978 related articles for article (PubMed ID: 23127079)
1. Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans.
Yuan L; Ge Y; Lee WR; Yin FF; Kirkpatrick JP; Wu QJ
Med Phys; 2012 Nov; 39(11):6868-78. PubMed ID: 23127079
[TBL] [Abstract][Full Text] [Related]
2. Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: an intertechnique and interinstitutional study.
Lian J; Yuan L; Ge Y; Chera BS; Yoo DP; Chang S; Yin F; Wu QJ
Med Phys; 2013 Dec; 40(12):121704. PubMed ID: 24320490
[TBL] [Abstract][Full Text] [Related]
3. A method for a priori estimation of best feasible DVH for organs-at-risk: Validation for head and neck VMAT planning.
Ahmed S; Nelms B; Gintz D; Caudell J; Zhang G; Moros EG; Feygelman V
Med Phys; 2017 Oct; 44(10):5486-5497. PubMed ID: 28777469
[TBL] [Abstract][Full Text] [Related]
4. Dose-shaping using targeted sparse optimization.
Sayre GA; Ruan D
Med Phys; 2013 Jul; 40(7):071711. PubMed ID: 23822415
[TBL] [Abstract][Full Text] [Related]
5. Predicting dose-volume histograms for organs-at-risk in IMRT planning.
Appenzoller LM; Michalski JM; Thorstad WL; Mutic S; Moore KL
Med Phys; 2012 Dec; 39(12):7446-61. PubMed ID: 23231294
[TBL] [Abstract][Full Text] [Related]
6. Patient geometry-driven information retrieval for IMRT treatment plan quality control.
Wu B; Ricchetti F; Sanguineti G; Kazhdan M; Simari P; Chuang M; Taylor R; Jacques R; McNutt T
Med Phys; 2009 Dec; 36(12):5497-505. PubMed ID: 20095262
[TBL] [Abstract][Full Text] [Related]
7. MO-D-BRB-10: Modeling Inter-Patient Variation of Organ-At-Risk Sparing in IMRT Plans: An Evidence-Based Plan Quality Evaluation.
Yuan L; Ge Y; Li T; Yin F; Wu QJ
Med Phys; 2012 Jun; 39(6Part21):3868. PubMed ID: 28518274
[TBL] [Abstract][Full Text] [Related]
8. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning.
Zhu X; Ge Y; Li T; Thongphiew D; Yin FF; Wu QJ
Med Phys; 2011 Feb; 38(2):719-26. PubMed ID: 21452709
[TBL] [Abstract][Full Text] [Related]
9. Creation of knowledge-based planning models intended for large scale distribution: Minimizing the effect of outlier plans.
Alpuche Aviles JE; Cordero Marcos MI; Sasaki D; Sutherland K; Kane B; Kuusela E
J Appl Clin Med Phys; 2018 May; 19(3):215-226. PubMed ID: 29633474
[TBL] [Abstract][Full Text] [Related]
10. Postoperative radiotherapy for prostate cancer: a comparison of four consensus guidelines and dosimetric evaluation of 3D-CRT versus tomotherapy IMRT.
Malone S; Croke J; Roustan-Delatour N; Belanger E; Avruch L; Malone C; Morash C; Kayser C; Underhill K; Li Y; Malone K; Nyiri B; Spaans J
Int J Radiat Oncol Biol Phys; 2012 Nov; 84(3):725-32. PubMed ID: 22444999
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Target miss using PTV-based IMRT compared to robust optimization via coverage probability concept in prostate cancer.
Outaggarts Z; Wegener D; Berger B; Zips D; Paulsen F; Bleif M; Thorwarth D; Alber M; Dohm O; Müller AC
Acta Oncol; 2020 Aug; 59(8):911-917. PubMed ID: 32436467
[No Abstract] [Full Text] [Related]
13. SU-E-T-626: Individualized Trade-Off of Dose Coverage and Sparing in IMRT Planning.
Yuan L; Ge Y; Li T; Yin F; Wu QJ
Med Phys; 2012 Jun; 39(6Part20):3850. PubMed ID: 28517546
[TBL] [Abstract][Full Text] [Related]
14. Semiautomated head-and-neck IMRT planning using dose warping and scaling to robustly adapt plans in a knowledge database containing potentially suboptimal plans.
Schmidt M; Lo JY; Grzetic S; Lutzky C; Brizel DM; Das SK
Med Phys; 2015 Aug; 42(8):4428-34. PubMed ID: 26233173
[TBL] [Abstract][Full Text] [Related]
15. An improved distance-to-dose correlation for predicting bladder and rectum dose-volumes in knowledge-based VMAT planning for prostate cancer.
Wall PDH; Carver RL; Fontenot JD
Phys Med Biol; 2018 Jan; 63(1):015035. PubMed ID: 29131812
[TBL] [Abstract][Full Text] [Related]
16. Radiotherapy for early mediastinal Hodgkin lymphoma according to the German Hodgkin Study Group (GHSG): the roles of intensity-modulated radiotherapy and involved-node radiotherapy.
Koeck J; Abo-Madyan Y; Lohr F; Stieler F; Kriz J; Mueller RP; Wenz F; Eich HT
Int J Radiat Oncol Biol Phys; 2012 May; 83(1):268-76. PubMed ID: 22079733
[TBL] [Abstract][Full Text] [Related]
17. A deep learning model to predict dose-volume histograms of organs at risk in radiotherapy treatment plans.
Liu Z; Chen X; Men K; Yi J; Dai J
Med Phys; 2020 Nov; 47(11):5467-5481. PubMed ID: 32677104
[TBL] [Abstract][Full Text] [Related]
18. Knowledge-based planning using both the predicted DVH of organ-at risk and planning target volume.
Jiao SX; Wang ML; Chen LX; Liu XW
Med Eng Phys; 2022 Dec; 110():103803. PubMed ID: 35461772
[TBL] [Abstract][Full Text] [Related]
19. Dosimetric Evaluation of Different Intensity-Modulated Radiotherapy Techniques for Breast Cancer After Conservative Surgery.
Zhang F; Wang Y; Xu W; Jiang H; Liu Q; Gao J; Yao B; Hou J; He H
Technol Cancer Res Treat; 2015 Oct; 14(5):515-23. PubMed ID: 25311257
[TBL] [Abstract][Full Text] [Related]
20. Dependence of achievable plan quality on treatment technique and planning goal refinement: a head-and-neck intensity modulated radiation therapy application.
Qi XS; Ruan D; Lee SP; Pham A; Kupelian P; Low DA; Steinberg M; Demarco J
Int J Radiat Oncol Biol Phys; 2015 Mar; 91(4):817-24. PubMed ID: 25752396
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
