Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics.
Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics.
复制标题
利用胸部 CT 图像和临床人口统计数据的共同学习进行肺癌检测。
DOI：
10.1117/12.2512965
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Landman,BennettA
中科院分区：
文献类型：
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作者：
Wang,Jiachen;Gao,Riqiang;Huo,Yuankai;Bao,Shunxing;Xiong,Yunxi;Antic,SanjaL;Osterman,TravisJ;Massion,PierreP;Landman,BennettA
肺癌的早期发现对于降低死亡率至关重要。最近的研究表明，低剂量计算机断层扫描（CT）的临床效用，以检测肺癌的个人选择的基础上非常有限的临床信息。然而，这种策略产生高假阳性率，这可能导致不必要的和潜在的有害程序。为了应对这些挑战，我们建立了一个管道，从详细的临床人口统计数据和3D CT图像中共同学习。为此，我们利用了来自筛查病变分子和细胞表征联盟（MCL）的数据，该联盟专注于肺癌的早期检测。提出了一种基于3D注意力的深度卷积神经网络（DCNN），用于在没有可疑结节的先前解剖位置的情况下从胸部CT扫描中识别肺癌。为了改进良性和恶性之间的非侵入性区分，我们将随机森林分类器应用于将临床信息与成像数据集成的数据集。结果显示，仅从临床人口统计学获得的AUC为0.635，而仅注意力网络达到0.687的准确度。相比之下，当应用我们提出的集成临床和成像变量的管道时，我们在测试数据集上达到了0.787的AUC。所提出的网络既能有效地捕获解剖信息进行分类，又能生成注意力地图，解释驱动性能的特征。
Early detection of lung cancer is essential in reducing mortality. Recent studies have demonstrated the clinical utility of low-dose computed tomography (CT) to detect lung cancer among individuals selected based on very limited clinical information. However, this strategy yields high false positive rates, which can lead to unnecessary and potentially harmful procedures. To address such challenges, we established a pipeline that co-learns from detailed clinical demographics and 3D CT images. Toward this end, we leveraged data from the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), which focuses on early detection of lung cancer. A 3D attention-based deep convolutional neural net (DCNN) is proposed to identify lung cancer from the chest CT scan without prior anatomical location of the suspicious nodule. To improve upon the non-invasive discrimination between benign and malignant, we applied a random forest classifier to a dataset integrating clinical information to imaging data. The results show that the AUC obtained from clinical demographics alone was 0.635 while the attention network alone reached an accuracy of 0.687. In contrast when applying our proposed pipeline integrating clinical and imaging variables, we reached an AUC of 0.787 on the testing dataset. The proposed network both efficiently captures anatomical information for classification and also generates attention maps that explain the features that drive performance.