An Investigation of the Conformity, Feasibility, and Expected Clinical Benefits of Multiparametric MRI-guided Dose Painting Radiotherapy in Glioblastoma

Overview

Journal Neurooncol Adv

Publisher Oxford University Press

Date 2022 Sep 15

PMID 36105390

Authors

Caterina Brighi

Paul J Keall

Lois C Holloway

Amy Walker

Brendan Whelan

Philip C de Witt Hamer

Niels Verburg

Farhannah Aly

Cathy Chen

Eng-Siew Koh

David E J Waddington

Affiliations

Soon will be listed here.

Abstract

Background: New technologies developed to improve survival outcomes for glioblastoma (GBM) continue to have limited success. Recently, image-guided dose painting (DP) radiotherapy has emerged as a promising strategy to increase local control rates. In this study, we evaluate the practical application of a multiparametric MRI model of glioma infiltration for DP radiotherapy in GBM by measuring its conformity, feasibility, and expected clinical benefits against standard of care treatment.

Methods: Maps of tumor probability were generated from perfusion/diffusion MRI data from 17 GBM patients via a previously developed model of GBM infiltration. Prescriptions for DP were linearly derived from tumor probability maps and used to develop dose optimized treatment plans. Conformity of DP plans to dose prescriptions was measured via a quality factor. Feasibility of DP plans was evaluated by dose metrics to target volumes and critical brain structures. Expected clinical benefit of DP plans was assessed by tumor control probability. The DP plans were compared to standard radiotherapy plans.

Results: The conformity of the DP plans was >90%. Compared to the standard plans, DP (1) did not affect dose delivered to organs at risk; (2) increased mean and maximum dose and improved minimum dose coverage for the target volumes; (3) reduced minimum dose within the radiotherapy treatment margins; (4) improved local tumor control probability within the target volumes for all patients.

Conclusions: A multiparametric MRI model of GBM infiltration can enable conformal, feasible, and potentially beneficial dose painting radiotherapy plans.

Citing Articles

Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery.

Pandey S, Kutuk T, Abdalah M, Stringfield O, Ravi H, Mills M Phys Imaging Radiat Oncol. 2024; 31:100602.

PMID: 39040435 PMC: 11261135. DOI: 10.1016/j.phro.2024.100602.

References

Kim M, Aryal M, Sun Y, Parmar H, Li P, Schipper M . Response assessment during chemoradiation using a hypercellular/hyperperfused imaging phenotype predicts survival in patients with newly diagnosed glioblastoma. Neuro Oncol. 2021; 23(9):1537-1546. PMC: 8408852. DOI: 10.1093/neuonc/noab038. View

Jiang H, Yu K, Li M, Cui Y, Ren X, Yang C . Classification of Progression Patterns in Glioblastoma: Analysis of Predictive Factors and Clinical Implications. Front Oncol. 2020; 10:590648. PMC: 7673412. DOI: 10.3389/fonc.2020.590648. View

Tsien C, Moughan J, Michalski J, Gilbert M, Purdy J, Simpson J . Phase I three-dimensional conformal radiation dose escalation study in newly diagnosed glioblastoma: Radiation Therapy Oncology Group Trial 98-03. Int J Radiat Oncol Biol Phys. 2008; 73(3):699-708. PMC: 2913423. DOI: 10.1016/j.ijrobp.2008.05.034. View

Smith S . Fast robust automated brain extraction. Hum Brain Mapp. 2002; 17(3):143-55. PMC: 6871816. DOI: 10.1002/hbm.10062. View

Wen P, Weller M, Quant Lee E, Alexander B, Barnholtz-Sloan J, Barthel F . Glioblastoma in adults: a Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions. Neuro Oncol. 2020; 22(8):1073-1113. PMC: 7594557. DOI: 10.1093/neuonc/noaa106. View

