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Abstract：
Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam.
摘要：
肺癌的早期诊断可以显着改善患者的预后。我们开发了基于Wasserstein生成对抗网络框架（GP-WGAN）的增长预测模型，以预测后续LDCT扫描中的结节生长模式。GP-WGAN使用包含约1年间隔的1121对结节图像的训练集（N=776）进行训练，并在基线LDCT扫描中部署到450个结节的独立测试集以预测结节图像（GP结节）在他们的1年随访扫描中。最后通过肺癌风险预测（LCRP）模型将450个GP结节分为恶性或良性。达到0.827±0.028的测试AUC，这与通过对真实随访结节图像进行分类的相同LCRP模型获得的0.862±0.028的AUC相当（p=0.071）。净重新分类指数产生了一致的结果(NRI=0.04；p=0.62)。其他基线方法，包括Lung-RADS和Brock模型,取得了显著较低的性能(p<0.05)。结果表明，我们的GP-WGAN模型预测的GP结节在肺癌诊断的真实随访扫描中实现了与结节相当的性能，与目前的等待下一次筛查的方法相比，与加速的临床管理相结合，表明更早发现肺癌的潜力。
