BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

171 related articles for article (PubMed ID: 37942993)

  • 1. Essentially unedited deep-learning-based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning.
    Koo J; Caudell J; Latifi K; Moros EG; Feygelman V
    J Appl Clin Med Phys; 2024 Mar; 25(3):e14202. PubMed ID: 37942993
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer.
    Guo H; Wang J; Xia X; Zhong Y; Peng J; Zhang Z; Hu W
    Radiat Oncol; 2021 Jun; 16(1):113. PubMed ID: 34162410
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: Time, geometrical, and dosimetric analysis.
    Fung NTC; Hung WM; Sze CK; Lee MCH; Ng WT
    Med Dosim; 2020 Spring; 45(1):60-65. PubMed ID: 31345672
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans.
    Gronberg MP; Beadle BM; Garden AS; Skinner H; Gay S; Netherton T; Cao W; Cardenas CE; Chung C; Fuentes DT; Fuller CD; Howell RM; Jhingran A; Lim TY; Marquez B; Mumme R; Olanrewaju AM; Peterson CB; Vazquez I; Whitaker TJ; Wooten Z; Yang M; Court LE
    Pract Radiat Oncol; 2023; 13(3):e282-e291. PubMed ID: 36697347
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Assessment of PlanIQ Feasibility DVH for head and neck treatment planning.
    Fried DV; Chera BS; Das SK
    J Appl Clin Med Phys; 2017 Sep; 18(5):245-250. PubMed ID: 28857470
    [TBL] [Abstract][Full Text] [Related]  

  • 10. General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis.
    Amjad A; Xu J; Thill D; Lawton C; Hall W; Awan MJ; Shukla M; Erickson BA; Li XA
    Med Phys; 2022 Mar; 49(3):1686-1700. PubMed ID: 35094390
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.
    McDonald BA; Cardenas CE; O'Connell N; Ahmed S; Naser MA; Wahid KA; Xu J; Thill D; Zuhour RJ; Mesko S; Augustyn A; Buszek SM; Grant S; Chapman BV; Bagley AF; He R; Mohamed ASR; Christodouleas J; Brock KK; Fuller CD
    Med Phys; 2024 Jan; 51(1):278-291. PubMed ID: 37475466
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy.
    Shen Y; Tang X; Lin S; Jin X; Ding J; Shao M
    Med Phys; 2024 Jan; 51(1):545-555. PubMed ID: 37748133
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy.
    Koo J; Caudell JJ; Latifi K; Jordan P; Shen S; Adamson PM; Moros EG; Feygelman V
    Radiother Oncol; 2022 Sep; 174():52-58. PubMed ID: 35817322
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.
    Alzahrani NM; Henry AM; Clark AK; Al-Qaisieh BM; Murray LJ; Nix MG
    J Appl Clin Med Phys; 2024 May; 25(5):e14345. PubMed ID: 38664894
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. PTV-based IMPT optimization incorporating planning risk volumes vs robust optimization.
    Liu W; Frank SJ; Li X; Li Y; Zhu RX; Mohan R
    Med Phys; 2013 Feb; 40(2):021709. PubMed ID: 23387732
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Clinical adequacy assessment of autocontours for prostate IMRT with meaningful endpoints.
    Nourzadeh H; Watkins WT; Ahmed M; Hui C; Schlesinger D; Siebers JV
    Med Phys; 2017 Apr; 44(4):1525-1537. PubMed ID: 28196288
    [TBL] [Abstract][Full Text] [Related]  

  • 18. DVHnet: A deep learning-based prediction of patient-specific dose volume histograms for radiotherapy planning.
    Chen X; Men K; Zhu J; Yang B; Li M; Liu Z; Yan X; Yi J; Dai J
    Med Phys; 2021 Jun; 48(6):2705-2713. PubMed ID: 33550616
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A feasibility study on deep learning-based individualized 3D dose distribution prediction.
    Ma J; Nguyen D; Bai T; Folkerts M; Jia X; Lu W; Zhou L; Jiang S
    Med Phys; 2021 Aug; 48(8):4438-4447. PubMed ID: 34091925
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning.
    Jiang D; Li T; Mao R; Du C; Liu J
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():868-871. PubMed ID: 31946032
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.