Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain.
Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain; BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Comput Methods Programs Biomed. 2020 Mar;185:105172. doi: 10.1016/j.cmpb.2019.105172. Epub 2019 Nov 2.
The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules; METHODS: In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection. For training and testing purposes we used independent subsets of the LIDC dataset; RESULTS: Adding the probabilities of nodules malignity in a baseline lung cancer pipeline improved its F1-weighted score by 14.7%, whereas integrating the malignancy model itself using transfer learning outperformed the baseline prediction by 11.8% of F1-weighted score; CONCLUSIONS: Despite the limited size of the lung cancer datasets, integrating predictive models of nodule malignancy improves prediction of lung cancer.
早期识别恶性肺结节对于改善肺癌预后和减少侵袭性化疗或放疗至关重要。放射科医生对结节良恶性的评估对于计划预防性干预非常有用，但不幸的是，这是一项复杂、耗时和容易出错的任务。这就解释了为什么缺乏包含放射科医生对结节恶性特征描述的大型数据集；方法：在本文中，我们提出通过 3D 卷积神经网络来评估结节的恶性，并将其集成到现有的肺癌检测自动化端到端管道中。为了培训和测试目的，我们使用了 LIDC 数据集的独立子集；结果：在基线肺癌管道中添加结节恶性概率可将其 F1 加权分数提高 14.7%，而使用迁移学习集成恶性模型本身则可将 F1 加权分数提高 11.8%；结论：尽管肺癌数据集的规模有限，但整合结节恶性预测模型可以提高肺癌的预测。
