A Theoretical Framework for Learning Tumor Dose-Response Uncertainty in Individualized Spatiobiologically Integrated Radiotherapy

Published Online:

References

  • Adib A, Salari E (2017) Spatiotemporally integrated radiotherapy plan optimization. AAPM 59th Annual Meeting (American Association of Physicists in Medicine, Alexandria, VA). Accessed July 8, 2019, http://www.aapm.org/meetings/2017AM/PRAbs.asp?mid=127{\&}aid=36650.Google Scholar
  • Ajdari A, Ghate A (2016a) A model predictive control approach for discovering nonstationary fluence-maps in cancer radiotherapy fractionation. Proc. Winter Simulation Conf. (IEEE Press, Piscataway, NJ), 2065–2075.CrossrefGoogle Scholar
  • Ajdari A, Ghate A (2016b) Robust spatiotemporally integrated fractionation in radiotherapy. Oper. Res. Lett. 44(4):544–549.CrossrefGoogle Scholar
  • Armpilia CI, Dale RG, Jones B (2004) Determination of the optimum dose per fraction in fractionated radiotherapy when there is delayed onset of tumour repopulation during treatment. British J. Radiology 77(921):765–767.CrossrefGoogle Scholar
  • Bading JR, Shields AF (2008) Imaging of cell proliferation: Status and prospects. J. Nucl. Med. 49(6):64S–80S.CrossrefGoogle Scholar
  • Badri H, Watanabe Y, Leder K (2016) Optimal radiotherapy dose schedules under parametric uncertainty. Phys. Med. Biol. 61(1):338–364.CrossrefGoogle Scholar
  • Bertsekas DP (2007) Dynamic Programming and Optimal Control, vols. 1 and 2, 3rd ed. (Athena Scientific, Nashua, NH).Google Scholar
  • Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev. 53(3):464–501.CrossrefGoogle Scholar
  • Bertuzzi A, Papa CBF, Sinisgalli C (2013) Optimal solution for a cancer radiotherapy problem. J. Math. Biol. 66(1–2):311–349.CrossrefGoogle Scholar
  • Bortfeld T, Ramakrishnan J, Tsitsiklis JN, Unkelbach J (2015) Optimization of radiation therapy fractionation schedules in the presence of tumor repopulation. INFORMS J. Comput. 27(4):788–803.LinkGoogle Scholar
  • Boutilier JJ, Craig T, Sharpe MB, Chan TCY (2016) Sample size requirements for knowledge-based treatment planning. Med. Phys. 43(3):1212–1221.CrossrefGoogle Scholar
  • Burman C, Chui CS, Kutcher G, Leibel S, Zelefsky M, LoSasso T, Spirou S, et al.. (1997) Planning, delivery, and quality assurance of intensity-modulated radiotherapy using dynamic multileaf collimator: A strategy for large-scale implementation for the treatment of carcinoma of the prostate. Internat. J. Radiation Oncology Biol. Phys. 39(4):863–873.CrossrefGoogle Scholar
  • Ehrgott M, Guler C, Hamacher HW, Shao L (2008) Mathematical optimization in intensity modulated radiation therapy. 4OR 6(3):199–262.CrossrefGoogle Scholar
  • Eisbruch A (2002) Intensity-modulated radiotherapy of head-and-neck cancer: Encouraging early results. Internat. J. Radiation Oncology Biol. Phys. 53(1):1–3.CrossrefGoogle Scholar
  • Emami B, Lyman J, Brown A, Coia L, Goiten M, Munzenride JE, Shank B, Solin LJ, Wesson M (1991) Tolerance of normal tissue to therapeutic radiation. Internat. J. Radiation Oncology Biol. Phys. 21(1):109–122.CrossrefGoogle Scholar
  • Fowler JF (1990) How worthwhile are short schedules in radiotherapy? A series of exploratory calculations. Radiotherepy Oncology 18(2):165–181.CrossrefGoogle Scholar
  • Fowler JF (2001) Biological factors influencing optimum fractionation in radiation therapy. Acta Oncology 40(6):712–717.CrossrefGoogle Scholar
  • Fowler JF (2008) Optimum overall times II: Extended modelling for head and neck radiotherapy. Clinical Oncology (Roy. College Radiologists) 20(2):113–126.CrossrefGoogle Scholar
  • Fowler JF (2009) Sensitivity analysis of parameters in linear-quadratic radiobiologic modeling. Internat. J. Radiation Oncology Biol. Phys. 73(5):1532–1537.CrossrefGoogle Scholar
  • Fowler JF (2010) 21 years of biologically effective dose. British J. Radiology 83(991):554–568.CrossrefGoogle Scholar
  • Fowler JF, Ritter MA (1995) A rationale for fractionation for slowly proliferating tumors such as prostatic adenocarcinoma. Internat. J. Radiation Oncology Biol. Phys. 32(2):521–529.CrossrefGoogle Scholar
  • Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2014) Bayesian Data Analysis, 3rd ed. (CRC Press, Boca Raton, FL).Google Scholar
  • Ghate A (2011) Dynamic optimization in radiotherapy. Geunes J, ed. Transforming Research into Action, TutORials in Operations Research (INFORMS, Catonsville, MD), 60–74.LinkGoogle Scholar
  • Grant M, Boyd S (2009) CVX: Matlab software for disciplined convex programming (web page and software). Accessed July 7, 2019, http://cvxr.com/cvx/.Google Scholar
  • Hall EJ, Giaccia AJ (2005) Radiobiology for the Radiologist (Lippincott Williams & Wilkins, Philadelphia).Google Scholar
  • Jones B, Tan LT, Dale RG (1995) Derivation of the optimum dose per fraction from the linear quadratic model. British J. Radiology 68(812):894–902.CrossrefGoogle Scholar
