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For: Kim M, Ghate A, Phillips MH. A stochastic control formalism for dynamic biologically conformal radiation therapy. European Journal of Operational Research 2012;219:541-56. [DOI: 10.1016/j.ejor.2011.10.039] [Cited by in Crossref: 30] [Cited by in F6Publishing: 16] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Ten Eikelder SCM, Ferjančič P, Ajdari A, Bortfeld T, den Hertog D, Jeraj R. Optimal treatment plan adaptation using mid-treatment imaging biomarkers. Phys Med Biol 2020;65:245011. [PMID: 33053518 DOI: 10.1088/1361-6560/abc130] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Ebrahimi S, Lim GJ. A reinforcement learning approach for finding optimal policy of adaptive radiation therapy considering uncertain tumor biological response. Artif Intell Med 2021;121:102193. [PMID: 34763808 DOI: 10.1016/j.artmed.2021.102193] [Reference Citation Analysis]
3 Kim M, Phillips MH. A feasibility study of dynamic adaptive radiotherapy for nonsmall cell lung cancer. Med Phys 2016;43:2153. [PMID: 27147327 DOI: 10.1118/1.4945023] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 0.8] [Reference Citation Analysis]
4 Saberian F, Ghate A, Kim M. Spatiotemporally Optimal Fractionation in Radiotherapy. INFORMS Journal on Computing 2017;29:422-37. [DOI: 10.1287/ijoc.2016.0740] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 1.6] [Reference Citation Analysis]
5 Sauré A, Patrick J, Tyldesley S, Puterman ML. Dynamic multi-appointment patient scheduling for radiation therapy. European Journal of Operational Research 2012;223:573-84. [DOI: 10.1016/j.ejor.2012.06.046] [Cited by in Crossref: 98] [Cited by in F6Publishing: 26] [Article Influence: 9.8] [Reference Citation Analysis]
6 Mar PA, Chan TC. Adaptive and robust radiation therapy in the presence of drift. Phys Med Biol 2015;60:3599-615. [PMID: 25860509 DOI: 10.1088/0031-9155/60/9/3599] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 0.7] [Reference Citation Analysis]
7 Unkelbach J, Papp D. The emergence of nonuniform spatiotemporal fractionation schemes within the standard BED model. Med Phys 2015;42:2234-41. [PMID: 25979017 DOI: 10.1118/1.4916684] [Cited by in Crossref: 18] [Cited by in F6Publishing: 15] [Article Influence: 3.0] [Reference Citation Analysis]
8 Gaddy MR, Yıldız S, Unkelbach J, Papp D. Optimization of spatiotemporally fractionated radiotherapy treatments with bounds on the achievable benefit. Phys Med Biol 2018;63:015036. [PMID: 29303116 DOI: 10.1088/1361-6560/aa9975] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.8] [Reference Citation Analysis]
9 ten Eikelder SCM, Ajdari A, Bortfeld T, den Hertog D. Adjustable robust treatment-length optimization in radiation therapy. Optim Eng. [DOI: 10.1007/s11081-021-09709-w] [Reference Citation Analysis]
10 Salari E, Unkelbach J, Bortfeld T. A mathematical programming approach to the fractionation problem in chemoradiotherapy. IIE Transactions on Healthcare Systems Engineering 2015;5:55-73. [DOI: 10.1080/19488300.2015.1017673] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 0.7] [Reference Citation Analysis]
11 Rockne RC, Frankel P. Mathematical Modeling in Radiation Oncology. In: Wong JY, Schultheiss TE, Radany EH, editors. Advances in Radiation Oncology. Cham: Springer International Publishing; 2017. pp. 255-71. [DOI: 10.1007/978-3-319-53235-6_12] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
12 Saberian F, Ghate A, Kim M. Optimal fractionation in radiotherapy with multiple normal tissues. Math Med Biol 2016;33:211-52. [DOI: 10.1093/imammb/dqv015] [Cited by in Crossref: 22] [Cited by in F6Publishing: 12] [Article Influence: 3.1] [Reference Citation Analysis]
13 Bortfeld T, Ramakrishnan J, Tsitsiklis JN, Unkelbach J. Optimization of Radiation Therapy Fractionation Schedules in the Presence of Tumor Repopulation. INFORMS Journal on Computing 2015;27:788-803. [DOI: 10.1287/ijoc.2015.0659] [Cited by in Crossref: 16] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]
14 Saberian F, Ghate A, Kim M. A theoretical stochastic control framework for adapting radiotherapy to hypoxia. Phys Med Biol 2016;61:7136-61. [DOI: 10.1088/0031-9155/61/19/7136] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
15 Böck M. On adaptation cost and tractability in robust adaptive radiation therapy optimization. Med Phys 2020;47:2791-804. [PMID: 32275778 DOI: 10.1002/mp.14167] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 Cheung NJ, Xu Z, Ding X, Shen H. Modeling nonlinear dynamic biological systems with human-readable fuzzy rules optimized by convergent heterogeneous particle swarm. European Journal of Operational Research 2015;247:349-58. [DOI: 10.1016/j.ejor.2015.03.047] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 1.1] [Reference Citation Analysis]
17 Ajdari A, Ghate A, Kim M. Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy. Phys Med Biol 2018;63:075009. [DOI: 10.1088/1361-6560/aab4b6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
18 Saberian F, Ghate A, Kim M. A two-variable linear program solves the standard linear–quadratic formulation of the fractionation problem in cancer radiotherapy. Operations Research Letters 2015;43:254-8. [DOI: 10.1016/j.orl.2015.02.005] [Cited by in Crossref: 16] [Cited by in F6Publishing: 11] [Article Influence: 2.3] [Reference Citation Analysis]
19 Hadid M, Elomri A, El Mekkawy T, Kerbache L, El Omri A, El Omri H, Taha RY, Hamad AA, Al Thani MHJ. Bibliometric analysis of cancer care operations management: current status, developments, and future directions. Health Care Manag Sci 2022. [PMID: 34981268 DOI: 10.1007/s10729-021-09585-x] [Reference Citation Analysis]
20 Saka B, Rardin RL, Langer MP. Biologically guided intensity modulated radiation therapy planning optimization with fraction-size dose constraints. Journal of the Operational Research Society 2017;65:557-71. [DOI: 10.1057/jors.2013.144] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
21 Azimi M, Kamrani A, Smadi H. Statistics-Based Prediction Analysis for Head and Neck Cancer Tumor Deformation. Journal of Healthcare Engineering 2012;3:571-86. [DOI: 10.1260/2040-2295.3.4.571] [Cited by in Crossref: 1] [Article Influence: 0.1] [Reference Citation Analysis]
22 Böck M, Eriksson K, Forsgren A. On the interplay between robustness and dynamic planning for adaptive radiation therapy*. Biomed Phys Eng Express 2019;5:045004. [DOI: 10.1088/2057-1976/ab1bfc] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]