1,651
Views
16
CrossRef citations to date
0
Altmetric
Original Articles

Regional deep hyperthermia: quantitative evaluation of predicted and direct measured temperature distributions in patients with high-risk extremity soft-tissue sarcoma

, , , , , , & show all
Pages 169-184 | Received 28 Nov 2017, Accepted 31 Oct 2018, Published online: 19 Feb 2019

References

  • Issels RD, Lindner LH, Verweij J, et al. Neo-adjuvant chemotherapy alone or with regional hyperthermia for localised high-risk soft-tissue sarcoma: a randomised phase 3 multicentre study. Lancet Oncol. 2010;11:561–570.
  • Franckena M, Stalpers LJ, Koper PC, et al. Long-term improvement in treatment outcome after radiotherapy and hyperthermia in locoregionally advanced cervix cancer: an update of the Dutch Deep Hyperthermia Trial. Int J Radiation Oncol, Biol, Phys. 2008;70:1176–1182.
  • van der Zee J, Gonzalez Gonzalez D, van Rhoon GC, et al. Comparison of radiotherapy alone with radiotherapy plus hyperthermia in locally advanced pelvic tumours: a prospective, randomised, multicentre trial. Dutch Deep Hyperthermia Group. Lancet. 2000;355:1119.
  • Issels RD, Lindner LH, Verweij J, et al. Effect of neoadjuvant chemotherapy plus regional hyperthermia on long-term outcomes among patients with localized high-risk soft tissue sarcoma: the EORTC 62961-ESHO 95 randomized clinical trial. JAMA Oncol. 2018; 4:483–492.
  • Gellermann J, Hildebrandt B, Issels R, et al. Noninvasive magnetic resonance thermography of soft tissue sarcomas during regional hyperthermia: correlation with response and direct thermometry. Cancer. 2006;107:1373–1382.
  • Wust P, Rau B, Gellerman J, et al. Radiochemotherapy and hyperthermia in the treatment of rectal cancer. Recent Results Cancer Res. 1998;146:175–191.
  • Oleson JR, Samulski TV, Leopold KA, et al. Sensitivity of hyperthermia trial outcomes to temperature and time: implications for thermal goals of treatment. Int J Radiat Oncol Biol Phys. 1993;25:289–297.
  • Sapareto SA, Dewey WC. Thermal dose determination in cancer therapy. Int J Radiat Oncol Biol Phys. 1984;10:787–800.
  • Wust P, Cho CH, Hildebrandt B, et al. Thermal monitoring: invasive, minimal-invasive and non-invasive approaches. Int J Hyperthermia. 2006;22:255–262.
  • Strobl FF, Azam H, Schwarz JB, et al. CT fluoroscopy-guided closed-tip catheter placement before regional hyperthermia treatment of soft tissue sarcomas: 5-Year experience in 35 consecutive patients. Int J Hyperthermia. 2016;32:151–158.
  • Oleson JR, Dewhirst MW, Harrelson JM, et al. Tumor temperature distributions predict hyperthermia effect. Int J Radiat Oncol Biol Phys. 1989;16:559–570.
  • Issels R. Regional deep hyperthermia of sarcoma for improving local tumor control. Langenbecks Arch Chir Suppl II Verh Dtsch Ges Chir. 1990;1:923–927.
  • Ludemann L, Wlodarczyk W, Nadobny J, et al. Non-invasive magnetic resonance thermography during regional hyperthermia. Int J Hyperthermia. 2010;26:273–282.
  • Winter L, Oberacker E, Paul K, et al. Magnetic resonance thermometry: methodology, pitfalls and practical solutions. Int J Hyperthermia. 2016;32:63–75.
  • Kok HP, Wust P, Stauffer PR, et al. Current state of the art of regional hyperthermia treatment planning: a review. Radiat Oncol. 2015;10:196.
  • Kok HP, Van Haaren PM, Van de Kamer JB, et al. High-resolution temperature-based optimization for hyperthermia treatment planning. Phys Med Biol. 2005;50:3127–3141.
  • Kok HP, van den Berg CA, Bel A, et al. Fast thermal simulations and temperature optimization for hyperthermia treatment planning, including realistic 3D vessel networks. Med Phys. 2013;40:103303.
  • Das SK, Clegg ST, Samulski TV. Computational techniques for fast hyperthermia temperature optimization. Med Phys. 1999;26:319–328.
  • Pa, Di Gennaro F H, Baumgarter C, et al. [IT’IS Database for thermal and electromagnetic parameters of biological tissues]. Version 3.0; 2015.
  • Gabriel C, Gabriel S, Corthout E. The dielectric properties of biological tissues: I. Literature survey. Phys Med Biol. 1996;41:2231–2249.
