扇形束CT引导的在线自适应放疗在宫颈癌中的临床实践
Clinical practice of fan beam CT-guided online adaptive radiotherapy for cervical cancer
马辰莺 1郭建 1蒋华 1李治斌 1王世梅 2李秉桓 2贾乐成 3曹然 2章卫 2徐晓婷 1周菊英1
作者信息
- 1. 苏州大学附属第一医院放疗科,苏州 215006
- 2. 上海联影医疗科技股份有限公司,上海 200232
- 3. 深圳市联影高端医疗装备创新研究院,深圳 518125
- 折叠
摘要
目的 探讨人工智能(AI)辅助联合低剂量扇形束计算机断层扫描(FBCT)引导的在线适应性放疗(OART)治疗宫颈癌的可行性及安全性.方法 在联影uCT-ART平台予11名宫颈癌(10名术后辅助,1名根治性放疗)患者行高年资放疗医师主导触发的OART.分析AI辅助低剂量FBCT引导的OART治疗宫颈癌的可行性,包括评估自动分割轮廓质量、自动放射治疗计划、OART在线剂量学分析、OART流程时长;以及11名宫颈癌患者的放疗相关不良反应分析.结果 在297个分次治疗中,经高年资放疗医师判断,共启动81次OART,人均启动OART 7.4次.OART流程平均总时长为18.97 min OART调整平均时长为15.87 min.11例患者在定位CT上经AI辅助勾画工具自动分割感兴趣区域(ROI),得到ROIauto经高年资放疗医师修改及审核后得到ROIedit,其中临床靶区(CTV)的Dice相似系数为0.85±0.04,不劣于前期模型0.89± 0.02(P＞0.05),95％豪斯多夫距离为(5.64±1.60)mm,优于前期模型构建的(6.28± 2.31)mm(t=-2.34,P＜0.05).OART启动后轮廓勾画策略为优先采用CTV刚性拷贝/OAR自动分割+高年资放疗医师修改.OART计划的计划靶区(PTV)剂量分布更为紧凑,且剂量整体更接近处方剂量;OART计划中OAR剂量控制更贴近临床要求,OAR受照剂量显著低于影像引导放疗(IGRT)计划;适形指数CI与均匀性指数HI优于手动计划.在OART过程中,分次间靶区体积变化范围主要集中在±5％的范围内,而OAR体积变化范围波动较大,且无明显规律.消化系统、泌尿系统、造血系统发生急性不良反应均为PRO-CTCAE 1～2级,未见3级及以上的不良反应发生.结论 本研究描述了基于uCT-ART的OART系统在宫颈癌放疗的成功实施,证实了OART临床应用的可行性与安全性.
Abstract
Objective To explore the feasibility and safety of artificial intelligence(AI)-assisted,low-dose fan beam CT(FBCT)-guided online adaptive radiotherapy(OART)for cervical cancer.Methods A total of 11 cervical cancer patients(10 treated with postoperative adjuvant therapy,and 1 with radical radiotherapy)underwent OART led by senior radiation oncologists(RO)on a uCT-ART platform.The feasibility of AI-assisted,low-dose FBCT-guided OART in the treatment of cervical cancer was analyzed,including the assessment of automatic contouring quality,automatic radiotherapy planning,OART online dosimetry analysis,and OART durations,as well as the analysis of radiotherapy-associated adverse reactions in the 11 cervical cancer patients.Results According to the senior ROs,OART was initiated 81 times in 297 fractions,with an average of 7.4.The average total OART duration was 18.97 min,and the average OART adjustment duration was 15.87 min.The regions of interest(ROIs)of 11 patients were automatically segmented based on CT images for positioning using the AI-assisted contouring tool.Then,the obtained ROIauto were modified and audited by ROs,yielding ROIedit.The dice similarity coefficient of the clinical target volumes(CTVs)was 0.85±0.04,which was not inferior to that derived from previous models(0.89±0.02;P＞0.05).The 95％Hausdorff distance was(5.64± 1.60)mm,which was better than that constructed using previous models[(6.28±2.31)mm;t=-2.34,P＜0.05].After the initiation of OART,a contouring strategy involving CTV rigid copy/automatic segmentation of organs at risk(OARs)+RO modification was preferred.In the OART plans,doses to the planning target volumes(PTVs)were more compact and closer to the prescribed doses overall.The dose control of OARs in the OART plans was more consistent with the clinical requirements,with the radiation doses to OARs significantly lower than those of image-guided radiation therapy plans.The conformity index and homogeneity index of the OART plans exhibited superiority over those of manual plans.In the course of OART,the target volumes between fractions fluctuated within±5％,while the OAR volumes changed greatly without following evident laws.Regarding adverse reactions,the acute adverse reactions in the digestive,urinary,and hematopoietic systems were all of grades 1 to 2 according to the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events,with no adverse reactions of grades 3 or above occurring.Conclusions This study presents the successful implementation of the uCT-ART-based OART system in radiotherapy for cervical cancer,confirming the feasibility and safety of the clinical applications of OART.
关键词
宫颈癌/在线自适应放疗/扇形束CT/人工智能/图像引导放疗Key words
Cervical cancer/Online adaptive radiotherapy/Fan beam CT/Artificial intelligence/Image-guided radiotherapy引用本文复制引用
基金项目
国家自然科学基金(81602792)
江苏省高等学校基础科学(自然科学)研究面上项目(23KJB310023)
江苏省妇幼保健科研项目(F202210)
江苏省医学重点学科建设项目(ZDXK202235)
放射医学与辐射防护国家重点实验室项目(GZK1202101)
苏州市科技计划(SLT201920)
苏州市科技发展项目(KJXW2020008)
苏州大学附属第一医院自然科学研究博习培育计划(BXQN202107)
中关村精准医学基金会医健公益行项目(XS-ZGC-0012)
出版年
2024
NETL
NSTL
万方数据