自动分割淋巴瘤并完成卢加诺分期，助力临床精准治疗决策。
AutoLugano: A Deep Learning Framework for Fully Automated Lymphoma Segmentation and Lugano Staging on FDG-PET/CT
- 三步流程：先分割病灶，再定位解剖区域，最后生成分期结果。
- 外部验证准确率达85.07%，对分层治疗判断效果显著。
- 首个端到端自动化系统，适合放射科医生快速辅助诊断。
目的：开发一种全自动深度学习系统AutoLugano，通过基线FDG-PET/CT扫描实现淋巴瘤病变的端到端分类，包括病灶分割、解剖定位和卢加诺分期。方法：该系统包含三个模块：(1) 基于多通道输入的3D nnU-Net模型进行病灶检测；(2) 利用TotalSegmentator工具包，根据解剖规则将病灶映射至21个预定义淋巴结区域；(3) 根据受累区域的空间分布生成卢加诺分期及治疗分组（局限期与进展期）。系统在公开数据集autoPET（n=1,007）上训练，并在独立队列67例患者中外部验证。评估指标包括区域受累检测的准确率、敏感性、特异性及F1分数，以及分期一致性。结果：在外部验证集中，系统整体准确率达88.31%，敏感性74.47%，特异性94.21%，F1分数80.80%，优于基线模型。尤其在关键临床任务——治疗分层（局限期 vs 进展期）中，准确率达85.07%，特异性90.48%，敏感性82.61%。结论：AutoLugano是首个将单次基线FDG-PET/CT扫描直接转化为完整卢加诺分期的全自动端到端流程，展现出辅助初始分期、治疗分层及支持临床决策的强大潜力。
原文摘要 · Abstract (English)
Purpose: To develop a fully automated deep learning system, AutoLugano, for end-to-end lymphoma classification by performing lesion segmentation, anatomical localization, and automated Lugano staging from baseline FDG-PET/CT scans. Methods: The AutoLugano system processes baseline FDG-PET/CT scans through three sequential modules:(1) Anatomy-Informed Lesion Segmentation, a 3D nnU-Net model, trained on multi-channel inputs, performs automated lesion detection (2) Atlas-based Anatomical Localization, which leverages the TotalSegmentator toolkit to map segmented lesions to 21 predefined lymph node regions using deterministic anatomical rules; and (3) Automated Lugano Staging, where the spatial distribution of involved regions is translated into Lugano stages and therapeutic groups (Limited vs. Advanced Stage).The system was trained on the public autoPET dataset (n=1,007) and externally validated on an independent cohort of 67 patients. Performance was assessed using accuracy, sensitivity, specificity, F1-scorefor regional involvement detection and staging agreement. Results: On the external validation set, the proposed model demonstrated robust performance, achieving an overall accuracy of 88.31%, sensitivity of 74.47%, Specificity of 94.21% and an F1-score of 80.80% for regional involvement detection,outperforming baseline models. Most notably, for the critical clinical task of therapeutic stratification (Limited vs. Advanced Stage), the system achieved a high accuracy of 85.07%, with a specificity of 90.48% and a sensitivity of 82.61%.Conclusion: AutoLugano represents the first fully automated, end-to-end pipeline that translates a single baseline FDG-PET/CT scan into a complete Lugano stage. This study demonstrates its strong potential to assist in initial staging, treatment stratification, and supporting clinical decision-making.
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