1,354
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Using MRI to measure position and anatomy changes and assess their impact on the accuracy of hyperthermia treatment planning for cervical cancer

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Article: 2151648 | Received 06 Jul 2022, Accepted 18 Nov 2022, Published online: 19 Dec 2022

References

  • van der Zee J. Heating the patient: a promising approach? Ann Oncol. 2002;13(8):1173–1184.
  • Wust P, Hildebrandt B, Sreenivasa G, et al. Hyperthermia in combined treatment of cancer. Lancet Oncol. 2002;3(8):487–497.
  • Yea JW, Park JW, Oh SA, et al. Chemoradiotherapy with hyperthermia versus chemoradiotherapy alone in locally advanced cervical cancer: a systematic review and Meta-Analysis. Int J Hyperthermia. 2021;38(1):1333–1340.
  • Franckena M, Stalpers LJA, Koper PCM, 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 Radiat Oncol Biol Phys. 2008;70(4):1176–1182.
  • Zee J, Van Der; González DG, Rhoon GC, et al. Comparison of radiotherapy alone with radiotherapy plus hyperthermia in locally advanced pelvic tumors. Lancet. 2000;355:1119–1125.
  • Franckena M, Fatehi D, Bruijne M D, et al. Hyperthermia dose-effect relationship in 420 patients with cervical cancer treated with combined radiotherapy and hyperthermia. Eur J Cancer. 2009;45(11):1969–1978.
  • Kroesen M, Mulder HT, van Holthe JML, et al. Confirmation of thermal dose as a predictor of local control in cervical carcinoma patients treated with state-of-the-Art radiation therapy and hyperthermia. Radiother Oncol. 2019;140:150–158.
  • VilasBoas‐Ribeiro I, Nouwens SAN, Curto S, et al. POD–kalman filtering for improving noninvasive 3D temperature monitoring in MR‐guided hyperthermia. Med. Phys. 2022;49(8):4955–4970.
  • Adibzadeh F, Sumser K, Curto S, et al. Systematic review of pre-clinical and clinical devices for magnetic resonance-guided radiofrequency hyperthermia. Int J Hyperthermia. 2020;37(1):15–27.
  • Gellermann J, Wlodarczyk W, Ganter H, et al. A practical approach to thermography in a hyperthermia/magnetic resonance hybrid system: validation in a heterogeneous phantom. Int J Radiat Oncol Biol Phys. 2005;61(1):267–277.
  • Gellermann J, Wlodarczyk W, Feussner A, et al. Methods and potentials of magnetic resonance imaging for monitoring radiofrequency hyperthermia in a hybrid system. Int J Hyperthermia. 2005;21(6):497–513.
  • Nouwens SAN, Paulides MM, Fölker J, et al. Integrated thermal and magnetic susceptibility modeling for Air-Motion artifact correction in proton resonance frequency shift thermometry. Int J Hyperthermia. 2022;39(1):967–976.
  • VilasBoas-Ribeiro I, Curto S, van Rhoon GC, et al. MR thermometry accuracy and prospective Imaging-Based patient selection in MR-Guided hyperthermia treatment for locally advanced cervical cancer. Cancers (Basel). 2021;13(14):3503.
  • Unsoeld M, Lamprecht U, Traub F, et al. MR thermometry data correlate with pathological response for soft tissue sarcoma of the lower extremity in a single center analysis of prospectively registered patients. Cancers (Basel). 2020;12(4):959.
  • Gellermann J, Wlodarczyk W, Hildebrandt B, et al. Noninvasive magnetic resonance thermography of recurrent rectal carcinoma in a 1.5 tesla hybrid system. Cancer Res. 2005;65(13):5872–5880.
  • 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(6):1373–1382.
  • 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(6):1619–1635.
  • Kok HP, Navarro F, Strigari L, et al. Locoregional hyperthermia of Deep-Seated tumours applied with capacitive and radiative systems: a simulation study. Int J Hyperthermia. 2018;34(6):714–730.
  • Paulides MM, Rodrigues DB, Bellizzi GG, et al. ESHO benchmarks for computational modeling and optimization in hyperthermia therapy. Int J Hyperthermia. 2021;38(1):1425–1442.
  • Aklan B, Zilles B, Paprottka P, et al. Regional deep hyperthermia: quantitative evaluation of predicted and direct measured temperature distributions in patients with High-Risk extremity Soft-Tissue sarcoma. Int J Hyperthermia. 2019;36(1):170–185.
  • Wust P, Stahl H, Löffel J, et al. Clinical, physiological and anatomical determinants for radiofrequency hyperthermia. Int J Hyperthermia. 1995;11(2):151–167.
