暗场X射线成像结合深度学习，显著提升小鼠模型中早期肺癌的检出率。
Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models
- 用暗场成像与衰减成像融合训练分割网络，增强微小肿瘤识别能力。
- 仅用暗场成像时检测率达83.7%，远超传统成像的51%。
- 适合资源有限或缺乏LDCT设备的早期筛查场景。
低剂量计算机断层扫描（LDCT）是当前肺癌筛查的标准，但其普及受限于设备不足及高假阳性率——如国家肺筛查试验（NLST）显示灵敏度为93.8%，假阳性率达26.6%。本研究探索暗场X射线成像（DFI）在深度学习分割中的应用，该技术对肺泡微结构的小角散射敏感，且受器官遮挡影响较小。通过使用处死小鼠肺部的配对衰减（ATTN）和DFI影像，生成具有不规则边界和真实肺部对比度的合成肿瘤。训练了基于小块图像的U-Net分割网络，分别使用ATTN、DFI或两者组合输入。结果表明，仅使用DFI的模型真阳性检出率为83.7%，优于仅用ATTN的51%，特异性相近（90.5% vs 92.9%）。联合输入达到79.6%灵敏度和97.6%特异性。结论：相较于传统衰减成像，DFI显著提升早期肿瘤可探测性，具备作为无LDCT地区预临床或资源匮乏区域筛查的低成本、低剂量替代方案潜力。
原文摘要 · Abstract (English)
Low-dose computed tomography (LDCT) is the current standard for lung cancer screening, yet its adoption and accessibility remain limited. Many regions lack LDCT infrastructure, and even among those screened, early-stage cancer detection often yield false positives, as shown in the National Lung Screening Trial (NLST) with a sensitivity of 93.8 percent and a false-positive rate of 26.6 percent. We aim to investigate whether X-ray dark-field imaging (DFI) radiograph, a technique sensitive to small-angle scatter from alveolar microstructure and less susceptible to organ shadowing, can significantly improve early-stage lung tumor detection when coupled with deep-learning segmentation. Using paired attenuation (ATTN) and DFI radiograph images of euthanized mouse lungs, we generated realistic synthetic tumors with irregular boundaries and intensity profiles consistent with physical lung contrast. A U-Net segmentation network was trained on small patches using either ATTN, DFI, or a combination of ATTN and DFI channels. Results show that the DFI-only model achieved a true-positive detection rate of 83.7 percent, compared with 51 percent for ATTN-only, while maintaining comparable specificity (90.5 versus 92.9 percent). The combined ATTN and DFI input achieved 79.6 percent sensitivity and 97.6 percent specificity. In conclusion, DFI substantially improves early-tumor detectability in comparison to standard attenuation radiography and shows potential as an accessible, low-cost, low-dose alternative for pre-clinical or limited-resource screening where LDCT is unavailable.
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