Murphy Andrew, Skalski Matthew, Gaillard Frank
1 Discipline of Medical Radiation Sciences, Faculty of Health Sciences, The University of Sydney , Sydney, NSW , Australia.
2 Department of Medical Imaging, Princess Alexandra Hospital , Brisbane, QLD , Australia.
Br J Radiol. 2018 Oct;91(1090):20180028. doi: 10.1259/bjr.20180028. Epub 2018 Jun 19.
Lung cancer is one of the leading causes of cancer-related fatality in the world. Patients display few or even no signs or symptoms in the early stages, resulting in up to 75% of patients diagnosed in the later stages of the disease. Consequently, there has been a call for lung cancer screening amongst at-risk populations. The early detection of malignant pulmonary nodules in CT is one of the suggested methods proposed to diagnose early-stage lung cancer; however, the reported sensitivity of radiologists' ability to accurately detect pulmonary nodules ranges widely from 30 to 97%. 2012 saw Alex Krizhevsky present a paper titled "ImageNet Classification with Deep Convolutional Networks" in which a multilayered convolutional computational model known as a convolutional neural network (CNN) was confirmed competent in identifying and classifying 1.2 million images to a previously unseen level of accuracy. Since then, CNNs have gained attention as a potential tool in aiding radiologists' detection of pulmonary nodules in CT imaging. This review found the use of CNN is a viable strategy to increase the overall sensitivity of pulmonary nodule detection. Small, non-validated data sets, computational constraints, and incomparable studies are currently limited factors of the existing research.
肺癌是全球癌症相关死亡的主要原因之一。患者在疾病早期几乎没有甚至没有任何体征或症状，导致高达75%的患者在疾病晚期才被诊断出来。因此，人们呼吁对高危人群进行肺癌筛查。CT检查中早期发现恶性肺结节是诊断早期肺癌的建议方法之一；然而，据报道，放射科医生准确检测肺结节的能力敏感性差异很大，从30%到97%不等。2012年，亚历克斯·克里兹hevsky发表了一篇题为《使用深度卷积网络进行ImageNet分类》的论文，其中一种名为卷积神经网络（CNN）的多层卷积计算模型被证实能够以前所未有的精度识别和分类120万张图像。从那时起，卷积神经网络作为一种辅助放射科医生在CT成像中检测肺结节的潜在工具受到了关注。本综述发现，使用卷积神经网络是提高肺结节检测总体敏感性的可行策略。小型、未经验证的数据集、计算限制和不可比的研究是目前现有研究的限制因素。
