Deep segmentation networks predict survival of non-small cell lung cancer
Deep segmentation networks predict survival of non-small cell lung cancer
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DOI：
10.1038/s41598-019-53461-2
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发表时间：
2019-11-21
影响因子：
4.6
通讯作者：
Wu, Xiaodong
中科院分区：
文献类型：
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作者：
Baek, Stephen;He, Yusen;Wu, Xiaodong
非小细胞肺癌（NSCLC）占肺癌诊断的约80-85%，是全球癌症相关死亡的主要原因。最近的研究表明，正电子发射断层扫描/计算机断层扫描（PET/CT）图像的基于图像的放射组学特征对NSCLC结局具有预测能力。为此，容易计算的功能特征，如标准摄取值（SUV）和总病变糖酵解（TLG）的最大值和平均值最常用于NSCLC的预测，但其预后价值仍存在争议。与此同时，卷积神经网络（CNN）正迅速成为癌症图像分析的新方法，与手工制作的放射组学特征相比，其预测能力显著增强。在这里，我们展示了经过训练的CNN执行肿瘤分割任务，除了医生轮廓之外没有其他信息，识别出一组丰富的与生存相关的图像特征，具有显着的预后价值。在一项对96例NSCLC患者在立体定向体部放疗（SBRT）前的治疗前PET-CT图像进行的回顾性研究中，我们发现针对PET和CT图像中的肿瘤分割进行训练的CNN分割算法（U-Net）包含与2年和5年总体和疾病特异性生存率具有强相关性的特征。U-Net算法尚未发现任何其他临床信息（例如，生存期、年龄、吸烟史等）。而不是医生提供的图像和相应的肿瘤轮廓。此外，我们通过验证U-Net功能对斯坦福大学癌症研究所提供的校外数据集，观察到相同的趋势。此外，通过对U-Net的可视化，我们还发现了令人信服的证据，即转移和复发的区域似乎与U-Net特征识别出的预测死亡可能性较高的模式相匹配。我们预计我们的研究结果将成为更复杂的非侵入性患者特异性癌症预后确定的起点。例如，深度学习的PET/CT特征不仅可以预测生存率，还可以可视化原发性肿瘤内或邻近的高风险区域，因此可能通过治疗策略的最佳选择或一线治疗调整来影响治疗结果。
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.