  • Vilasboas-Ribeiro I, van Rhoon GC, Drizdal T, et al. Impact of number of segmented tissues on SAR prediction accuracy in deep pelvic hyperthermia treatment planning. Cancers (Basel). 2020;12(9):2646.
  • Wust P, Nadobny J, Seebass M, et al. Influence of patient models and numerical methods on predicted power deposition patterns. Int. J. Hyperth. 1999;15(6):519–540.
  • Bellizzi GG, Sumser K, VilasBoas-Ribeiro I, et al. Standardization of patient modeling in hyperthermia simulation studies: introducing the erasmus virtual patient repository. Int J Hyperthermia. 2020;37(1):608–616.
  • Schooneveldt G, Kok HP, Bakker A, et al. Clinical validation of a novel thermophysical bladder model designed to improve the accuracy of hyperthermia treatment planning in the pelvic region. Int J Hyperthermia. 2018;35(1):383–397.
  • Canters RAM, 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(1):74–80.
  • Paulides MM, Stauffer PR, Neufeld E, et al. Simulation techniques in hyperthermia treatment planning. Int J Hyperthermia. 2013;29(4):346–357.
  • Kok HP, Wust P, Stauffer PR, et al. Current state of the art of regional hyperthermia treatment planning: a review. Radiat Oncol. 2015;10(1):1–14.
  • van Haaren PMA, Kok HP, van den Berg CAT, et al. On verification of hyperthermia treatment planning for cervical carcinoma patients. Int J Hyperthermia. 2007;23(3):303–314.
  • 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(2):407–419.
  • Kok HP, Ciampa S, De Kroon-Oldenhof R, et al. Toward online adaptive hyperthermia treatment planning: correlation between measured and simulated specific absorption rate changes caused by phase steering in patients. Int J Radiat Oncol Biol Phys. 2014;90(2):438–445.
  • Rijnen Z, Bakker JF, Canters RAM, et al. Clinical integration of software tool VEDO for adaptive and quantitative application of phased array hyperthermia in the head and neck. Int J Hyperthermia. 2013;29(3):181–193.
  • 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(4):1039–1047.
  • 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. 2018;34(7):1082–1091.
  • Franckena M, Canters R, Termorshuizen F, et al. Clinical implementation of hyperthermia treatment planning guided steering: a cross over trial to assess its current contribution to treatment quality. Int J Hyperthermia. 2010;26(2):145–157.
  • Wiersma J, van Wieringen N, Crezee H, et al. Delineation of potential hot spots for hyperthermia treatment planning optimisation. Int J Hyperthermia. 2007;23(3):287–301.
  • 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(11):3233–3250.
  • Gavazzi S, van Lier ALHMW, Zachiu C, et al. Advanced Patient-Specific hyperthermia treatment planning. Int J Hyperthermia. 2020;37(1):992–1007.
  • Ribeiro IVB, Van Holthe N, Van Rhoon GC, et al. Impact of segmentation detail in hyperthermia treatment planning. Cancer (Basel). 2020;12(9):2646.
  • Canters RAM, 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(12):3923–3936.
  • Gellermann J, Göke J, Figiel R, et al. Simulation of different applicator positions for treatment of a presacral tumour. Int J Hyperthermia. 2007;23(1):37–47.
  • Craciunescu OI, Stauffer PR, Soher BJ, et al. Accuracy of real time noninvasive temperature measurements using magnetic resonance thermal imaging in patients treated for high grade extremity soft tissue sarcomas. Med Phys. 2009;36(11):4848–4858.
  • Stauffer PR, Craciunescu OI, Maccarini PF, et al. Clinical utility of magnetic resonance thermal imaging (MRTI) for realtime guidance of deep hyperthermia. Proceedings Volume 7181, Energy-based Treatment of Tissue and Assessment V. 2009. p 7181.
  • Feddersen TV, Hernandez-Tamames JA, Franckena M, et al. Clinical performance and future potential of magnetic resonance thermometry in hyperthermia. Cancers (Basel). 2020;13(1):31.
  • Canters RAM, Paulides MM, Franckena MF, et al. Implementation of treatment planning in the routine clinical procedure of regional hyperthermia treatment of cervical cancer: an overview and the rotterdam experience. Int J Hyperthermia. 2012;28(6):570–581.
  • Canters RAM, Wust P, Bakker JF, et al. A literature survey on indicators for characterisation and optimisation of SAR distributions in deep hyperthermia, a plea for standardisation. Int J Hyperthermia. 2009;25(7):593–608.
