Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Radiation Oncology
Published in: Radiation Oncology 1/2022

Open Access 01-12-2022 | Cervical Cancer | Research

Dose prediction for cervical cancer VMAT patients with a full-scale 3D-cGAN-based model and the comparison of different input data on the prediction results

Authors: Gongsen Zhang, Zejun Jiang, Jian Zhu, Linlin Wang

Published in: Radiation Oncology | Issue 1/2022

Login to get access

Excerpt

Cervical cancer is the second most common female malignant tumor in the world [1], for which radiotherapy is currently one of the main treatment methods. Related surveys show that approximately 80% of cervical cancer patients receive radiotherapy at different stages [2, 3]. Intensity modulated radiotherapy (IMRT) and volumetric modulated arc radiotherapy (VMAT) have become standard radiotherapy methods. Compared with 3-dimensional conformal radiotherapy (3D-CRT), the dose distribution formed by new technologies above using reverse optimization algorithms is highly consistent with the planned target area and has better uniformity[4‐7]. However, advanced technology also brings corresponding computational burden, which greatly increases the total planning time. According to statistics, it takes an average of approximately 4 h for radiotherapists to delineate the planning target volume (PTV) and organs at risk (OARs), and may even take longer for some complex diseases. After that, a radiotherapy plan meeting the treatment standards is formulated by radiation physicists, which takes approximately 10 h for each patient [8, 9]. The large amount of time required for the treatment plan inevitably leads to delayed treatment, thereby affecting the quality of treatment and prognosis of patients [10]. …
Literature
1.
Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359-86.CrossRefPubMed
2.
Small W, Jr, Mell LK, Anderson P, et al. Consensus guidelines for delineation of clinical target volume for intensity-modulated pelvic radiotherapy in postoperative treatment of endometrial and cervical cancer. Int J Radiat Oncol Biol Phys. 2008;71:428–34.PubMedCrossRef
3.
Grigsby PW. Radiotherapy for pelvic recurrence after radical hysterectomy for cervical cancer. Radiat Med. 2005;23:327–30.PubMed
4.
Cibula D, Pötter R, Planchamp F, et al. The European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology guidelines for the management of patients with cervical cancer. Virchows Arch. 2018;472(6):919–36.PubMedCrossRef
5.
Wang W, Zhang F, Hu K, et al. Image-guided, intensity-modulated radiation therapy in definitive radiotherapy for 1433 patients with cervical cancer. Gynecol Oncol. 2018;151(3):444–8.PubMedCrossRef
6.
Lin Y, Chen K, Lu Z, et al. Intensity-modulated radiation therapy for definitive treatment of cervical cancer: a meta-analysis. Radiat Oncol. 2018;13(1):1–8.CrossRef
7.
Chen VE, Gillespie EF, Manger RP, et al. The impact of daily bladder filling on small bowel dose for intensity modulated radiation therapy for cervical cancer. Med Dosim. 2019;44(2):102–6.PubMedCrossRef
8.
Rehman J, Zahra, Ahmad N, et al. Intensity modulated radiation therapy: a review of current practice and future outlooks. J Radiat Res Appl Sci. 2018;11(4):361–7.CrossRef
9.
Boon IS, Au Yong T, Boon CS. Assessing the role of artificial intelligence (AI) in clinical oncology: utility of machine learning in radiotherapy target volume delineation. Medicines. 2018;5(4):131.PubMedCentralCrossRef
10.
Delahaut G, Témam S, Ambroise J, et al. Prognostic impact of tumor growth velocity in head and neck squamous cell carcinoma treated by radiotherapy: a pilot study. Head Neck. 2019;41(9):3041–8.PubMedCrossRef
11.
12.
Wada Y, Monzen H, Tamura M, et al. Dosimetric evaluation of simplified knowledge-based plan with an extensive stepping validation approach in volumetric-modulated arc therapy-stereotactic body radiotherapy for lung cancer. J Med Phys. 2021;46(1):7.PubMedPubMedCentral
13.
Cagni E, Botti A, Chendi A, et al. Use of knowledge based DVH predictions to enhance automated re-planning strategies in head and neck adaptive radiotherapy. Phys Med Biol. 2021;66:135004.CrossRef
