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基于低剂量计算机断层扫描的宫颈癌高效计划池自适应放射治疗技术可行性研究

Feasibility Study of an Efficient Plan Pool Adaptive Radiotherapy Technology Based on Low-dose Computed Tomography for Cervical Cancer.

作者信息

Sun F, Xu Y, Xu X, Gong W, Mo Z, Jia L, Qin S, Gan G

机构信息

Department of Radiation Oncology, First Affiliated Hospital of Soochow University, Suzhou, 215000, China.

Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, 518048, China.

出版信息

Clin Oncol (R Coll Radiol). 2025 Sep;45:103903. doi: 10.1016/j.clon.2025.103903. Epub 2025 Jul 9.

Abstract

BACKGROUND

Online adaptive radiotherapy (oART) involves a complex workflow across multiple departments, requiring significant resources and increasing the workload of radiation oncologists (ROs) and physicists. For cervical cancer, there is a need for a low-dose, image-guided adaptive radiotherapy solution that is both efficient and clinically effective AIMS: The aim is to explore the feasibility and performance of a plan-pool adaptive radiotherapy (plan-pool ART) workflow, with a focus on efficiency and dosimetric benefits for both the tumour and organs at risk (OARs).

MATERIALS AND METHODS

A plan-pool ART framework was developed for cervical cancer radiotherapy based on the daily low-dose computed tomography (LDCT). The LDCT images were synthesised into high-quality restorative CT (RCT) images by an image-synthesis model. A total of 257 fractionated fan-beam computed tomography (FBCT) datasets from 17 cervical cancer patients treated with the oART regimen were collected (171 fractions treated with oART and 86 fractions treated with the original plan). A support vector machine (SVM) was used to train (180 cases) and evaluate (77 cases) the oART classification model, which predicts whether the fraction needs to execute oART. The oART classification model selects the daily treatment plan that best aligns with the patient's anatomical positions from the plan pool. Finally, the performance of image-guided radiotherapy (IGRT), plan-pool ART, and triggered oART (trigger-oART) techniques was compared by simulating treatments for 5 cervical cancer cases.

RESULTS

The oART classification model achieved high predictive performance, with an under the curve (AUC) of 0.98, accuracy of 0.86, recall of 0.89, and specificity of 0.92. Plan-pool ART reduced the number of oART execution (1.4 vs 3.0 for trigger-oART) while optimising dosimetry. Compared to IGRT, plan-pool ART decreased mean bladder dose (3122cGy vs 3258cGy) and rectum dose (3265cGy vs 3325cGy), along with lower V values for both organs. Target coverage remained comparable across techniques, but IGRT showed greater variability in CTV D, leading to potential underdosing.

CONCLUSION

The simulation results demonstrate that the plan-pool ART technology is feasible, ensuring reliable target dose coverage, reducing the dose to OARs, and lowering the number of oART implementation. This approach offers a promising new technical solution for clinical treatment.

摘要

背景

在线自适应放射治疗(oART)涉及多个部门的复杂工作流程,需要大量资源,并增加了放射肿瘤学家(RO)和物理学家的工作量。对于宫颈癌,需要一种低剂量、图像引导的自适应放射治疗解决方案,该方案既要高效又要具有临床有效性。目的:旨在探索计划池自适应放射治疗(计划池ART)工作流程的可行性和性能,重点关注肿瘤和危及器官(OAR)的效率和剂量学益处。

材料与方法

基于每日低剂量计算机断层扫描(LDCT)开发了一种用于宫颈癌放射治疗的计划池ART框架。通过图像合成模型将LDCT图像合成为高质量的恢复性CT(RCT)图像。收集了17例接受oART方案治疗的宫颈癌患者的257个分次扇形束计算机断层扫描(FBCT)数据集(171个分次接受oART治疗,86个分次接受原计划治疗)。使用支持向量机(SVM)训练(180例)并评估(77例)oART分类模型,该模型预测该分次是否需要执行oART。oART分类模型从计划池中选择与患者解剖位置最匹配的每日治疗计划。最后,通过模拟5例宫颈癌病例的治疗,比较了图像引导放射治疗(IGRT)、计划池ART和触发式oART(触发-oART)技术的性能。

结果

oART分类模型具有较高的预测性能,曲线下面积(AUC)为0.98,准确率为0.86,召回率为0.89,特异性为0.92。计划池ART减少了oART的执行次数(触发-oART为1.4次,而触发-oART为3.0次),同时优化了剂量学。与IGRT相比,计划池ART降低了膀胱平均剂量(3122cGy对3258cGy)和直肠剂量(3265cGy对3325cGy),两个器官的V值也较低。各技术的靶区覆盖情况相当,但IGRT在CTV D方面表现出更大的变异性,导致可能的剂量不足。

结论

模拟结果表明,计划池ART技术是可行的,可确保可靠的靶区剂量覆盖,降低对OAR的剂量,并减少oART的实施次数。这种方法为临床治疗提供了一种有前景的新技术解决方案。

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