中，回顾性纳入了 192 例肺癌脑转移患者（病灶宽度和高度>30 像素）的 MRI 数据集（232 例 SCLC，479 例 NSCLC），这些患者的 BM 经病理证实。采用典型的卷积神经网络 ResNet18 对 T1CE 图像上的肺癌 BM 病变进行自动分类，按患者随机分组进行训练和测试，以消除学习偏差。采用 5 折交叉验证评估模型的分类。计算受试者工作特征（ROC）曲线、准确率、精确率、召回率和 f1 评分。
在 5 折交叉验证测试中，DL 模型对 SCLC 和 NSCLC 脑转移患者的 AUC 分别为 0.8019 和 0.8024，总体平均准确率为 0.7515±0.04。DL 模型在鉴别 SCLC 和 NSCLC 脑转移患者方面表现良好。
所提出的 DL 模型在鉴别引起 BM 的 SCLC 和 NSCLC 的病理亚型方面是可行且有效的，它可能成为一种新的工具，用于肿瘤学家根据肿瘤的影像学结构对 BM 进行非侵入性诊断和指导治疗。
