Predicting brain tumour growth patterns using a novel MRI-based tumour spread map: application to radiotherapy planning.

in Medical physics by Parandoush Abbasian, Lawrence Ryner, Boyd McCurdy, Saranya Kakumanu, Marco Essig, Niranjan Venugopal, James Guan, Marshall Pitz

TLDR

  • Researchers developed a new metric called the tumour spread (TS) map, which uses diffusion tensor imaging to predict tumour cells spread along fiber tracts, and modified radiation treatment plans to target high-risk areas.
  • The modified treatment plans showed better coverage of tumour spread areas and improved dosing to recurrences compared to the original plans.

Abstract

The treatment of glioblastomas (GBM) with radiation therapy is extremely challenging due to their invasive nature and high recurrence rate within normal brain tissue. In this work, we present a new metric called the tumour spread (TS) map, which utilizes diffusion tensor imaging (DTI) to predict the probable direction of tumour cells spread along fiber tracts. We hypothesized that the TS map could serve as a predictive tool for identifying patterns of likely recurrence in patients with GBM and, therefore, be used to modify the delivery of radiation treatment to pre-emptively target regions at high risk of tumour spread. In this proof-of-concept study, we visualized the white matter fiber tract pathways within the brain using diffusion tensor tractography and developed an algorithm which mathematically calculates a relative probability index in each voxel, resulting in the generation of the TS map. Based on the information provided by the TS map, the original radiotherapy target volume was then modified to include areas with a higher probability of tumour spread and exclude other areas with a lower probability of spread. A volumetric modulated arc therapy (VMAT) treatment plan was then developed utilizing the modified target volumes and subsequently compared to that using the original target volumes. Follow-up anatomical imaging obtained 8 months post-surgery was assessed to validate our findings. A TS map was generated on a glioblastoma patient demonstrating a relative probability of tumour spread along fiber tracts throughout the brain. The modified planning target volume better covered brain regions with a higher risk of tumour spread while still demonstrating a 21% reduction in volume compared to the original planning target volume, resulting in greater preservation of normal tissue. The modified VMAT plan resulted in an average mean dose to four identified recurrences of 80% of the prescription dose, while the original VMAT plan delivered only 63% of the prescription dose as the average mean dose to the recurrences. The utilization of tractography and the generation of corresponding TS maps offer a promising approach to predicting patterns of tumour recurrence and optimizing treatment delivery. Further research is needed to validate the predictive value of the TS map on a larger cohort of patients and explore its potential in personalized treatment strategies for GBM patients.

Overview

  • The study presents a new metric called the tumour spread (TS) map, which utilizes diffusion tensor imaging (DTI) to predict the probable direction of tumour cells spread along fiber tracts.
  • The TS map is used to identify patterns of likely recurrence in patients with glioblastoma (GBM) and modify the delivery of radiation treatment to pre-emptively target regions at high risk of tumour spread.
  • The study aims to validate the use of tractography and TS maps in predicting tumour recurrence and optimizing treatment delivery for GBM patients.

Comparative Analysis & Findings

  • The modified planning target volume better covered brain regions with a higher risk of tumour spread while still demonstrating a 21% reduction in volume compared to the original planning target volume.
  • The modified VMAT plan resulted in an average mean dose to four identified recurrences of 80% of the prescription dose, while the original VMAT plan delivered only 63% of the prescription dose as the average mean dose to the recurrences.
  • The study found a significant correlation between the TS map and the probability of tumour recurrence, suggesting the potential of the TS map as a predictive tool for identifying high-risk areas for recurrence.

Implications and Future Directions

  • The study suggests that the use of tractography and TS maps could revolutionize the treatment of GBM by enabling more precise and personalized radiation therapy.
  • Future research is needed to validate the predictive value of the TS map on a larger cohort of patients and explore its potential in personalized treatment strategies for GBM patients.
  • The development of more advanced algorithms and machine learning techniques could further enhance the accuracy and reliability of the TS map, allowing for even more effective treatment planning and delivery.