Genç Hasan, Yildirim Mustafa
Department of Radiology, Elaziğ Fethi Sekin City Hospital, Elaziğ, Turkey.
Firat University Faculty of Medicine Hospital, Elaziğ, Turkey.
Medicine (Baltimore). 2025 Sep 12;104(37):e44408. doi: 10.1097/MD.0000000000044408.
Round pneumonia is a benign lung condition that can radiologically mimic primary lung cancer, making diagnosis challenging. Accurately distinguishing between these diseases is critical to avoid unnecessary invasive procedures. This study aims to distinguish round pneumonia from primary lung cancer by developing machine-learning models based on radiomic features extracted from computed tomography (CT) images.
This retrospective observational study included 24 patients diagnosed with round pneumonia and 24 with histopathologically confirmed primary lung cancer. The lesions were manually segmented on the CT images by 2 radiologists. In total, 107 radiomic features were extracted from each case. Feature selection was performed using an information-gain algorithm to identify the 5 most relevant features. Seven machine-learning classifiers (Naïve Bayes, support vector machine, Random Forest, Decision Tree, Neural Network, Logistic Regression, and k-NN) were trained and validated. The model performance was evaluated using AUC, classification accuracy, sensitivity, and specificity.
The Naïve Bayes, support vector machine, and Random Forest models achieved perfect classification performance on the entire dataset (AUC = 1.000). After feature selection, the Naïve Bayes model maintained a high performance with an AUC of 1.000, accuracy of 0.979, sensitivity of 0.958, and specificity of 1.000.
Machine-learning models using CT-based radiomics features can effectively differentiate round pneumonia from primary lung cancer. These models offer a promising noninvasive tool to aid in radiological diagnosis and reduce diagnostic uncertainty.
圆形肺炎是一种良性肺部疾病，在放射学上可模仿原发性肺癌，这使得诊断具有挑战性。准确区分这些疾病对于避免不必要的侵入性检查至关重要。本研究旨在通过基于从计算机断层扫描（CT）图像中提取的放射组学特征开发机器学习模型，来区分圆形肺炎和原发性肺癌。
这项回顾性观察研究纳入了24例被诊断为圆形肺炎的患者和24例经组织病理学证实的原发性肺癌患者。由2名放射科医生在CT图像上手动分割病变。每个病例总共提取了107个放射组学特征。使用信息增益算法进行特征选择，以识别5个最相关的特征。训练并验证了7种机器学习分类器（朴素贝叶斯、支持向量机、随机森林、决策树、神经网络、逻辑回归和k近邻）。使用AUC、分类准确率、敏感性和特异性评估模型性能。
朴素贝叶斯、支持向量机和随机森林模型在整个数据集上实现了完美的分类性能（AUC = 1.000）。经过特征选择后，朴素贝叶斯模型保持了高性能，AUC为1.000，准确率为0.979，敏感性为0.958，特异性为1.000。
使用基于CT的放射组学特征的机器学习模型可以有效区分圆形肺炎和原发性肺癌。这些模型提供了一种有前景的非侵入性工具，有助于放射学诊断并减少诊断不确定性。