  • Kok HP, Korshuize-van Straten L, Bakker A, et al. Online adaptive hyperthermia treatment planning during locoregional heating to suppress treatment-limiting hot spots. Int J Radiat Oncol Biol Phys. 2017; 99:1039–1047.
  • Kok HP, Kotte A, Crezee J. Planning, optimisation and evaluation of hyperthermia treatments. Int J Hyperthermia. 2017;33:593–607.
  • Paulides MM, Van Rhoon GC. Towards developing effective hyperthermia treatment for tumours in the nasopharyngeal region. Int J Hyperthermia. 2011;27:523–525.
  • Gellermann J, Wust P, Stalling D, et al. Clinical evaluation and verification of the hyperthermia treatment planning system hyperplan. Int J Radiat Oncol Biol Phys. 2000;47:1145–1156.
  • Sreenivasa G, Gellermann J, Rau B, et al. Clinical use of the hyperthermia treatment planning system HyperPlan to predict effectiveness and toxicity. Int J Radiat Oncol Biol Phys. 2003;55:407–419.
  • Wust P, Fahling H, Wlodarczyk W, et al. Antenna arrays in the SIGMA-eye applicator: interactions and transforming networks. Med Phys. 2001;28:1793–1805.
  • Canters RA, Paulides MM, Franckena M, et al. Benefit of replacing the Sigma-60 by the Sigma-Eye applicator. A Monte Carlo-based uncertainty analysis. Strahlenther Onkol. 2013;189:74–80.
  • Klein S, Staring M, Murphy K, et al. elastix: a toolbox for intensity based medical image registration. IEEE Trans Med Imaging. 2010;29:196–205.
  • Aklan B, Gierse P, Hartmann J, et al. Influence of patient mispositioning on SAR distribution and simulated temperature in regional deep hyperthermia. Phys Med Biol. 2017;62:4929–4945.
  • Gellermann J, Goke J, Figiel R, et al. Simulation of different applicator positions for treatment of a presacral tumour. Int J Hyperthermia. 2007; 23:37–47.
  • Canters RA, Franckena M, Paulides MM, et al. Patient positioning in deep hyperthermia: influences of inaccuracies, signal correction possibilities and optimization potential. Phys Med Biol. 2009;54:3923–3936.
  • Wust P, Fahling H, Jordan A, et al. Development and testing of SAR-visualizing phantoms for quality control in RF hyperthermia. International journal of hyperthermia: the official journal of European Society for Hyperthermic Oncology. North Am Hyperthermia Group. 1994;10:127–142.
  • R RDCT. A Language and Environment for Statistical Computing. Vienna (Austria): R Foundation for Statistical Computing; 2013. http://www.R-project.org.
  • Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–310.
  • Song CW. Effect of local hyperthermia on blood flow and microenvironment: a review. Cancer Res. 1984;44:4721s–4730s.
  • de Greef M, Kok HP, Correia D, et al. Uncertainty in hyperthermia treatment planning: the need for robust system design. Phys Med Biol. 2011;56:3233–3250.
  • Kok HP, Korshuize-van Straten L, Bakker A, et al. Feasibility of on-line temperature-based hyperthermia treatment planning to improve tumour temperatures during locoregional hyperthermia. Int J Hyperthermia 2017;16:1–10.
  • De Greef M, Kok HP, Bel A, et al. 3D versus 2D steering in patient anatomies: a comparison using hyperthermia treatment planning. Int J Hyperthermia. 2011;27:74–85.
  • Schooneveldt G, Kok HP, Balidemaj E, et al. Improving hyperthermia treatment planning for the pelvis by accurate fluid modeling. Med Phys. 2016;43:5442.
  • Balidemaj E, Kok HP, Schooneveldt G, et al. Hyperthermia treatment planning for cervical cancer patients based on electrical conductivity tissue properties acquired in vivo with EPT at 3 T MRI. Int J Hyperthermia. 2016;32:558–568.
  • Aklan B, Hartmann J, Zink D, et al. Regional deep hyperthermia: impact of observer variability in CT-based manual tissue segmentation on simulated temperature distribution. Phys Med Biol. 2017;62:4479–4495.
  • Erdmann B, Lang J, Seebass M. Optimization of temperature distributions for regional hyperthermia based on a nonlinear heat transfer model. Annals NY Acad Sci. 1998;858:36–46.
  • Cheng KS, Stakhursky V, Craciunescu OI, et al. Fast temperature optimization of multi-source hyperthermia applicators with reduced-order modeling of ‘virtual sources’. Phys Med Biol. 2008;53:1619–1635.