Welsh Ceilidh, Harrison Karl, Lightowlers Sara, Gleeson Ian, Beard Alfred J W, Harris Emma, Barnett Gillian C, Jena Rajesh
Department of Oncology, University of Cambridge, Cambridge, Cambridge Biomedical Campus, CB2 0AH, United Kingdom.
Cavendish Laboratory, University of Cambridge, Cambridge, CB3 0HE, United Kingdom.
BJR Open. 2025 May 3;7(1):tzaf007. doi: 10.1093/bjro/tzaf007. eCollection 2025 Jan.
This work describes a unified workflow for classifying patterns of locoregional recurrence (LRR) using radiotherapy planning dose distributions. This approach aims to incorporate dose parameters into LRR classifications and facilitate application across different treatment sites and dose prescriptions to standardise classification terminology.
The relapse diagnostic CT (rCT) and manually delineated relapse gross tumour volume (rGTV) were co-registered with the radiotherapy planning CT (pCT) using deformable image registration (DIR). The DIR accuracy was quantified using the target registration error (TRE) using the absolute centroid distance between cancer site-specific regions of interest (ROIs). Dosimetric structures were delineated for planning regions receiving 95% of the dose prescribed to high-risk, intermediate-risk, and low-risk CTVs, relative to the cancer site or trial. The mapped rGTV was compared relative to each dose structure and classified into one of five categories: central and peripheral high-dose (Type A, Type B), central and peripheral elective-dose (Type C, Type D), and extraneous dose (Type E) failures.
The unified workflow was successfully implemented on two different patient use cases, one from the IMPORT HIGH breast cancer trial, one from the VoxTox head-and-neck study, classifying LRR as Type A and Type E failures, respectively.
This workflow for classifying LRR is applicable across different cancer sites, despite differences in treatment protocol, target dose, and dose delivery. This provides a basis for utilising radiotherapy dose distributions to analyse patterns of failure irrespective of trial design or cancer-site.
Standardised classifications of LRR that are correlated with the planning dose distribution could provide insight into the underlying causes of LRR burden post-radiotherapy and allow for critical evaluation of the current concepts of defined clinical tumour volumes and optimal PTV dose regions.
本研究描述了一种使用放射治疗计划剂量分布对局部区域复发（LRR）模式进行分类的统一工作流程。该方法旨在将剂量参数纳入LRR分类，并促进其在不同治疗部位和剂量处方中的应用，以标准化分类术语。
使用可变形图像配准（DIR）将复发诊断CT（rCT）和手动勾画的复发大体肿瘤体积（rGTV）与放射治疗计划CT（pCT）进行配准。使用目标配准误差（TRE），通过癌症部位特异性感兴趣区域（ROI）之间的绝对质心距离来量化DIR准确性。相对于癌症部位或试验，为接受规定给高危、中危和低危临床靶体积（CTV）95%剂量的计划区域勾画剂量学结构。将映射的rGTV相对于每个剂量结构进行比较，并分为五类之一：中央和外周高剂量（A型、B型）、中央和外周选择性剂量（C型、D型）以及额外剂量（E型）失败。
该统一工作流程在两个不同的患者用例上成功实施，一个来自IMPORT HIGH乳腺癌试验，一个来自VoxTox头颈研究，分别将LRR分类为A型和E型失败。
尽管治疗方案、靶剂量和剂量递送存在差异，但这种LRR分类工作流程适用于不同的癌症部位。这为利用放射治疗剂量分布分析失败模式提供了基础，而无需考虑试验设计或癌症部位。
与计划剂量分布相关的LRR标准化分类可以深入了解放疗后LRR负担的潜在原因，并允许对定义的临床肿瘤体积和最佳计划靶体积（PTV）剂量区域的当前概念进行批判性评估。
