Xia Xueming, Tan Qiaoyue, Du Wei, Gou Qiheng
Division of Head & Neck Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Front Oncol. 2025 Jul 23;15:1599853. doi: 10.3389/fonc.2025.1599853. eCollection 2025.
This study aims to develop and evaluate a radiomics-based machine learning model using T1-enhanced magnetic resonance imaging (MRI) features to differentiate between lung squamous cell carcinoma (SCC) and adenocarcinoma (AC) in patients with brain metastases (BMs). While prior studies have largely focused on primary lung tumors, our work uniquely targets metastatic brain lesions, which pose distinct diagnostic and therapeutic challenges.
In this retrospective study, 173 patients with BMs from lung cancer were included, consisting of 88 with AC and 85 with SCC. MRI images were acquired using a standardized protocol, and 833 radiomic features were identified from the segmented lesions utilizing the PyRadiomics package. Feature selection was performed using a combination of univariate analysis, correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. Ten machine learning classifiers were trained and validated utilizing the selected features. The performance of the classifier models was assessed through receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was examined for analysis.
Ten classifier models were built on the basis of features derived from MRI. Among the ten classifier models, the LightGBM model performed the best. In the training dataset, the LightGBM classifier achieved an accuracy of 0.814, with a sensitivity of 0.726 and specificity of 0.896. The classifier's efficiency was validated on an independent testing dataset, where it maintained an accuracy of 0.779, with a sensitivity of 0.725 and specificity of 0.857. The AUC was 0.858 for the training dataset and 0.857 for the testing dataset. The model effectively distinguished between SCC and AC based on radiomic features, highlighting its potential for noninvasive non-small cell lung cancer (NSCLC) subtype classification.
This research demonstrates the efficacy of a radiomics-based machine learning model in accurately classifying NSCLC subtypes from BMs, providing a valuable noninvasive tool for guiding personalized treatment strategies. Further validation on larger, multi-center datasets is crucial to verify these findings.
本研究旨在开发并评估一种基于放射组学的机器学习模型，该模型利用T1增强磁共振成像（MRI）特征来区分脑转移瘤（BMs）患者的肺鳞状细胞癌（SCC）和腺癌（AC）。虽然先前的研究主要集中在原发性肺肿瘤上，但我们的工作独特地针对转移性脑病变，这些病变带来了独特的诊断和治疗挑战。
在这项回顾性研究中，纳入了173例来自肺癌的脑转移瘤患者，其中88例为腺癌，85例为鳞状细胞癌。使用标准化方案采集MRI图像，并利用PyRadiomics软件包从分割的病变中识别出833个放射组学特征。采用单变量分析、相关性分析和最小绝对收缩和选择算子（LASSO）回归相结合的方法进行特征选择。利用选定的特征训练并验证了10个机器学习分类器。通过受试者操作特征（ROC）曲线评估分类器模型的性能，并检查曲线下面积（AUC）进行分析。
基于MRI衍生的特征建立了10个分类器模型。在这10个分类器模型中，LightGBM模型表现最佳。在训练数据集中，LightGBM分类器的准确率为0.814，灵敏度为0.726，特异性为0.896。该分类器的效率在独立测试数据集中得到验证，其准确率保持在0.779，灵敏度为0.725，特异性为0.857。训练数据集的AUC为0.858，测试数据集的AUC为0.857。该模型基于放射组学特征有效地区分了鳞状细胞癌和腺癌，突出了其在非小细胞肺癌（NSCLC）亚型无创分类中的潜力。
本研究证明了基于放射组学的机器学习模型在准确分类脑转移瘤的NSCLC亚型方面的有效性，为指导个性化治疗策略提供了一种有价值的无创工具。在更大的多中心数据集上进行进一步验证对于证实这些发现至关重要。
