谷歌AI研究所的Daniel Tse等研究人员取得一项成果，他们开发出了一种可预测肺部恶性肿瘤的深度学习模型。这种AI能够发现早期没被发现的肺癌病人，还能减少肺癌的误诊率。相关论文发表在2019年出版的《Nature Medicine》杂志上。

课题组人员提出了一种深度学习算法，该算法以患者当前和之前的计算机断层扫描量为主题来预测肺癌的风险。在全国6716例测试数据集中，他们的模型达到了94.4%的曲线下面积，并对1139例的独立临床验证集执行了类似操作。当先前的计算机断层扫描成像不可用时，他们的模型优于所有六位放射科医师，假阳性绝对减少11％，假阴性绝对减少5％。在先前的计算机断层扫描成像可用的情况下，模型性能与相同的放射科医师相当。这创造了一个通过计算机的帮助和自动化来优化筛选的过程。在现今大多数患者没有被筛查的情况下，课题组显示潜在的深度学习模型可以提高全球肺癌筛查的准确性和一致性。

据估计，2018年美国约有16万人死于肺癌，肺癌是美国最常见的癌症死亡原因。使用低剂量计算机断层扫描进行肺癌筛查已被证明可将死亡率降低2043%，目前已被纳入美国筛查指南。现有的挑战包括级间的变异性和高假阳性和假阴性率。

附：英文原文

Title: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Author: Diego Ardila, Atilla P. Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J. Reicher, Lily Peng, Daniel Tse, Mozziyar Etemadi, Wenxing Ye, Greg Corrado, David P. Naidich, Shravya Shetty

Issue&Volume: Volume 25 Issue 6，June 2019

Abstract: With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 2043% and is now included in US screening guidelines. Existing challenges include inter-grader variability and high false-positive and false-negative rates. We propose a deep learning algorithm that uses a patients current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.

DOI: 10.1038/s41591-019-0447-x

Source: https://www.nature.com/articles/s41591-019-0447-x