Abstract
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 on 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 premise for cancer image analysis, with significantly enhanced predictive power compared to other hand-crafted radiomics features. Here we show that CNN 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 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET/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) than the images and the corresponding tumor contours provided by physicians. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of progression appear to match with the regions where the U-Net features identified patterns that predicted higher likelihood of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination.
Abstract (translated)
非小细胞肺癌（NSCLC）约占肺癌诊断的80-85%，是全世界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）图像中基于图像的放射性特征对非小细胞肺癌的预后具有预测能力。因此，标准摄取值（SUV）的最大值和平均值（TLG）和总病变糖酵解（TLG）等易于计算的功能特征最常用于NSCLC预后，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一个新前提，与其他手工制作的放射成像特征相比，它的预测能力显著增强。在这里，我们显示CNN接受过肿瘤分割任务的训练，除了医师轮廓外，没有其他信息，能够识别出一组丰富的生存相关图像特征，具有显著的预后价值。在对96例立体定向体放射治疗（SBRT）前NSCLC患者的回顾性研究中，我们发现在PET/CT图像中训练用于肿瘤分割的CNN分割算法（U-NET）包含与2年和5年总生存率和疾病特异性生存率有很强相关性的特征。U-NET算法没有看到任何其他临床信息（如生存率、年龄、吸烟史）比医生提供的图像和相应的肿瘤轮廓。此外，通过对U-NET的可视化，我们还发现了令人信服的证据，即进展区域似乎与U-NET特征识别的模式相匹配，这些模式预测更高的死亡可能性。我们预计我们的发现将是更复杂的非侵入性患者特异性癌症预后判断的起点。
URL
https://arxiv.org/abs/1903.11593
