Ghimire Rupesh, Moore Lance, Branco Daniela, Rash Dominique L, Mayadev Jyoti S, Ray Xenia
Department of Radiation Medicine and Applied Sciences, UC San Diego Health, La Jolla, California, USA.
J Appl Clin Med Phys. 2025 Mar;26(3):e14596. doi: 10.1002/acm2.14596. Epub 2025 Jan 27.
Daily online adaptive radiotherapy (ART) improves dose metrics for gynecological cancer patients, but the on-treatment process is resource-intensive requiring longer appointments and additional time from the entire adaptive team. To optimize resource allocation, we propose a model to identify high-priority patients.
For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard-of-care (Initial) and a reduced margin initial plan (Initial) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (Daily and Daily) were determined by re-segmenting the anatomy based on the treatment CBCT and calculating dose on a synthetic CT. These initial and daily doses were used to estimate the ART benefit ( = Daily-Daily) versus initial plan differences ( = Initial-Initial) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences ( ) of (cc), (Gy), and (Gy). Anatomy (intact uterus or post-hysterectomy), DoseType (simultaneous integrated boost [SIB] vs. single dose), and/or prescription value. To establish a logistic model, we classified the top 10% in each metric as high-benefit patients. We then built a logistic model to predict these patients from the previous predictors. Leave-one-out validation and ROC analysis were used to evaluate the accuracy. To improve the clinical efficiency of this predictive process, we also created knowledge-based plans for the ΔInitial plans ( ) and repeated the analysis.
In both and our multivariate analysis showed low R values 0.34-0.52 versus 0.14-0.38. The most significant predictor in each multivariate model was the corresponding ∆Initial metric (e.g., Bowel (V40 Gy), p < 1e-05). In the logistic model, the metrics with the strongest correlation to the high-benefit patients were (cc), (Gy), , and prescription. The models for original and knowledge-based plans had an AUC of 0.85 versus 0.78. The sensitivity and specificity were 0.92/0.72 and 0.69/0.80, respectively.
This methodology will allow clinics to prioritize patients for resource-intensive daily online ART.
每日在线自适应放疗（ART）可改善妇科癌症患者的剂量指标，但治疗过程资源密集，需要更长的预约时间以及整个自适应团队投入额外时间。为了优化资源分配，我们提出了一种识别高优先级患者的模型。
对于49例回顾性宫颈癌和子宫内膜癌患者，我们计算了两个初始计划：采用Ethos治疗计划系统进行适配的已治疗的标准治疗方案（初始方案）和缩小 margins 的初始计划（初始方案）。基于治疗CBCT对解剖结构进行重新分割，并在合成CT上计算剂量，从而确定对应标准 margins 和缩小 margins 的每日剂量（每日剂量和每日剂量）。通过多元线性回归，利用这些初始剂量和每日剂量来估计ART获益（=每日剂量 - 每日剂量）与初始计划差异（=初始方案 - 初始方案）。剂量学获益通过初始计划差异（）为（cc）、（Gy）和（Gy）进行建模。解剖结构（完整子宫或子宫切除术后）、剂量类型（同步整合加量 [SIB] 与单次剂量）和/或处方值。为建立逻辑模型，我们将每个指标中排名前10%的患者归类为高获益患者。然后，我们构建了一个逻辑模型，根据先前的预测指标来预测这些患者。采用留一法验证和ROC分析来评估准确性。为提高这种预测过程的临床效率，我们还为Δ初始计划（）创建了基于知识的计划，并重复了分析。
在和中，我们的多变量分析显示R值较低，分别为0.34 - 0.52和0.14 - 0.38。每个多变量模型中最显著的预测指标是相应的∆初始指标（例如，肠（V40 Gy），p < 1e - 05）。在逻辑模型中，与高获益患者相关性最强的指标是（cc）、（Gy）、和处方。原始计划和基于知识的计划的模型AUC分别为0.85和0.78。敏感性和特异性分别为0.92/0.72和0.69/0.80。
这种方法将使临床能够对资源密集型的每日在线ART患者进行优先级排序。
