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Cited by in F6Publishing
For: Campbell WG, Miften M, Olsen L, Stumpf P, Schefter T, Goodman KA, Jones BL. Neural network dose models for knowledge-based planning in pancreatic SBRT. Med Phys. 2017;44:6148-6158. [PMID: 28994459 DOI: 10.1002/mp.12621] [Cited by in Crossref: 27] [Cited by in F6Publishing: 24] [Article Influence: 6.8] [Reference Citation Analysis]
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6 Jiao SX, Chen LX, Zhu JH, Wang ML, Liu XW. Prediction of dose-volume histograms in nasopharyngeal cancer IMRT using geometric and dosimetric information. Phys Med Biol 2019;64:23NT04. [PMID: 31648210 DOI: 10.1088/1361-6560/ab50eb] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
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9 Zhuang Y, Xie Y, Wang L, Huang S, Chen LX, Wang Y. DVH Prediction for VMAT in NPC with GRU-RNN: An Improved Method by Considering Biological Effects. Biomed Res Int 2021;2021:2043830. [PMID: 33532489 DOI: 10.1155/2021/2043830] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Momin S, Fu Y, Lei Y, Roper J, Bradley JD, Curran WJ, Liu T, Yang X. Knowledge-based radiation treatment planning: A data-driven method survey. J Appl Clin Med Phys 2021;22:16-44. [PMID: 34231970 DOI: 10.1002/acm2.13337] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
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12 Poortmans PMP, Takanen S, Marta GN, Meattini I, Kaidar-Person O. Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer. Breast 2020;49:194-200. [PMID: 31931265 DOI: 10.1016/j.breast.2019.11.011] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
13 Wang C, Zhu X, Hong JC, Zheng D. Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future. Technol Cancer Res Treat. 2019;18:1533033819873922. [PMID: 31495281 DOI: 10.1177/1533033819873922] [Cited by in Crossref: 34] [Cited by in F6Publishing: 21] [Article Influence: 34.0] [Reference Citation Analysis]
14 Nilsson V, Gruselius H, Zhang T, De Kerf G, Claessens M. Probabilistic dose prediction using mixture density networks for automated radiation therapy treatment planning. Phys Med Biol 2021;66:055003. [PMID: 33470973 DOI: 10.1088/1361-6560/abdd8a] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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18 Thomas MA, Fu Y, Yang D. Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy. J Appl Clin Med Phys 2020;21:60-9. [PMID: 32306535 DOI: 10.1002/acm2.12884] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
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20 Momin S, Lei Y, Wang T, Zhang J, Roper J, Bradley JD, Curran WJ, Patel P, Liu T, Yang X. Learning-based dose prediction for pancreatic stereotactic body radiation therapy using dual pyramid adversarial network. Phys Med Biol 2021;66. [PMID: 34087807 DOI: 10.1088/1361-6560/ac0856] [Reference Citation Analysis]
21 Vinogradskiy Y, Goodman KA, Schefter T, Miften M, Jones BL. The Clinical and Dosimetric Impact of Real-Time Target Tracking in Pancreatic SBRT. Int J Radiat Oncol Biol Phys 2019;103:268-75. [PMID: 30145394 DOI: 10.1016/j.ijrobp.2018.08.021] [Cited by in Crossref: 13] [Cited by in F6Publishing: 6] [Article Influence: 4.3] [Reference Citation Analysis]
22 Nakamura A, Prichard HA, Wo JY, Wolfgang JA, Hong TS. Elective nodal irradiation with simultaneous integrated boost stereotactic body radiotherapy for pancreatic cancer: Analyses of planning feasibility and geometrically driven DVH prediction model. J Appl Clin Med Phys 2019;20:71-83. [PMID: 30636367 DOI: 10.1002/acm2.12528] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
23 Chen X, Men K, Li Y, Yi J, Dai J. A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning. Med Phys 2019;46:56-64. [PMID: 30367492 DOI: 10.1002/mp.13262] [Cited by in Crossref: 51] [Cited by in F6Publishing: 38] [Article Influence: 17.0] [Reference Citation Analysis]
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