We would like to thank Dr. Glenn Bauman and Dr. Michael Lock for providing invaluable assistance in acquiring the patient data used in this study. This work was funded by the Ontario Research Fund for the Ontario Consortium for Adaptive Interventions in Radiation Oncology(OCAIRO).
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Chapter 4
A generalized approach towards
multi-parametric response mapping using
principal component analysis
∗
4.1
Introduction
The development of early treatment response prediction methods represents an impor- tant step towards improving cancer care via personalized adaptive treatment strate- gies. Once identified, ineffective treatments could be adapted, halted, or alternative therapies could be considered to improve patient outcomes or reduce toxicities. Such an approach may be of particular interest for aggressive cancers such as glioblas- toma multiforme where there is a limited therapeutic window and treatments may be associated with significant toxicity [1].
Parametric response mapping (PRM) has emerged as a powerful image-analysis technique which can be used to predict early treatment response [2]. Typically, PRM analysis involves a voxel-wise comparison of longitudinally acquired and spatially-
∗This chapter is adapted from the manuscript entitled “A generalized approach towards multi-
parametric response mapping using principal component analysis.” This manuscript is in preparation for submission to the journalMedical Physics.
aligned functional images. Tumour voxels are classified as increasing, decreasing, or not changing in function. The fraction of the tumour volume associated with one of these classes may then be found to correlate with a global measure of response such as overall survival [2]. To date, the predictive utility of the PRM method has been demonstrated for a variety of different pathologies such as glioblastoma, head and neck cancer, breast cancer, hepatocellular carcinoma and chronic obstructive pulmonary disease (COPD) [2–7]. Due to the voxel-wise nature of PRM analysis, this technique may also have potential for guiding personalized locally adaptive interventions such as adaptive radiotherapy (RT).
There are several key advantages to the PRM method. First, it involves voxel- wise image analysis and incorporates spatial correspondences to describe the spatial heterogeneity in intra-tumour response. Second, the PRM enables intuitive visual- ization of this heterogeneity. Third, PRM analysis is straightforward to implement and interpret, providing a uniquely accessible and effective means of probing image data for treatment response biomarkers.
However to date the PRM method has been almost exclusively applied to longi- tudinally acquired pairs of single-parameter image data. Galbanet al. [8] previously investigated the benefit of combining two independent single-parameter analyses for a cohort of patients diagnosed with glioblastoma. Independent PRM analyses were performed on diffusion and perfusion image data and then the resulting scalar-valued PRM biomarkers were combined via multivariate logistic regression. This two-variate approach was shown to improve prediction of overall survival when compared to PRM analysis of diffusion or perfusion data alone, highlighting the potential of multi- parametric response prediction. However, since the two PRM analyses were performed independently, spatial correspondences between the diffusion and perfusion data were not included in the analysis. Consequently, spatial-heterogeneity in multi-parametric
treatment response cannot be taken into account nor visualized. Incorporation of spatial correspondences between functional imaging at two time points was a key driver of the success of the original PRM method. Therefore, an analogous multi- parametric approach may have potential for improving treatment response prediction and assist in finding new treatment response biomarkers. A fully voxel-wise approach towards multi-parametric treatment response prediction using PRM may also facil- itate investigation into voxel-wise treatment response prediction for the purposes of guiding locally adaptive interventions.
Here our goal was to develop a unified multi-parametric response analysis frame- work which extends the key advantages of the single-parameter PRM method to analysis of multi-parametric data. We introduce a generalizable N-dimensional ap- proach towards multi-parametric response mapping (MPRM) which takes spatial het- erogeneity in multi-parametric response into account and supports visualization and interpretation of this heterogeneity. For preliminary demonstration, the proposed method is applied to a multi-parametric image dataset acquired from a group of pa- tients treated for high-grade glioma. Representative MPRMs and prediction of overall survival are demonstrated with comparison to single-parameter PRM analyses.