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. 2023 Nov 3;9(6):2052-2066.
doi: 10.3390/tomography9060161.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology

Eve LoCastro  1 , Ramesh Paudyal  1 , Amaresha Shridhar Konar  1 , Peter S LaViolette  2 , Oguz Akin  3 , Vaios Hatzoglou  3 , Alvin C Goh  4 , Bernard H Bochner  4 , Jonathan Rosenberg  5 , Richard J Wong  4 , Nancy Y Lee  6 , Lawrence H Schwartz  3 , Amita Shukla-Dave  1   3
Affiliations

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology

Eve LoCastro et al. Tomography. .

Abstract

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

Keywords: cancer; diffusion-weighted MRI; dynamic contrast-enhanced MRI; multiparametric MRI; oncology; optimal model mapping; quantitative imaging biomarkers.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow of MRI-QAMPER v3.0: (A) Images are acquired from MRI scanner and converted from DICOM to NIfTI. Skilled physician or planner contours ROI on image. The NIfTI MR images and ROI are loaded into the MRI-QAMPER GUI. (B) View of the MRI-QAMPER DCE GUI, with preview of patient image with ROI overlaid. Interface provides options for selecting multiple DCE routine for analysis, parameter bounds, option for OMM and AIF. (C) View of the MRI-QAMPER DW GUI, with patient and ROI preview. Interface provides options for selecting multiple DW routines, parameter bounds, manual toggling/editing of b-values and OMM option.
Figure 2
Figure 2
Schematic of image processing routines included with MRI-QAMPER v3.0. The software provides methods for: multi-flip angle T1 mapping, multi-echo T2 relaxometry, multi-compartmental methods for DCE, and fitting for multi-b-value DW imaging.
Figure 3
Figure 3
Representative output of MRI-QAMPER v3.0: input images (DW b = 0, T1-weighted base image) and output quantitative parametric maps (ADC, Ktrans), computed for images in brain, head and neck, pancreas and bladder. Visualization of parametric map overlay was created with MRIcron (v1.0.20190902) software.
Figure 4
Figure 4
Proposed architecture schema for cloud implementation of MRI-QAMPER.
See this image and copyright information in PMC

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