Brighi C, Verburg N, Koh E, Walker A, Chen C, Pillay S . Repeatability of radiotherapy dose-painting prescriptions derived from a multiparametric magnetic resonance imaging model of glioblastoma infiltration. Phys Imaging Radiat Oncol. 2022; 23:8-15. PMC: 9207284. DOI: 10.1016/j.phro.2022.06.004. View

Jafari-Khouzani K, Emblem K, Kalpathy-Cramer J, Bjornerud A, Vangel M, Gerstner E . Repeatability of Cerebral Perfusion Using Dynamic Susceptibility Contrast MRI in Glioblastoma Patients. Transl Oncol. 2015; 8(3):137-46. PMC: 4486737. DOI: 10.1016/j.tranon.2015.03.002. View

Holloway L, Miller J, Kumar S, Whelan B, Vinod S . Comp Plan: A computer program to generate dose and radiobiological metrics from dose-volume histogram files. Med Dosim. 2012; 37(3):305-9. DOI: 10.1016/j.meddos.2011.11.004. View

Li M, Zhang Q, Yang K . Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment. Front Oncol. 2021; 11:645177. PMC: 8429950. DOI: 10.3389/fonc.2021.645177. View

10.

Thorwarth D, Notohamiprodjo M, Zips D, Muller A . Personalized precision radiotherapy by integration of multi-parametric functional and biological imaging in prostate cancer: A feasibility study. Z Med Phys. 2016; 27(1):21-30. DOI: 10.1016/j.zemedi.2016.02.002. View

11.

Jungk C, Warta R, Mock A, Friauf S, Hug B, Capper D . Location-Dependent Patient Outcome and Recurrence Patterns in IDH1-Wildtype Glioblastoma. Cancers (Basel). 2019; 11(1). PMC: 6356480. DOI: 10.3390/cancers11010122. View

12.

Rodal J, Sovik A, Malinen E . Influence of MLC leaf width on biologically adapted IMRT plans. Acta Oncol. 2010; 49(7):1116-23. DOI: 10.3109/0284186X.2010.498832. View

13.

Yaniv Z, Lowekamp B, Johnson H, Beare R . SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research. J Digit Imaging. 2017; 31(3):290-303. PMC: 5959828. DOI: 10.1007/s10278-017-0037-8. View

14.

Whitfield G, Kennedy S, Djoukhadar I, Jackson A . Imaging and target volume delineation in glioma. Clin Oncol (R Coll Radiol). 2014; 26(7):364-76. DOI: 10.1016/j.clon.2014.04.026. View

15.

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P . The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013; 26(6):1045-57. PMC: 3824915. DOI: 10.1007/s10278-013-9622-7. View

16.

Lipkova J, Angelikopoulos P, Wu S, Alberts E, Wiestler B, Diehl C . Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference. IEEE Trans Med Imaging. 2019; 38(8):1875-1884. PMC: 7170051. DOI: 10.1109/TMI.2019.2902044. View

17.

Duprez F, De Neve W, De Gersem W, Coghe M, Madani I . Adaptive dose painting by numbers for head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2010; 80(4):1045-55. DOI: 10.1016/j.ijrobp.2010.03.028. View

18.

van der Heide U, Houweling A, Groenendaal G, Beets-Tan R, Lambin P . Functional MRI for radiotherapy dose painting. Magn Reson Imaging. 2012; 30(9):1216-23. PMC: 5134673. DOI: 10.1016/j.mri.2012.04.010. View

19.

Orlandi M, Botti A, Sghedoni R, Cagni E, Ciammella P, Iotti C . Feasibility of voxel-based Dose Painting for recurrent Glioblastoma guided by ADC values of Diffusion-Weighted MR imaging. Phys Med. 2016; 32(12):1651-1658. DOI: 10.1016/j.ejmp.2016.11.106. View

20.

GRAY L, CONGER A, Ebert M, HORNSEY S, Scott O . The concentration of oxygen dissolved in tissues at the time of irradiation as a factor in radiotherapy. Br J Radiol. 1953; 26(312):638-48. DOI: 10.1259/0007-1285-26-312-638. View