School of Medical Imaging, Bengbu Medical University, Bengbu, 233030, China.
Department of Radiology, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, 210000, China.
Sci Rep. 2024 Feb 25;14(1):4565. doi: 10.1038/s41598-024-55280-6.
The benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients' DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application.
孤立性肺结节（SPN）的良恶性状态是治疗决策的关键决定因素。本研究的主要目的是验证具有双层探测器光谱 CT（DLCT）参数的机器学习（ML）模型在识别 SPN 良恶性状态方面的功效。本研究纳入了 250 名经病理证实的 SPN 患者。根据患者 DLCT 胸部增强图像上病变的感兴趣区，获得了 8 个定量和 16 个衍生参数。从结合患者临床参数（包括性别、年龄和吸烟史）后选择的 10 个参数中构建了 6 个 ML 模型。逻辑回归模型在测试集上表现出最佳的诊断性能，ROC 曲线下面积（AUC）为 0.812，准确率为 0.813，灵敏度为 0.750，特异性为 0.791。结果表明，基于 DLCT 参数的 ML 模型在识别 SPN 的良恶性方面优于传统 CT 参数模型，具有更大的应用潜力。
