Suppr超能文献

MO-D-BRB-10:调强放疗计划中危及器官 sparing 的患者间差异建模:基于证据的计划质量评估

MO-D-BRB-10: Modeling Inter-Patient Variation of Organ-At-Risk Sparing in IMRT Plans: An Evidence-Based Plan Quality Evaluation.

作者信息

Yuan L, Ge Y, Li T, Yin F, Wu Q Jackie

机构信息

Duke University Medical Center, Durham, NC.

Wake Forest University Health Sciences, Winston-Salem, NC.

出版信息

Med Phys. 2012 Jun;39(6Part21):3868. doi: 10.1118/1.4735791.

Abstract

PURPOSE

To develop a predictive model to assess the quality of critical organ dose sparing in IMRT plans by providing patient specific dose sparing references, based on an array of patient anatomical features and prior planning experience.

METHODS

Contributions of various patient anatomical features to the inter-patient OAR dose sparing variation in IMRT planning were systematically studied using machine learning method based on high quality prior plans. The dependence of anatomical factor on OAR dosimetric parameters is formulated into predictive models. The OAR dosimetric parameters generated by these predictive models represent the "best feasible" clinical outcomes based on past planning experiences. IMRT plans of 88 prostate, 106 head-and-neck (HN) and 21 spine SBRT treatments were used to train the models. The final models were tested by additional 24 prostate and 48 HN plans. The model for spine SBRT was tested by the leave-one-out method.

RESULTS

For HN and prostate planning, the significant patient anatomical features that affect OAR sparing are: the distance between OAR and PTV, the portion of OAR volume within an OAR specific distance range, the overlap volume between OAR and PTV, and the portion of OAR volume outside the primary treatment field. For spine SBRT planning, the most significant patient anatomical feature that affects cord sparing is the tightness of the geometric enclosure of PTV surrounding the cord and the homogeneity of PTV dose coverage. The dosimetric parameters predicted for the test patient cases using the models were in agreement with those from the clinical plans in more than 75% of the cases.

CONCLUSIONS

The developed predictive models indicated substantial correlation between some important patient anatomical features and OAR dose sparing based on expert experiences. These models can be used as effective tools for evaluating the quality of treatment plans customized to individual patient's anatomy. Partially supported by a master research agreement with Varian Medical System, Inc.

摘要

目的

通过基于一系列患者解剖特征和既往规划经验提供患者特异性剂量 sparing 参考,开发一种预测模型,以评估调强放疗(IMRT)计划中关键器官剂量 sparing 的质量。

方法

基于高质量的既往计划,使用机器学习方法系统研究了各种患者解剖特征对 IMRT 计划中患者间危及器官(OAR)剂量 sparing 变化的贡献。将解剖因素对 OAR 剂量学参数的依赖性纳入预测模型。这些预测模型生成的 OAR 剂量学参数代表基于过去规划经验的“最佳可行”临床结果。使用 88 例前列腺、106 例头颈部(HN)和 21 例脊柱立体定向放射治疗(SBRT)的 IMRT 计划来训练模型。最终模型通过另外 24 例前列腺和 48 例 HN 计划进行测试。脊柱 SBRT 模型采用留一法进行测试。

结果

对于 HN 和前列腺规划,影响 OAR sparing 的重要患者解剖特征包括:OAR 与计划靶体积(PTV)之间的距离、OAR 特定距离范围内 OAR 体积的比例、OAR 与 PTV 之间的重叠体积以及主要治疗野之外 OAR 体积的比例。对于脊柱 SBRT 规划,影响脊髓 sparing 的最重要患者解剖特征是 PTV 围绕脊髓的几何包绕紧密程度以及 PTV 剂量覆盖的均匀性。使用模型为测试患者病例预测的剂量学参数在超过 75%的病例中与临床计划的参数一致。

结论

所开发的预测模型表明,基于专家经验,一些重要的患者解剖特征与 OAR 剂量 sparing 之间存在显著相关性。这些模型可作为评估针对个体患者解剖结构定制的治疗计划质量的有效工具。部分得到与瓦里安医疗系统公司的主研究协议支持。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验