  • Keller H, Meier G, Hope A, Davison M (2012) SU-E-T-461: Fractionation schedule optimization for lung cancer treatments using radiobiological and dose distribution characteristics. Med. Phys. 39(6):3811.CrossrefGoogle Scholar
  • Kim M, Craft DL, Orton CG (2016) Within the next five years, most radiotherapy treatment schedules will be designed using spatiotemporal optimization. Med. Phys. 43(5):2009–2012.CrossrefGoogle Scholar
  • Kim M, Ghate A, Phillips M (2012) A stochastic control formalism for dynamic biologically conformal radiation therapy. Eur. J. Oper. Res. 219(3):541–556.CrossrefGoogle Scholar
  • Langer M, Lee EK, Deasy JO, Rardin RL, Deye JA (2003) Operations research applied to radiotherapy, an NCI-NSF-sponsored workshop February 7–9, 2002. Internat. J. Radiation Oncology Biol. Phys. 57(3):762–768.CrossrefGoogle Scholar
  • Marks LB, Yorke ED, Jackson A, Haken RKT, Constine LS, Eisbruch A, Bentzen SM, Nam J, Deasy JO (2010) Use of normal tissue complication probability models in the clinic. Internat. J. Radiation Oncology Biol. Phys. 76(3):S10–S19.CrossrefGoogle Scholar
  • Mizuta M, Takao S, Date H, Kishimoto N, Sutherland KL, Onimaru R, Shirato H (2012) A mathematical study to select fractionation regimen based on physical dose distribution and the linear-quadratic model. Internat. J. Radiation Oncology Biol. Phys. 84(3):829–833.CrossrefGoogle Scholar
  • Powell W (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • Romeijn HE, Ahuja RK, Dempsey JF, Kumar A (2006) A new linear programming approach to radiation therapy treatment planning problems. Oper. Res. 54(2):201–216.LinkGoogle Scholar
  • Saberian F, Ghate A, Kim M (2015a) Optimal fractionation in radiotherapy with multiple normal tissues. Math. Med. Biol. 33(2):211–252.CrossrefGoogle Scholar
  • Saberian F, Ghate A, Kim M (2015b) A two-variable linear program solves the standard linear–quadratic formulation of the fractionation problem in cancer radiotherapy. Oper. Res. Lett. 43(3):254–258.CrossrefGoogle Scholar
  • Saberian F, Ghate A, Kim M (2016) A theoretical stochastic control framework for adapting radiotherapy to hypoxia. Phys. Med. Biol. 61(19):7136–7161.CrossrefGoogle Scholar
  • Saberian F, Ghate A, Kim M (2017) Spatiotemporally optimal fractionation in radiotherapy. INFORMS J. Comput. 29(3):422–437.LinkGoogle Scholar
  • Shepard DM, Ferris MC, Olivera GH, Mackie TR (1999) Optimizing the delivery of radiation therapy to cancer patients. SIAM Rev. 41(4):721–744.CrossrefGoogle Scholar
  • Stewart RD, Li XA (2007) BGRT: Biologically guided radiation therapy—the future is fast approaching. Med. Phys. 34(10):3739–3751.CrossrefGoogle Scholar
  • Thorwarth D (2015) Functional imaging for radiotherapy treatment planning: Current status and future directions—a review. British J. Radiology 88(1051):20150056.CrossrefGoogle Scholar
  • Unkelbach J (2015) Non-uniform spatiotemporal fractionation schemes in photon radiotherapy. IFMBE Proc. World Congress Med. Phys. Biomed. Engrg. (Springer, Cham, Switzerland), 401–404.Google Scholar
  • Unkelbach J, Papp D (2015) The emergence of nonuniform spatiotemporal fractionation schemes within the standard BED model. Med. Phys. 42(5):2234–2241.CrossrefGoogle Scholar
  • Unkelbach J, Zeng C, Engelsman M (2013b) Simultaneous optimization of dose distributions and fractionation schemes in particle radiotherapy. Med. Phys. 40(9):091702.CrossrefGoogle Scholar
  • Unkelbach J, Craft D, Saleri E, Ramakrishnan J, Bortfeld T (2013a) The dependence of optimal fractionation schemes on the spatial dose distribution. Phys. Med. Biol. 58(1):159–167.CrossrefGoogle Scholar
  • Vogel WV, Lam MGEH, Pameijer FA, van der Heide UA, van den Kamer JB, Philippens ME, van Vulpen M, Verheij M (2016) Functional imaging in radiotherapy in the Netherlands: Availability and impact on clinical practice. Clinical Oncology (Roy. College Radiologists) 28(12):e206–e215.CrossrefGoogle Scholar
  • Webb S (2010) Contemporary IMRT: Developing Physics and Clinical Implementation (IOP Publishing, Bristol, UK).Google Scholar
  • Williams MV, Denekamp J, Fowler JF (1985) A review of alpha/beta ratios for experimental tumors: Implications for clinical studies of altered fractionation. Internat. J. Radiation Oncology Biol. Phys. 11(1):87–96.CrossrefGoogle Scholar
  • Yang Y, Xing L (2005a) Optimization of radiotherapy dose-time fractionation with consideration of tumor specific biology. Med. Phys. 32(12):3666–3677.CrossrefGoogle Scholar
  • Yang Y, Xing L (2005b) Towards biologically conformal radiation therapy (BCRT): Selective IMRT dose escalation under the guidance of spatial biology distribution. Med. Phys. 32(6):1473–1484.CrossrefGoogle Scholar
  • Zarepisheh M, Long T, Li N, Tian Z, Romeijn HE, Jiang X, Jiang SB (2014) A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning. Med. Phys. 41(6 Part 1):061711.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.