An augmented parametric response map with consideration of image registration error: towards guidance of locally adaptive radiotherapy

Abstract

Parametric response map (PRM) analysis is a voxel-wise technique for predicting overall treatment outcome, which shows promise as a tool for guiding personalized locally adaptive radiotherapy (RT). However, image registration error (IRE) introduces uncertainty into this analysis which may limit its use for guiding RT. Here we extend the PRM method to include an IRE-related PRM analysis confidence interval and also incorporate multiple graded classification thresholds to facilitate visualization. A Gaussian IRE model was used to compute an expected value and confidence interval for PRM analysis. The augmented PRM (A-PRM) was evaluated using CT-perfusion functional image data from patients treated with RT for glioma and hepatocellular carcinoma. Known rigid IREs were simulated by applying one thousand different rigid transformations to each image set. PRM and A-PRM analyses of the transformed images were then compared to analyses of the original images (ground truth) in order to investigate the two methods in the presence of controlled IRE. The A-PRM was shown to help visualize and quantify IRE-related analysis uncertainty. The use of multiple graded classification thresholds also provided additional contextual information which could be useful for visually identifying adaptive RT targets (e.g. sub-volume boosts). The A-PRM should facilitate reliable PRM guided adaptive RT by allowing the user to identify if a patient's unique IRE-related PRM analysis uncertainty has the potential to influence target delineation.

A generalized parametric response mapping method for analysis of multi‐parametric imaging: A feasibility study with application to glioblastoma

“…, response heterogeneity can then be visualized via familiar three‐class maps in keeping with the original single‐parameter PRM method. To further improve visualization of intra‐tumor heterogeneity, a map could also be constructed which shows the position of voxels along v resp prior to application of the T MPRM similar to the multi‐threshold approach discussed in previous work …”

“…To further improve visualization of intra-tumor heterogeneity, a map could also be constructed which shows the position of voxels along v resp prior to application of the T MPRM similar to the multi-threshold approach discussed in previous work. 16 With respect to ease of use, multi-parametric analysis is necessarily more complex than single-parameter analysis since it involves simultaneous manipulation and interpretation of N > 2 image dimensions. The proposed method reduces this complexity by focusing analysis of multi-parametric variation along a single dimension described by the response vector v resp .…”

Review of the clinical benefits and implementation of peer review of treatment plans in undergraduate medical dosimetry and radiation therapy training

“…The delivery of radiation therapy has become more accurate with the introduction of image guidance and adaptive radiation therapy techniques, which promise improved accuracy of tumour targeting and avoidance of normal tissues. 1,2 The rapid changes in treatment techniques and the introduction of technologies such as Tomotherapy, CyberKnife and Gamma Knife, and the use of Stereotactic Body Radiation Therapy (SBRT) or stereotactic radiosurgery techniques has also added to the complexity of radiation treatment planning requiring advanced critical knowledge and skills. Moreover, the use of reduced margins in these techniques means that robust measures to ensure accuracy and precision needed to be introduced.…”

Development of a Multiparametric Voxel-Based Magnetic Resonance Imaging Biomarker for Early Cancer Therapeutic Response Assessment

“…In these studies, a larger tumor portion of PRM rCBV− predicted shorter overall survival. More importantly, PRM was described as a general analytical technique that could be applied to multimodal quantitative maps for voxel-based tracking of disease status ( 11 ) and progression and has since been applied across a wide variety of imaging parameters ( 28 – 30 ).…”