Rectal and bladder dose prediction in volumetric modulated arc therapy for cervical cancer based on dose-volume histogram

Ping ZHENG; Dong WANG

doi:10.11889/j.1000-3436.2022-0039

[1] M H Guo, J Xu, J Y Du. Trends in cervical cancer mortality in China from 1989 to 2018: an age-period-cohort study and Joinpoint analysis. BMC Public Health, 21, 1329(2021).

[2] U Yoshihiro, O Shingo, I Masaru et al. Strategies for reducing ovarian dose in volumetric modulated arc therapy (VMAT) for postoperative uterine cervical cancer. The British Journal of Radiology, 91, 20160777(2018).

[3] MAI Yanhua. Automatic multi-objective optimization for intensity modulated radiation therapy(2018).

[4] Minmin QIU, Yongjin DENG, Jiajian ZHONG et al. Lung dose prediction model in coplanar intensity-modulated radiotherapy for thoracic esophageal carcinoma. Chinese Journal of Medical Physics, 37, 1248-1254(2020).

[5] Jiacheng LIU, Hanlin WANG, Qingying WANG et al. Fully automatic volumetric modulated arc therapy planning based on dose prediction combined with an iterative optimization algorithm. Chinese Journal of Radiological Medicine and Protection, 830-835(2021).

[6] K L Moore, L M Appenzoller, J Tan et al. Clinical implementation of dose-volume histogram predictions for organs-at-risk in IMRT planning. Journal of Physics: Conference Series, 489, 012055(2014).

[7] Ping LIU. To investigate the changes of volume and spatial location in target and organs at risk during intensity-modulated radiotherapy for cervical cancer(2015).

[8] Haowen PANG, Xiaoyang SUN, Bo YANG et al. Relationship between the overlap volume of organ at risk and target and the dose to organ at risk in intensity-modulated radiotherapy. Chinese Journal of Radiation Oncology, 26, 187-191(2017).

[9] M J E van den Akker, N Horeweg, J J Beltman et al. Efficacy and toxicity of postoperative external beam radiotherapy or chemoradiation for early-stage cervical cancer. International Journal of Gynecological Cancer, 30, 1878-1886(2020).

[10] Feihu CHEN, Yijiang LI, Han BAI et al. Effects of dose calculation grid size on physical dose and biological dose in radiotherapy for cervical cancer. Chinese Journal of Medical Physics, 37, 971-976(2020).

[11] X F Zhu, Y R Ge, T R Li et al. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Medical Physics, 38, 719-726(2011).

[12] X Bai, B B Wang, S Y Wang et al. Radiotherapy dose distribution prediction for breast cancer using deformable image registration. Biomedical Engineering Online, 19, 39(2020).

[13] Qiang HE, Xuetao WANG, Xin LI et al. Prediction of rectal toxicity of radiotherapy for prostate cancer based on multi-modality feature and multi-classifiers. Journal of Southern Medical University, 39, 972-979(2019).

[14] D Christophides, A Gilbert, A L Appelt et al. OC-0255: practical use of principal component analysis in radiotherapy planning. Radiotherapy and Oncology, 123, S129-S130(2017).

[15] C M Lutz, A Tröller, D S Møller et al. PO-0855: model-free investigation of the dose-volume-response of radiation pneumonitis by principal component analysis. Radiotherapy and Oncology, 111, S83(2014).

[16] Z Q Liu, X Y Chen, K Men et al. A deep learning model to predict dose-volume histograms of organs at risk in radiotherapy treatment plans. Medical Physics, 47, 5467-5481(2020).

[17] Y Song, J J Hu, Y Liu et al. Dose prediction using a deep neural network for accelerated planning of rectal cancer radiotherapy. Radiotherapy and Oncology, 149, 111-116(2020).

[18] H W Pang, X Y Sun, B Yang et al. Predicting the dose absorbed by organs at risk during intensity modulated radiation therapy for nasopharyngeal carcinoma. The British Journal of Radiology, 91, 20170289(2018).

[19] Yifeng YANG, Weiwei WANG, Ping LI et al. Prediction model of local effect model-based rectal dose volume histogram in prostate cancer patients treated with carbon ion radiotherapy. Chinese Journal of Radiation Oncology, 30, 1041-1046(2021).