Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China.
Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
BMC Med Imaging. 2024 May 24;24(1):121. doi: 10.1186/s12880-024-01300-w.
At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT.
A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model.
By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model.
Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.
目前，评估肺腺癌淋巴结转移存在诸多局限性。因此，人们需要一种安全、准确的方法来预测肺癌的淋巴结转移。本研究基于增强 CT 利用影像组学准确预测肺腺癌患者的淋巴结状态。
本研究共纳入两家医院符合分析要求的 503 例患者。其中 287 例患者有淋巴结转移（LNM+），216 例患者无淋巴结转移（LNM-）。对每位患者增强 CT 分割图像分别采用传统方法和深度学习方法提取 22318 个特征，然后采用 Spearman 检验和最小绝对收缩和选择算子（LASSO）进行特征降维，筛选出最有意义的特征，提高整体分析效果。最后，采用机器学习算法构建肺腺癌淋巴结转移分类模型，用准确率、AUC、特异度、敏感度、召回率和 F1 值评价模型效能。
采用极端梯度提升法（XGBoost）提取并综合选择特征构建的预测模型，能够有效区分肺腺癌患者淋巴结状态。外部测试集模型预测的准确率、AUC、特异度、敏感度、召回率和 F1 值分别为 0.765、0.845、0.705、0.784、0.811 和 0.797。此外，模型在外部测试集上的决策曲线分析、校准曲线和混淆矩阵也均表明了模型的稳定性和准确性。
本研究基于增强 CT 图像，利用极端梯度提升法构建了一种非侵入性分类预测模型，对肺腺癌患者的淋巴结状态具有较高的识别精度，为临床提供了一种安全、准确的替代侵袭性操作的方法。该方法为临床医生提供了一种可靠的诊断和评估疾病进展的工具，有望改善患者的预后，提高临床实践的整体质量。
