CADOnc ⓒ: An integrated toolkit for evaluating radiation therapy related changes in the prostate using multiparametric MRI

Abstract

The use of multi-parametric Magnetic Resonance Imaging (T2-weighted, MR Spectroscopy (MRS), Diffusion-weighted (DWI)) has recently shown great promise for diagnosing and staging prostate cancer (CaP) in vivo. Such imaging has also been utilized for evaluating the early effects of radiotherapy (RT) (e.g. intensity-modulated radiation therapy (IMRT), proton beam therapy, brachytherapy) in the prostate with the overarching goal being to successfully predict short- and long-term patient outcome. Qualitative examination of post-RT changes in the prostate using MRI is subject to high inter- and intra-observer variability. Consequently, there is a clear need for quantitative image segmentation, registration, and classification tools for assessing RT changes via multi-parametric MRI to identify (a) residual disease, and (b) new foci of cancer (local recurrence) within the prostate. In this paper, we present a computerized image segmentation, registration, and classification toolkit called CADOnc©, and leverage it for evaluating (a) spatial extent of disease pre-RT, and (b) post-RT related changes within the prostate. We demonstrate the applicability of CADOnc© in studying IMRTrelated changes using a cohort of 7 multi-parametric (T2w, MRS, DWI) prostate MRI patient datasets. First, the different MRI protocols from pre- and post-IMRT MRI scans are affinely registered (accounting for gland shrinkage), followed by automated segmentation of the prostate capsule using an active shape model. A number of feature extraction schemes are then applied to extract multiple textural, metabolic, and functional MRI attributes on a per-voxel basis. An AUC of 0.7132 was achieved for automated detection of CaP on pre-IMRT MRI (via integration of T2w, DWI, MRS features); evaluated on a per-voxel basis against radiologist-derived annotations. CADOnc© also successfully identified a total of 40 out of 46 areas where disease-related changes (both absence and recurrence) occurred post-IMRT, based on changes in the expression of quantitative MR imaging biomarkers. CADOnc© thus provides an integrated platform of quantitative analysis tools to evaluate treatment response in vivo, based on multi-parametric MRI data.

Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings

“…Our group 8,16,17 has leveraged these qualitative characteristics within novel quantitative schemes for per-voxel evaluation and MRI signature construction to differentiate between possible radiation treatment outcomes (successful, unsuccessful, and recurrence). In our recent work, 18 a fiducial driven based registration scheme was presented to evaluate LITT changes for GBM and epilepsy patients.…”

“…In our recent work, 18 a fiducial driven based registration scheme was presented to evaluate LITT changes for GBM and epilepsy patients. Similarly, Viswanath et al 17,19 recently presented a quantitative approach via texture descriptors in conjunction with MP-MRI to evaluate post-LITT changes for prostate cancer. Texture descriptors were shown to outperform original MR intensities in identifying changes within the ablation zone, pre-and post-LITT.…”

Identifying quantitative in vivo multi-parametric MRI features for treatment related changes after laser interstitial thermal therapy of prostate cancer

“…Our group 13,14 has leveraged these qualitative characteristics within novel quantitative schemes for per-voxel evaluation and MRI signature construction to differentiate between possible radiation treatment outcomes (success, unsuccessful, recurrence). However, to our knowledge there is no similar work on evaluating treatment related changes due to LITT, via co-registration and quantitative analysis of pre- and post-LITT MRI.…”

“…A spatially constrained affine registration scheme 14,21 will be used to bring the T2w and ADC images into alignment within each of the pre- and post-LITT acquisitions, as well as to align the pre- and post-LITT acquisitions. This co-registration step is performed in order to correct for (a) voxel size, resolution differences, and calculation artifacts between the ADC map and the T2w MRI, and (b) acquisition differences between the pre- and post-LITT MRI acquisitions.…”

“…This particular registration methodology was chosen as it has been well-validated for different multi-modal registration applications 21 . This methodology has previously resulted in successful registrations in quantitative treatment evaluation schemes 13,14 , and was therefore utilized in the current work instead of developing a more complex deformable scheme.…”