文章：

提升早期肺癌诊断：利用深度生成模型预测随访低剂量CT扫描中肺结节的进展

Enhancing Early Lung Cancer Diagnosis: Predicting Lung Nodule Progression in Follow-Up Low-Dose CT Scan with Deep Generative Model

原文发布日期：15 June 2024

DOI: 10.3390/cancers16122229

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

肺癌的早期诊断可显著改善患者预后。本研究基于Wasserstein生成对抗网络框架开发了生长预测模型（GP-WGAN），用于预测随访低剂量CT扫描中的结节生长模式。该模型使用包含1121对间隔约1年结节图像的训练集（N=776）进行训练，并部署于包含450个基线低剂量CT扫描结节的独立测试集，以预测其1年随访扫描中的结节图像（GP-结节）。最终通过肺癌风险预测模型对这450个GP-结节进行恶性/良性分类，获得0.827±0.028的测试AUC值，与同一肺癌风险预测模型对真实随访结节图像分类所得的AUC值0.862±0.028具有可比性（p=0.071）。净重分类指数结果一致（NRI=0.04；p=0.62）。其他基线方法（包括Lung-RADS和Brock模型）性能显著较低（p<0.05）。结果表明，本研究所建GP-WGAN模型预测的GP-结节在肺癌诊断中与真实随访扫描结节具有相当效能，提示相较于当前需等待下次筛查的策略，结合加速临床管理流程可能实现更早期的肺癌检测。

原文链接：

Enhancing Early Lung Cancer Diagnosis: Predicting Lung Nodule Progression in Follow-Up Low-Dose CT Scan with Deep Generative Model