van der Pol Luuk H G, Pomp Jacquelien, Mohamed Hoesein Firdaus A A, Raaymakers Bas W, Verhoeff Joost J C, Fast Martin F
Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands.
Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands.
Phys Imaging Radiat Oncol. 2024 Dec 1;32:100686. doi: 10.1016/j.phro.2024.100686. eCollection 2024 Oct.
BACKGROUND/PURPOSE: Radiation-induced cardiac toxicity in lung cancer patients has received increased attention since RTOG 0617. However, large cohort studies with accurate cardiac substructure (CS) contours are lacking, limiting our understanding of the potential influence of individual CSs. Here, we analyse the correlation between CS dose and overall survival (OS) while accounting for deep learning (DL) contouring uncertainty, uncertainty and different modelling approaches.
MATERIALS/METHODS: This single institution, retrospective cohort study includes 730 patients (early-stage tumours (I or II). All treated: 2009-2019), who received stereotactic body radiotherapy (≥ 5 Gy per fraction). A DL model was trained on 70 manually contoured patients to create 12 cardio-vascular structures. Structures with median dice score above 0.8 and mean surface distance (MSD) <2 mm during testing, were further analysed. Patientspecific CS dose was used to find the correlation between CS dose and OS with elastic net and random survival forest models (with and without confounding clinical factors). The influence of delineation-induced dose uncertainty on OS was investigated by expanding/contracting the DL-created contours using the MSD ± 2 standard deviations.
Eight CS contours met the required performance level. The left atrium (LA) mean dose was significant for OS and an LA mean dose of 3.3 Gy (in EQD2) was found as a significant dose stratum.
Explicitly accounting for input parameter uncertainty in lung cancer survival modelling was crucial in robustly identifying critical CS dose parameters. Using this robust methodology, LA mean dose was revealed as the most influential CS dose parameter.
背景/目的：自RTOG 0617研究以来，肺癌患者的放射性心脏毒性受到了越来越多的关注。然而，缺乏具有精确心脏亚结构（CS）轮廓的大型队列研究，这限制了我们对各个CS潜在影响的理解。在此，我们分析CS剂量与总生存期（OS）之间的相关性，同时考虑深度学习（DL）轮廓勾画的不确定性、不确定性和不同的建模方法。
材料/方法：这项单机构回顾性队列研究纳入了730例患者（早期肿瘤（I期或II期）。所有患者均接受治疗：2009 - 2019年），他们接受了立体定向体部放疗（每次分割剂量≥5 Gy）。在70例手动勾画轮廓的患者上训练一个DL模型，以创建12个心血管结构。对测试期间中位骰子分数高于0.8且平均表面距离（MSD）<2 mm的结构进行进一步分析。使用患者特异性CS剂量，通过弹性网和随机生存森林模型（有和没有混杂临床因素）来寻找CS剂量与OS之间的相关性。通过使用MSD±2个标准差扩展/收缩DL创建的轮廓，研究轮廓勾画引起的剂量不确定性对OS的影响。
八个CS轮廓达到了所需的性能水平。左心房（LA）平均剂量对OS有显著影响，发现LA平均剂量为3.3 Gy（等效剂量2）是一个显著的剂量分层。
在肺癌生存模型中明确考虑输入参数的不确定性对于稳健地识别关键CS剂量参数至关重要。使用这种稳健的方法，LA平均剂量被揭示为最有影响力的CS剂量参数。
