标题： MedSR-影响：基于Transformer的肺部CT分割、放射组学、分类和预后超分辨率 显示英文标题

标题： MedSR-Impact: Transformer-Based Super-Resolution for Lung CT Segmentation, Radiomics, Classification, and Prognosis

摘要： 高分辨率容积计算机断层扫描（CT）对于胸部疾病的准确诊断和治疗计划至关重要；然而，它受到辐射剂量和硬件成本的限制。我们提出了Transformer Volumetric超分辨率网络（\textbf{TVSRN-V2}），这是一个基于Transformer的超分辨率（SR）框架，专为临床肺部CT分析的实际部署而设计。该模型由可扩展组件构建而成，包括跨平面注意力块（TAB）和Swin Transformer V2，能够有效重建低剂量CT体积中的精细解剖细节，并与下游分析流程无缝集成。我们在三个关键的肺癌任务——叶分割、影像组学和预后——上对它的有效性进行了评估，覆盖多个临床队列。为了增强在不同采集协议下的鲁棒性，我们引入了伪低分辨率增强，无需私有数据即可模拟扫描仪多样性。TVSRN-V2在分割准确性方面显著提高（Dice分数提高4%），放射组学特征的可重复性更高，并且预测性能增强（C指数和AUC分别提高0.06）。这些结果表明，结构细节的超分辨率驱动恢复显著增强了临床决策支持，使TVSRN-V2成为一种工程良好、临床上可行的系统，适用于现实世界CT工作流程中的剂量效率成像和定量分析。 显示英文摘要

摘要： High-resolution volumetric computed tomography (CT) is essential for accurate diagnosis and treatment planning in thoracic diseases; however, it is limited by radiation dose and hardware costs. We present the Transformer Volumetric Super-Resolution Network (\textbf{TVSRN-V2}), a transformer-based super-resolution (SR) framework designed for practical deployment in clinical lung CT analysis. Built from scalable components, including Through-Plane Attention Blocks (TAB) and Swin Transformer V2 -- our model effectively reconstructs fine anatomical details in low-dose CT volumes and integrates seamlessly with downstream analysis pipelines. We evaluate its effectiveness on three critical lung cancer tasks -- lobe segmentation, radiomics, and prognosis -- across multiple clinical cohorts. To enhance robustness across variable acquisition protocols, we introduce pseudo-low-resolution augmentation, simulating scanner diversity without requiring private data. TVSRN-V2 demonstrates a significant improvement in segmentation accuracy (+4\% Dice), higher radiomic feature reproducibility, and enhanced predictive performance (+0.06 C-index and AUC). These results indicate that SR-driven recovery of structural detail significantly enhances clinical decision support, positioning TVSRN-V2 as a well-engineered, clinically viable system for dose-efficient imaging and quantitative analysis in real-world CT workflows.