University of California San Diego Health, 3855 Health Sciences Drive La Jolla, CA 92093, United States of America.
Biomed Phys Eng Express. 2023 Jun 30;9(4). doi: 10.1088/2057-1976/acdf62.
. Adaptive Radiotherapy (ART) is an emerging technique for treating cancer patients which facilitates higher delivery accuracy and has the potential to reduce toxicity. However, ART is also resource-intensive, Requiring extra human and machine time compared to standard treatment methods. In this analysis, we sought to predict the subset of node-negative cervical cancer patients with the greatest benefit from ART, so resources might be properly allocated to the highest-yield patients.. CT images, initial plan data, and on-treatment Cone-Beam CT (CBCT) images for 20 retrospective cervical cancer patients were used to simulate doses from daily non-adaptive and adaptive techniques. We evaluated the coefficient of determination (R) between dose and volume metrics from initial treatment plans and the dosimetric benefits to theBowelV40Gy,BowelV45Gy,BladderDmean,andRectumDmeanfrom adaptive radiotherapy using reduced 3 mm or 5 mm CTV-to-PTV margins. The LASSO technique was used to identify the most predictive metrics forBowelV40Gy.The three highest performing metrics were used to build multivariate models with leave-one-out validation forBowelV40Gy.. Patients with higher initial bowel doses were correlated with the largest decreases in BowelV40Gyfrom daily adaptation (linear best fit R= 0.77 for a 3 mm PTV margin and R= 0.8 for a 5 mm PTV margin). Other metrics had intermediate or no correlation. Selected covariates for the multivariate model were differences in the initialBowelV40GyandBladderDmeanusing standard versus reduced margins and the initial bladder volume. Leave-one-out validation had an Rof 0.66 between predicted and true adaptiveBowelV40Gybenefits for both margins.. The resulting models could be used to prospectively triage cervical cancer patients on or off daily adaptation to optimally manage clinical resources. Additionally, this work presents a critical foundation for predicting benefits from daily adaptation that can be extended to other patient cohorts.
. 自适应放疗（ART）是一种新兴的癌症治疗技术，它可以提高治疗的准确性，并有可能降低毒性。然而，ART 也需要更多的人力和机器资源，与标准治疗方法相比，需要额外的时间。在这项分析中，我们试图预测那些从 ART 中获益最大的淋巴结阴性宫颈癌患者亚组，以便将资源合理分配给获益最大的患者。.. 我们使用 20 名回顾性宫颈癌患者的 CT 图像、初始计划数据和治疗中的锥形束 CT（CBCT）图像，模拟每日自适应和非自适应技术的剂量。我们评估了初始治疗计划中剂量与体积指标之间的决定系数（R），以及使用减少 3mm 或 5mm CTV 至 PTV 边界的自适应放疗对 BowelV40Gy、BowelV45Gy、BladderDmean 和 RectumDmean 的剂量学益处。我们使用 LASSO 技术来确定 BowelV40Gy 最具预测性的指标。使用这三个表现最好的指标，结合 Leave-one-out 验证，建立 BowelV40Gy 的多元模型。.. 初始肠道剂量较高的患者与 BowelV40Gy 从每日适应治疗中最大降低呈线性相关（3mm PTV 边界时的线性最佳拟合 R=0.77，5mm PTV 边界时 R=0.8）。其他指标的相关性中等或没有相关性。多元模型的选择变量是标准与减少边界的初始 BowelV40Gy 和 BladderDmean 的差异，以及初始膀胱体积。两种边界的 Leave-one-out 验证的 R 值为 0.66，预测与真实自适应 BowelV40Gy 获益之间的相关性。.. 由此产生的模型可用于前瞻性地对接受或不接受每日适应治疗的宫颈癌患者进行分类，以优化临床资源的管理。此外，这项工作为预测每日适应治疗的益处提供了一个重要的基础，该基础可以扩展到其他患者群体。
