Background: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images.
Materials and methods: Open-source data sets and multicenter data sets have been used in this study. A three-dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results.
Results: The sensitivity and specificity of this well-trained model were found to be 84.4% (95% confidence interval [CI], 80.5%-88.3%) and 83.0% (95% CI, 79.5%-86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10-30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three-dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment.
Conclusion: Under the companion diagnostics, the three-dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices.
Implications for practice: The three-dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.
摘要
背景。在肺癌的诊断中，计算机断层扫描 (CT) 对于肺结节的检测必不可少。近几年，随着医学领域逐渐认识到深度学习算法这种技术的价值，本研究试图集成一种训练有素的深度学习算法，对临床 CT 图像中的肺结节进行检测和分类。
材料和方法。本研究使用了开源数据集和多中心数据集。本文设计了一种三维卷积神经网络 (CNN) 来检测肺结节，然后根据病理和实验室证实的结果，判断为恶性或良性结节。
结果。这种训练有素的模型敏感性和特异性分别为 84.4% [95% 可信区间 (CI)， 80.5%‐88.3%]和83.0%(95% CI，79.5%‐86.5%)。小结节 (< 10mm) 亚组分析显示的敏感性和特异性显著，与大结节 (10‐30mm) 相似。对比不同级别医生的人工评估结果与三维 CNN 的评估结果，进行了额外的模型验证。结果表明，CNN 模型的表现优于人工评估。
结论。通过伴随诊断可知，加入深度学习算法的三维 CNN 能够提供准确、及时的信息，有助于放射科医生在常规临床实践中的肺结节诊断工作。
实践意义:在对各种直径的肺结节分类中，本文所述的三维卷积神经网络具有较高的敏感性和特异性，与人工评估结果相比具有优越性。虽然仍需在更大的筛选队列中进行进一步改进和验证，但可以肯定的是，临床应用三维卷积神经网络可以促进和协助医生的临床实践工作。
Keywords: Convolutional neural network; Diagnostics; Lung cancer; Pulmonary nodule.
© AlphaMed Press 2019.