118 related articles for article (PubMed ID: 38088017)
21. Potential for reduced radiation-induced toxicity using intensity-modulated arc therapy for whole-brain radiotherapy with hippocampal sparing.
Pokhrel D; Sood S; Lominska C; Kumar P; Badkul R; Jiang H; Wang F
J Appl Clin Med Phys; 2015 Sep; 16(5):131–141. PubMed ID: 26699321
[TBL] [Abstract][Full Text] [Related]
22. Improvement of conformal arc plans by using deformable margin delineation method for stereotactic lung radiotherapy.
Güngör G; Demir M; Aydın G; Yapıcı B; Atalar B; Özyar E
J Appl Clin Med Phys; 2018 Jan; 19(1):184-193. PubMed ID: 29218841
[TBL] [Abstract][Full Text] [Related]
23. [Dosimetric comparison of the helical tomotherapy, intensity-modulated radiotherapy and volumetric-modulated arc therapy in radical radiotherapy for esophageal cancer].
Xu YJ; Li P; Hu X; Wang J; Ma HL; Chen M
Zhonghua Yi Xue Za Zhi; 2019 Nov; 99(41):3260-3265. PubMed ID: 31694123
[No Abstract] [Full Text] [Related]
24. Applying the technique of volume-modulated arc radiotherapy to upper esophageal carcinoma.
Ma P; Wang X; Xu Y; Dai J; Wang L
J Appl Clin Med Phys; 2014 May; 15(3):4732. PubMed ID: 24892348
[TBL] [Abstract][Full Text] [Related]
25. Advanced techniques in neoadjuvant radiotherapy allow dose escalation without increased dose to the organs at risk : Planning study in esophageal carcinoma.
Fakhrian K; Oechsner M; Kampfer S; Schuster T; Molls M; Geinitz H
Strahlenther Onkol; 2013 Apr; 189(4):293-300. PubMed ID: 23443611
[TBL] [Abstract][Full Text] [Related]
26. Deep learning architecture with transformer and semantic field alignment for voxel-level dose prediction on brain tumors.
Yang J; Zhao Y; Zhang F; Liao M; Yang X
Med Phys; 2023 Feb; 50(2):1149-1161. PubMed ID: 36434793
[TBL] [Abstract][Full Text] [Related]
27. Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning.
Jiao SX; Wang ML; Chen LX; Liu XW
Sci Rep; 2021 Feb; 11(1):3117. PubMed ID: 33542427
[TBL] [Abstract][Full Text] [Related]
28. Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning.
Wang H; Wang R; Liu J; Zhang J; Yao K; Yue H; Zhang Y; You J; Wu H
Br J Radiol; 2021 Jul; 94(1123):20210214. PubMed ID: 34111955
[TBL] [Abstract][Full Text] [Related]
29. Efficacy of virtual block objects in reducing the lung dose in helical tomotherapy planning for cervical oesophageal cancer: a planning study.
Ito M; Shimizu H; Aoyama T; Tachibana H; Tomita N; Makita C; Koide Y; Kato D; Ishiguchi T; Kodaira T
Radiat Oncol; 2018 Apr; 13(1):62. PubMed ID: 29618353
[TBL] [Abstract][Full Text] [Related]
30. Auto- versus human-driven plan in mediastinal Hodgkin lymphoma radiation treatment.
Clemente S; Oliviero C; Palma G; D'Avino V; Liuzzi R; Conson M; Pacelli R; Cella L
Radiat Oncol; 2018 Oct; 13(1):202. PubMed ID: 30340604
[TBL] [Abstract][Full Text] [Related]
31. Intentional deep overfit learning for patient-specific dose predictions in adaptive radiotherapy.
Maniscalco A; Liang X; Lin MH; Jiang S; Nguyen D
Med Phys; 2023 Sep; 50(9):5354-5363. PubMed ID: 37459122
[TBL] [Abstract][Full Text] [Related]
32. Automated Intensity Modulated Radiation Therapy Treatment Planning for Cervical Cancer Based on Convolution Neural Network.
Jihong C; Penggang B; Xiuchun Z; Kaiqiang C; Wenjuan C; Yitao D; Jiewei Q; Kerun Q; Jing Z; Tianming W
Technol Cancer Res Treat; 2020; 19():1533033820957002. PubMed ID: 33016230
[TBL] [Abstract][Full Text] [Related]
33. Automatic treatment planning facilitates fast generation of high-quality treatment plans for esophageal cancer.
Hansen CR; Nielsen M; Bertelsen AS; Hazell I; Holtved E; Zukauskaite R; Bjerregaard JK; Brink C; Bernchou U
Acta Oncol; 2017 Nov; 56(11):1495-1500. PubMed ID: 28840767
[TBL] [Abstract][Full Text] [Related]
34. Impact of dose calculation accuracy during optimization on lung IMRT plan quality.
Li Y; Rodrigues A; Li T; Yuan L; Yin FF; Wu QJ
J Appl Clin Med Phys; 2015 Jan; 16(1):5137. PubMed ID: 25679172
[TBL] [Abstract][Full Text] [Related]
35. A deep learning method for prediction of three-dimensional dose distribution of helical tomotherapy.
Liu Z; Fan J; Li M; Yan H; Hu Z; Huang P; Tian Y; Miao J; Dai J
Med Phys; 2019 May; 46(5):1972-1983. PubMed ID: 30870586
[TBL] [Abstract][Full Text] [Related]
36. [Dosimetric comparison between intensity-modulated radiotherapy and conformal radiotherapy for upper thoracic esophageal carcinoma].
Zhang WZ; Chen ZJ; Li DR; Lin ZX; Li DS; Chen CZ
Ai Zheng; 2009 Nov; 28(11):1127-31. PubMed ID: 19895730
[TBL] [Abstract][Full Text] [Related]
37. Evaluation of auto-planning in IMRT and VMAT for head and neck cancer.
Ouyang Z; Liu Shen Z; Murray E; Kolar M; LaHurd D; Yu N; Joshi N; Koyfman S; Bzdusek K; Xia P
J Appl Clin Med Phys; 2019 Jul; 20(7):39-47. PubMed ID: 31270937
[TBL] [Abstract][Full Text] [Related]
38. 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.
Nguyen D; Jia X; Sher D; Lin MH; Iqbal Z; Liu H; Jiang S
Phys Med Biol; 2019 Mar; 64(6):065020. PubMed ID: 30703760
[TBL] [Abstract][Full Text] [Related]
39. Dosimetric benefits of IMRT and VMAT in the treatment of middle thoracic esophageal cancer: is the conformal radiotherapy still an alternative option?
Wu Z; Xie C; Hu M; Han C; Yi J; Zhou Y; Yuan H; Jin X
J Appl Clin Med Phys; 2014 May; 15(3):93–101. PubMed ID: 24892336
[TBL] [Abstract][Full Text] [Related]
40. Dose-volume comparison of intensity modulated proton therapy and volumetric modulated arc therapy for cervical esophageal cancer.
Kato T; Ono T; Narita Y; Komori S; Murakami M
Med Dosim; 2022 Autumn; 47(3):216-221. PubMed ID: 35346554
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]