14.
Jeong C, Park JW, Kwak J, et al. Volumetric-modulated arc radiotherapy using knowledge-based planning: application to spine stereotactic body radiotherapy. Prog Med Phys. 2019;30(4):94–103.CrossRef
15.
Bossart E, Duffy M, Simpson G, et al. Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy. J Appl Clin Med Phys. 2018;19(6):209–16.PubMedPubMedCentralCrossRef
16.
Visak J, McGarry RC, Randall ME, et al. Development and clinical validation of a robust knowledge-based planning model for stereotactic body radiotherapy treatment of centrally located lung tumors. J Appl Clin Med Phys. 2021;22(1):146–55.PubMedCrossRef
17.
Hu J, Liu B, Xie W, et al. Quantitative comparison of knowledge-based and manual intensity modulated radiation therapy planning for nasopharyngeal carcinoma. Front Oncol. 2021;10:3028.CrossRef
18.
Ge Y, Wu QJ. Knowledge-based planning for intensity‐modulated radiation therapy: a review of data‐driven approaches. Med Phys. 2019;46(6):2760–75.PubMedCrossRef
19.
Wu A, Li Y, Qi M, et al. Robustness comparative study of dose–volume–histogram prediction models for knowledge-based radiotherapy treatment planning. J Radiat Res Appl Sci. 2020;13(1):390–7.CrossRef
20.
Kamilaris A, Prenafeta-Boldú FX. Deep learning in agriculture: a survey. Comput Electron Agric. 2018;147:70–90.CrossRef
21.
Pouyanfar S, Sadiq S, Yan Y, et al. A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surv (CSUR). 2018;51(5):1–36.CrossRef
22.
Grigorescu S, Trasnea B, Cocias T, et al. A survey of deep learning techniques for autonomous driving. J Field Robot. 2020;37(3):362–86.CrossRef
23.
Minaee S, Boykov YY, Porikli F, et al. Image segmentation using deep learning: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 2021.
24.
Li J, Sun A, Han J, et al. A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering 2020.
25.
Nguyen D, Jia X, Sher D, et al. 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture. Phys Med Biol. 2019;64(6):065020.PubMedCrossRef
26.
Ma M, Buyyounouski MK, Vasudevan V, et al. Dose distribution prediction in isodose feature cc reserving voxelization domain using deep convolutional neural network. Med Phys. 2019;46(7):2978–87.PubMedCrossRef
27.
Chen X, Men K, Li Y, et al. A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning. Med Phys. 2019;46(1):56–64.PubMedCrossRef
28.
Barragán-Montero AM, Nguyen D, Lu W, et al. Three‐dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations. Med Phys. 2019;46(8):3679–91.PubMedCrossRef
29.
Nguyen D, Long T, Jia X, et al. A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning. Sci Rep. 2019;9(1):1–10.
30.
Sahiner B, Pezeshk A, Hadjiiski LM, et al. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46(1):e1-36.PubMedCrossRef
31.
Kearney V, Chan JW, Haaf S, et al. DoseNet: a volumetric dose prediction algorithm using 3D fully-convolutional neural networks. Phys Med Biol. 2018;63(23):235022.PubMedCrossRef
32.
Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. 2014. Preprint http://​arxiv.​org/​abs/​1406.​2661.​ 
33.
Mirza M, Osindero S. Conditional generative adversarial nets. 2014. Preprint http://​arxiv.​org/​abs/​1411.​1784.​
34.
Mahmood R, Babier A, McNiven A, et al. Automated treatment planning in radiation therapy using generative adversarial networks. Proc Mac Learn Res. 2018;85:1–15.
35.
36.
Huang H, Lin L, Tong R, et al. Unet 3+: a full-scale connected unet for medical image segmentation[C]//ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020;1055–1059.
37.
Eminowicz G, Rompokos V, Stacey C, et al. The dosimetric impact of target volume delineation variation for cervical cancer radiotherapy. Radiother Oncol J Eur Soc Ther Radiol Oncol. 2016;120:493–9.CrossRef
38.
Erdi YE, Rosenzweig K, Erdi AK, et al. Radiotherapy treatment planning for patients with non-small cell lung cancer using positron emission tomography (PET). Radiother Oncol. 2002;62(1):51–60.PubMedCrossRef
39.
Small W Jr, Mell LK, Anderson P, et al. Consensus guidelines for delineation of clinical target volume for intensity-modulated pelvic radiotherapy in postoperative treatment of endometrial and cervical cancer. Int J Radiat Oncol Biol Phys. 2008;71(2):428–34.PubMedCrossRef
