[论文解读] Automated Dose-Based Anatomic Region Classification of Radiotherapy Treatment for Big Data Applications

Justin Hink, Yasin Abdulkadir+2 more|arXiv (Cornell University)|Feb 26, 2026

Advanced Radiotherapy Techniques被引用 0

一句话总结

这项工作利用深度学习分割结构的批量DICOM工作流，从剂量-体积重叠推断解剖区域，在放射治疗计划的大数据分析中实现高精度标签化。

ABSTRACT

Curation is a significant barrier to using 'big data' radiotherapy planning databases of 100,000+ patients. Anatomic site stratification is essential for downstream analyses, but current methods rely on inconsistent plan labels or target nomenclature, which is unreliable for multi-institutional data. We developed software to automate labeling by inferring anatomic regions directly from dose-volume overlap with deep-learning segmentations, eliminating metadata reliance. The software processes DICOM files in bulk, utilizing deep learning to segment 118 structures (organs, glands, and bones) categorized into six regions: Cranial, Head and Neck, Pelvis, Abdomen, Thorax, Extremity. The 85% and 50% isodose lines are converted to structures to compute organ-specific dose-overlap metrics. Plans are assigned ranked regional labels based on these intersections. The algorithm was refined using 109 expert-labeled cases and validated on 100 consecutive clinical plans. On the 100-plan test dataset, the algorithm achieved 91% Exact Accuracy (matching all expert labels and order), 94% Top-2 Accuracy (matching the top two expert regions regardless of order), and 95% Top-1 Accuracy (matching the primary expert label). The automated workflow demonstrated high accuracy and robustness. The 95% Top-1 Accuracy is particularly significant, as it enables reliable querying of plans based on the primary treatment site. Detailed analysis of the few mismatched cases showed most were treated areas at the border between anatomic regions and were ambiguous between these two regions in a common-sense interpretation. This algorithm provides a scalable, standardized solution for curating the large, multi-institutional datasets required for 'big data' in radiotherapy and provides an important complement to text-based approaches.

研究动机与目标

• 通过消除对不一致计划标签的依赖，缓解大型多机构放疗数据库的整理瓶颈。
• 通过深度学习分割将剂量-体积重叠推断出的解剖区域实现治疗计划的自动标注。
• 实现可扩展、标准化的整理，以支持放疗计划中的大数据分析。

提出的方法

• 批量处理DICOM文件。
• 使用深度学习将118个结构（器官、腺体、骨骼）分割为六个区域。
• 将85%和50%等剂量等值线转化为结构，以计算器官特异的剂量-重叠指标。
• 基于剂量重叠与分割的交集为计划分配分级区域标签。

实验结果

研究问题

• RQ1 自动化的基于剂量的重叠度量是否能够在跨多机构数据中可靠地为放疗计划分配解剖区域标签？
• RQ2 相对于专家标注基准，自动区域标签的准确性如何？
• RQ3 边界区域情况如何影响标签性能和区域之间的歧义？

主要发现

Metric	Value
Exact Accuracy	91%
Top-2 Accuracy	94%
Top-1 Accuracy	95%

• 在100个连续临床计划上，该方法实现了91%的Exact Accuracy（所有专家标签及顺序均匹配）。
• 该方法实现了94%的Top-2 Accuracy（前两名专家区域匹配，无论顺序）。
• 该方法实现了95%的Top-1 Accuracy（主专家标签匹配）。
• 大多数不匹配发生在治疗区域位于解剖区域边界处，导致歧义。
• 该工作流展示了大数据放疗整理的可扩展性和鲁棒性。