整合临床、CT和实验室特征的机器学习诊断模型的开发和验证，以区分孤立性肺结节患者的肺癌和肺结核：一项单中心回顾性研究
Development and validation of machine learning diagnostic models integrating clinical, CT, and laboratory features to differentiate lung cancer from pulmonary tuberculosis in patients with solitary pulmonary nodules: a single-center retrospective study.
作者
作者单位
- Department of Medical Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
- Department of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Clinic and Research Center of Tuberculosis, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
- Innovation and Incubation Center (IIC), Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
摘要
中文
在孤立性肺结节（SPNs）患者中区分肺癌和肺结核在临床上仍然具有挑战性，尤其是在结核病流行地区，因为这两种情况可能表现出重叠的计算机断层扫描（CT）形态特征。本研究旨在开发和内部验证整合这些常规可用变量的机器学习（ML）诊断模型，并通过特征重要性分析确定稳定的判别特征。这项单中心回顾性诊断预测模型开发和内部验证研究纳入了2020年5月至2024年6月期间CT检测到SPNs ≤3 cm并明确诊断为原发性肺癌或肺结核的成年患者。候选预测因子来自基线临床信息、结核病相关检测、人工评估的CT形态特征、循环肿瘤细胞指标、常规实验室检查和术前或活检前7天内获得的血气分析变量。缺失超过5%的变量被排除，在最小绝对收缩和选择算子（LASSO）特征选择之前保留了72个变量。数据集按7:3分层随机分为训练集和验证集。随后构建了七种ML模型，包括逻辑回归（LR）、随机森林（RF）、极端随机树（ET）、径向基函数支持向量机（RBF-SVM）、k近邻（KNN）、多层感知器（MLP）和梯度提升决策树（GBDT）。使用受试者工作特征（ROC）曲线的曲线下面积（AUC）、敏感性、特异性和平衡准确性评估模型性能。进一步进行了置换重要性和SHapley Additive exPlanations (SHAP)分析以评估模型可解释性。共纳入431名患者，包括168名病理确诊的肺癌患者和263名肺结核患者。中位年龄为60.00岁（四分位距，53.00-67.00岁），277名患者（64.3%）为男性，349名患者（81.0%）有实性结节，277名患者（64.3%）QuantiFERON-TB (QFT)结果阳性。经过缺失过滤，保留了72个变量，LASSO为模型开发选择了29个特征。在这七种模型中，RBF-SVM模型实现了最高的验证AUC为0.803，敏感性为0.686，特异性为0.734，平衡准确性为0.710。ET模型表现出相当的验证性能，AUC为0.791，而LR实现了最高的平衡准确性0.711。跨模型可解释性分析确定结节类型和年龄是最稳定的核心特征。整合常规临床、人工评估CT和实验室特征的ML模型在区分SPNs患者的肺癌和肺结核方面表现出中等的判别性能。RBF-SVM模型实现了最高的验证AUC，跨模型可解释性分析确定结节类型和年龄是稳定的判别特征。
English
Differentiating lung cancer from pulmonary tuberculosis in patients with solitary pulmonary nodules (SPNs) remains clinically challenging, particularly in tuberculosis-endemic settings, because these two conditions may show overlapping computed tomography (CT) morphological features. This study aimed to develop and internally validate machine learning (ML) diagnostic models integrating these routinely available variables and to identify stable discriminative features using feature-importance analyses. This single-center retrospective diagnostic prediction model development and internal validation study included adult patients with CT-detected SPNs measuring ≤3 cm and a definitive diagnosis of primary lung cancer or pulmonary tuberculosis between May 2020 and June 2024. Candidate predictors were extracted from baseline clinical information, tuberculosis-related tests, manually assessed CT morphological features, circulating tumor cell indicators, routine laboratory tests, and blood gas analysis variables obtained within 7 days before surgery or biopsy. Variables with more than 5% missingness were excluded, and 72 variables were retained before least absolute shrinkage and selection operator (LASSO) feature selection. The dataset was randomly divided into training and validation sets in a stratified 7:3 ratio. Seven ML models were subsequently constructed, including logistic regression (LR), random forest (RF), extra trees (ET), radial basis function support vector machine (RBF-SVM), k-nearest neighbors (KNN), multilayer perceptron (MLP), and gradient boosting decision tree (GBDT). Model performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic (ROC) curve, sensitivity, specificity, and balanced accuracy. Permutation importance and SHapley Additive exPlanations (SHAP) analyses were further performed to assess model interpretability. A total of 431 patients were included, comprising 168 patients with pathologically confirmed lung cancer and 263 patients with pulmonary tuberculosis. The median age was 60.00 years (interquartile range, 53.00-67.00 years), 277 patients (64.3%) were male, 349 patients (81.0%) had solid nodules, and 277 patients (64.3%) had positive QuantiFERON-TB (QFT) results. After missingness filtering, 72 variables were retained, and 29 features were selected by LASSO for model development. Among the seven models, the RBF-SVM model achieved the highest validation AUC of 0.803, with a sensitivity of 0.686, specificity of 0.734, and balanced accuracy of 0.710. The ET model showed comparable validation performance, with an AUC of 0.791, whereas LR achieved the highest balanced accuracy of 0.711. Cross-model interpretability analyses identified nodule type and age as the most stable core features. ML models integrating routine clinical, manually assessed CT, and laboratory features showed moderate discriminative performance for differentiating lung cancer from pulmonary tuberculosis in patients with SPNs. The RBF-SVM model achieved the highest validation AUC, and cross-model interpretability analyses identified nodule type and age as stable discriminative features.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q2
- 影响因子
- 3.4
- 新锐分区
- 3区