40.
Lim K, Small W Jr, Portelance L, et al. Consensus guidelines for delineation of clinical target volume for intensity-modulated pelvic radiotherapy for the definitive treatment of cervix cancer. Int J Radiat Oncol Biol Phys. 2011;79(2):348–55.PubMedCrossRef
41.
Small W Jr, Bosch WR, Harkenrider MM, et al. NRG Oncology/RTOG consensus guidelines for delineation of clinical target volume for intensity modulated pelvic radiation therapy in postoperative treatment of endometrial and cervical cancer: an update. Int J Radiat Oncol Biol Phys. 2021;109(1):413–24.PubMedCrossRef
42.
Tanabe Y, Ishida T, Eto H, et al. Evaluation of the correlation between prostatic displacement and rectal deformation using the Dice similarity coefficient of the rectum. Med Dosim. 2019;44:e39–43.PubMedCrossRef
43.
Liang K, Fu H, Zhou H, et al. Accurate and automatic 3D segmentation of femur and pelvis from CT images of the hip based on deep learning. J Imaging Sci Technol. 2021;65:30411–1.CrossRef
44.
Yun YJ, Ahn BC, Kavitha MS, et al. An efficient region precise thresholding and direct hough transform in femur and femoral neck segmentation using pelvis CT. IEEE Access 2020; PP(99): 1–1.
45.
Wang C, Connolly B, Lopes P, et al. Pelvis segmentation using multi-pass U-Net and iterative shape estimation[C]/ MICCAI 2018. In: 21st international conference on medical image computing & computer assisted intervention. 2019.
46.
Hirashima H, Nakamura M, Baillehache P, et al. Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT. Radiat Oncol. 2021;16(1):1–10.CrossRef
47.
48.
Hedden N, Xu H. Radiation therapy dose prediction for left-sided breast cancers using two-dimensional and three-dimensional deep learning models. Phys Med. 2021;83:101–7.PubMedCrossRef
49.
Guerreiro F, Seravalli E, Janssens GO, et al. Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours. Radiother Oncol. 2020;156:36–42.PubMedCrossRef
50.
51.
Tomohiro K, Noriyuki K, Kengo I, et al. A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients. J Radiat Res. 2019;(5):5.
52.
Liu Z, Fan J, Li M, et al. A deep learning method for prediction of three-dimensional dose distribution of helical tomotherapy. Med Phys. 2019;46(5):1972–83.PubMedCrossRef
53.
Zhou J, Peng Z, Song Y, et al. A method of using deep learning to predict three-dimensional dose distributions for intensity modulated radiotherapy of rectal cancer. J Appl Clin Med Phys. 2020;21(5):26–37.PubMedPubMedCentralCrossRef
54.
Li H, Peng X, Zeng J, et al. Explainable attention guided adversarial deep network for 3D radiotherapy dose distribution prediction. Knowl-Based Syst. 2022;241.CrossRef
Metadata
Title
Dose prediction for cervical cancer VMAT patients with a full-scale 3D-cGAN-based model and the comparison of different input data on the prediction results
Authors
Gongsen Zhang
Zejun Jiang
Jian Zhu
Linlin Wang
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Radiation Oncology / Issue 1/2022
Electronic ISSN: 1748-717X
DOI
https://doi.org/10.1186/s13014-022-02155-7

Other articles of this Issue 1/2022

Radiation Oncology 1/2022 Go to the issue

Research

Impact of fractionated stereotactic radiotherapy on activity of daily living and performance status in progressive/recurrent glioblastoma: a retrospective study

Research

Chemoradiotherapy is an alternative choice for patients with primary mediastinal seminoma

Research

Dosimetric parameters and absolute monocyte count can predict the prognosis of acute hematologic toxicity in cervical cancer patients undergoing concurrent chemotherapy and volumetric-modulated arc therapy

Research

Postoperative complications and oncologic outcomes after multimodal therapy of localized high risk soft tissue sarcoma

Research

Ganetespib selectively sensitizes cancer cells for proximal and distal spread-out Bragg peak proton irradiation

Research

Predictors of radiation-induced hypothyroidism in nasopharyngeal carcinoma survivors after intensity-modulated radiotherapy