Highly accurate model for prediction of lung nodule malignancy with CT scans.
Highly accurate model for prediction of lung nodule malignancy with CT scans.
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DOI：
10.1038/s41598-018-27569-w
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发表时间：
2018-06-18
影响因子：
4.6
通讯作者：
Huang X
中科院分区：
文献类型：
--
作者：
Causey JL;Zhang J;Ma S;Jiang B;Qualls JA;Politte DG;Prior F;Zhang S;Huang X
计算机断层扫描（CT）检查通常用于预测患者的肺结节恶性程度，这表明可以提高肺癌的无创早期诊断。它仍然具有挑战性的计算方法，以实现性能相比，经验丰富的放射科医生。在这里，我们介绍了NoduleX，这是一种基于深度学习卷积神经网络（CNN）从CT数据预测肺结节恶性肿瘤的系统方法。为了训练和验证，我们分析了来自LIDC/IDRI队列的图像中的>1000个肺结节。所有结节均由参与LIDC项目的四位经验丰富的胸部放射科医生进行识别和分类。NoduleX实现了结节恶性分类的高准确性，AUC约为0.99。这与有经验的放射科医生对数据集的分析是相称的。我们的方法NoduleX为高度准确的结节恶性肿瘤预测提供了一个有效的框架，该模型在大量患者人群中进行了训练。我们的结果可以用http://bioinformatics.astate.edu/NoduleX上的软件复制。
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX.
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