  • Kiser KJ, Barman A, Stieb S, et al. Novel autosegmentation spatial similarity metrics capture the time required to correct segmentations better than traditional metrics in a thoracic cavity segmentation workflow. J Digit Imaging. 2021;34(3):541–553.
  • Yeghiazaryan V, Voiculescu I. Family of boundary overlap metrics for the evaluation of medical image segmentation. J Med Imag. 2018;5(01):1.
  • Aydin OU, Taha AA, Hilbert A, et al. On the usage of average hausdorff distance for segmentation performance assessment: hidden error when used for ranking. Eur Radiol Exp. 2021;5(1):4.
  • Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15(1).
  • Hasgall P, Di Gennaro F, Baumgartner C, et al. IT’IS database for thermal and electromagnetic parameters of biological tissues itis.swiss/database 2018 [accessed 22 April 2020]. Available from: https://itis.swiss/virtual-population/tissue-properties/database/dielectric-properties/
  • Bruggmoser G, Bauchowitz S, Canters R, et al. Quality assurance for clinical studies in regional deep hyperthermia. Strahlenther Onkol. 2011;187(10):605–610.
  • Bruggmoser G, Bauchowitz S, Canters R, et al. Guideline for the clinical application, documentation and analysis of clinical studies for regional deep hyperthermia. Strahlenther Onkol. 2012;188(S2):198–211.
  • Oberacker E, Kuehne A, Nadobny J, et al. Radiofrequency applicator concepts for simultaneous MR imaging and hyperthermia treatment of glioblastoma multiforme. Curr Dir Biomed Eng. 2017;3(2):473–477.
  • Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155–163.
  • Iglesias JE, Sabuncu MR. Multi-Atlas segmentation of biomedical images: a survey. Med Image Anal. 2015;24(1):205–219.
  • Fortunati V, Verhaart RF, Niessen WJ, et al. Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning. Phys Med Biol. 2015;60(16):6547–6562.
  • Teguh DN, Levendag PC, Voet PWJ, et al. Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (swallowing/mastication) structures in the head and neck. Int J Radiat Oncol Biol Phys. 2011;81(4):950–957.
  • Ju Z, Wu Q, Yang W, et al. Automatic segmentation of pelvic organs-at-Risk using a fusion network model based on limited training samples. Acta Oncol. 2020;59(8):933–939.
  • Kalantar R, Lin G, Winfield JM, et al. Automatic segmentation of pelvic cancers using deep learning: state-of-the-Art approaches and challenges. Diagnostics. 2021;11(11):1964.
  • Estrada S, Lu R, Conjeti S, et al. FatSegNet: a fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI. Magn Reson Med. 2020;83(4):1471–1483.
  • O’Connor LM, Dowling JA, Choi JH, et al. Validation of an MRI-Only planning workflow for definitive pelvic radiotherapy. Radiat Oncol. 2022;17(1):1–11.
  • Farjam R, Tyagi N, Deasy JO, et al. Dosimetric evaluation of an Atlas-Based synthetic CT generation approach for MR-Only radiotherapy of pelvis anatomy. J Appl Clin Med Phys. 2019;20(1):101–109.
  • Guerreiro F, Burgos N, Dunlop A, et al. Evaluation of a Multi-Atlas CT synthesis approach for MRI-Only radiotherapy treatment planning. Phys Med. 2017;35:7–17.
  • Bentvelzen LG, Cronholm RO, Karlsson A, et al. Auto-Segmentation of Pelvic Structures Using MRI Planner; A Quantitative Evaluation. No. 2020. 1–5.
  • Savenije MHF, Maspero M, Sikkes GG, et al. Clinical implementation of MRI-Based organs-at-Risk Auto-Segmentation with convolutional networks for prostate radiotherapy. Radiat Oncol. 2020;15(1):1–12.
  • Liu Z, Liu X, Guan H, et al. Development and validation of a deep learning algorithm for Auto-Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy. Radiother Oncol. 2020;153:172–179.
  • Chen M, Wu S, Zhao W, et al. Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy. Cancer/Radiotherapie. 2022;26(3):494–501.
  • Min H, Dowling J, Jameson MG, et al. Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial. Phys Med Biol. 2021;66(19):195008.
  • Kamer JBVD, Wieringen NV, Leeuw aaCD, et al. The significance of accurate dielectric tissue data for hyperthermia treatment planning. Int J Hyperthermia. 2001;17(2):123–142.
  • McIntosh R, Anderson V. A comprehensive tissue properties database provided for the thermal assessment of a human At rest. Biophys Rev Lett. 2013;08(01n02):99–100.