Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules
Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules
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DOI：
10.1164/rccm.201903-0505oc
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发表时间：
2020-07-15
影响因子：
24.7
通讯作者：
Gleeson, Fergus
中科院分区：
文献类型：
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作者：
Massion, Pierre P.;Antic, Sanja;Gleeson, Fergus
理论基础：不明肺结节(IPN)的管理仍然具有挑战性，导致侵入性程序和诊断和治疗的延误。目的：开发和验证一种深度学习方法，以改进IPN的管理。方法：使用国家肺部筛查试验的IPN的计算机断层扫描图像训练肺癌预测卷积神经网络模型，并在两个学术机构的队列上进行内部验证和外部测试。测量和主要结果：外部验证队列中接受者操作特征曲线下的面积分别为83.5%(95%可信区间[CI]，75.4-90.7%)和91.9%(95%CI，88.7-94.7%)，而常用的偶发结节临床风险模型分别为78.1%(95%CI，68.7-86.4%)和81.9%(95%CI，76.1-87.1%)。使用定义低风险和高风险类别的5%和65%的恶性阈值，与Mayo模型相比，癌症和良性结节验证队列中的总体净重分类作为规则检验是0.34(Vanderbilt)和0.30(Oxford)，以及0.33(Vanderbilt)和0.58(Oxford)作为排除检验。与传统的风险预测模型相比，肺癌预测卷积神经网络在每个管理阈值和我们的外部验证队列中预测疾病可能性的准确性都有所提高。结论：与传统风险模型相比，该深度学习算法可以正确地将三分之一以上的癌症和良性结节的IPN重新分类为低风险或高风险类别，潜在地减少了不必要的侵入性操作和诊断延迟。
Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed.Objectives: To develop and validate a deep learning method to improve the management of IPNs.Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions.Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4-90.7%) and 91.9% (95% CI, 88.7-94.7%), compared with 78.1% (95% CI, 68.7-86.4%) and 81.9 (95% CI, 76.1-87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts.Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.