论文标题
3D神经网络用于CT体积的肺癌风险预测
3D Neural Network for Lung Cancer Risk Prediction on CT Volumes
论文作者
论文摘要
肺癌估计在2018年死亡，是美国癌症死亡的最常见原因。肺癌CT筛查已显示可将死亡率降低多达40％，现在已包含在美国筛查指南中。由于诊断错误引起的临床和财务成本很高，因此必须降低肺癌筛查中的高错误率。尽管使用标准来进行放射学诊断，但持续的研究生变异性和全面成像发现的不完整表征仍然是当前方法的局限性。这些限制为更复杂的系统提供了提高性能和阅读器间一致性的机会。在本报告中，我们复制了一种最先进的深度学习算法，用于肺癌风险预测。我们的模型可预测肺CT研究中的恶性概率和风险桶分类。这允许对正在筛查的患者进行风险分类，并提出最合适的监视和管理。结合我们的解决方案的高精度，一致性和完全自动化的性质，我们的方法可以实现高效的筛查程序并加速采用肺癌筛查。
With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States. Lung cancer CT screening has been shown to reduce mortality by up to 40% and is now included in US screening guidelines. Reducing the high error rates in lung cancer screening is imperative because of the high clinical and financial costs caused by diagnosis mistakes. Despite the use of standards for radiological diagnosis, persistent inter-grader variability and incomplete characterization of comprehensive imaging findings remain as limitations of current methods. These limitations suggest opportunities for more sophisticated systems to improve performance and inter-reader consistency. In this report, we reproduce a state-of-the-art deep learning algorithm for lung cancer risk prediction. Our model predicts malignancy probability and risk bucket classification from lung CT studies. This allows for risk categorization of patients being screened and suggests the most appropriate surveillance and management. Combining our solution high accuracy, consistency and fully automated nature, our approach may enable highly efficient screening procedures and accelerate the adoption of lung cancer screening.
