一种用于宫颈癌容积调强弧形放疗的新型剂量体积直方图评分算法的自动个体化质量保证。

An automatic patient-specific quality assurance with a novel DVH scoring algorithm for volumetric modulated arc therapy of cervical cancer.

作者信息

Zhang Ji, Ning Boda, Jin Xiaotong, Shen Yanting, Mu Yiran, Yi Jinling, Han Ce, Zhou Yongqiang, Bai Yanling, Jin Xiance

机构信息

Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.

Department of Radiation Physics, Harbin Medical University Cancer Hospital, Harbin, 150081, China.

出版信息

Appl Radiat Isot. 2025 Nov;225:112030. doi: 10.1016/j.apradiso.2025.112030. Epub 2025 Jul 4.

DOI:10.1016/j.apradiso.2025.112030

PMID:40628009

Abstract

PURPOSE

To develop a novel DVH scoring algorithm to predict and classify patient-specific quality assurance (PSQA) results by different DVH metrics automatically and efficiently.

METHODS

A total of 200 cervical cancer patients who treated by Infinity (109 cases) and Synergy (91 cases) linear accelerators underwent volumetric modulated arc therapy (VMAT) from 2019 to 2022 were enrolled and used as technical testing (TT) and technical validation (TV) datasets, respectively, which was then randomly divided into training, validation and testing set at a ratio of 7:1:2. PSQA dose distributions were predicted using U-shape-like network with skip-connection modules (called T-Net) with the input of CT and plan dose distributions. A novel weight-based DVH scoring (WDS) algorithm was developed and trained to classify "pass" or "fail" (PoF) of PSQA results based on the dose errors (DEs) and volumetric errors (VEs) calculated between predicted and planned DVHs.

RESULTS

T-Net achieved a best performance in predicting PSQA dose distributions in comparison with other deep learning models. The WDS method achieved a sensitivity, specificity and accuracy of 100.00 %, 50.00 %, 0.955, and 100.00 %,33.33 %, 0.890 in TT and TV, respectively, which was better than models of random forest (RF) and support vector machines (SVM) with an accuracy of 0.909, 0.833 and 0.864, 0.722 in TT and TV, respectively. The threshold DVH score for 22 and 18 validation patients were 49.62 and 57.62 in the TT and TV with a precision, recall rate and F1 score of 0.952, 1, 0.976 and 0.882, 1, 0.938, respectively.

CONCLUSIONS

The suggested novel WDS algorithm can improve the accuracy and efficiency of classifying the PoF of PSQA objectively and automatically.

摘要

目的

开发一种新型剂量体积直方图（DVH）评分算法，以通过不同的DVH指标自动、高效地预测和分类患者特异性质量保证（PSQA）结果。

方法

共有200例接受容积调强弧形放疗（VMAT）的宫颈癌患者，其中109例由Infinity直线加速器治疗，91例由Synergy直线加速器治疗，这些患者在2019年至2022年期间接受治疗，分别用作技术测试（TT）和技术验证（TV）数据集，然后以7:1:2的比例随机分为训练集、验证集和测试集。使用具有跳跃连接模块的类U形网络（称为T-Net），以CT和计划剂量分布作为输入来预测PSQA剂量分布。开发并训练了一种新型的基于权重的DVH评分（WDS）算法，以根据预测的和计划的DVH之间计算出的剂量误差（DE）和体积误差（VE）对PSQA结果的“通过”或“失败”（PoF）进行分类。

结果

与其他深度学习模型相比，T-Net在预测PSQA剂量分布方面表现最佳。WDS方法在TT和TV中的灵敏度、特异性和准确率分别为100.00%、50.00%、0.955以及100.00%、33.33%、0.890，优于随机森林（RF）和支持向量机（SVM）模型，RF和SVM在TT和TV中的准确率分别为0.909、0.833以及0.864、0.722。在TT和TV中，22例和18例验证患者的阈值DVH分数分别为49.62和57.62，精确率、召回率和F1分数分别为0.952、1、0.976以及0.882、1、0.938。