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Volume 9
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Article

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

by
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 and
Amita Shukla-Dave
1,3,*
1
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
2
Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
3
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
4
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
5
Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
6
Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Tomography 2023, 9(6), 2052-2066; https://doi.org/10.3390/tomography9060161
Submission received: 14 September 2023 / Revised: 12 October 2023 / Accepted: 18 October 2023 / Published: 3 November 2023
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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: multiparametric MRI; dynamic contrast-enhanced MRI; diffusion-weighted MRI; optimal model mapping; cancer; oncology; quantitative imaging biomarkers multiparametric MRI; dynamic contrast-enhanced MRI; diffusion-weighted MRI; optimal model mapping; cancer; oncology; quantitative imaging biomarkers
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MDPI and ACS Style

LoCastro, E.; Paudyal, R.; Konar, A.S.; LaViolette, P.S.; Akin, O.; Hatzoglou, V.; Goh, A.C.; Bochner, B.H.; Rosenberg, J.; Wong, R.J.; et al. A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology. Tomography 2023, 9, 2052-2066. https://doi.org/10.3390/tomography9060161

AMA Style

LoCastro E, Paudyal R, Konar AS, LaViolette PS, Akin O, Hatzoglou V, Goh AC, Bochner BH, Rosenberg J, Wong RJ, et al. A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology. Tomography. 2023; 9(6):2052-2066. https://doi.org/10.3390/tomography9060161

Chicago/Turabian Style

LoCastro, Eve, Ramesh Paudyal, Amaresha Shridhar Konar, Peter S. LaViolette, Oguz Akin, Vaios Hatzoglou, Alvin C. Goh, Bernard H. Bochner, Jonathan Rosenberg, Richard J. Wong, and et al. 2023. "A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology" Tomography 9, no. 6: 2052-2066. https://doi.org/10.3390/tomography9060161

APA Style

LoCastro, E., Paudyal, R., Konar, A. S., LaViolette, P. S., Akin, O., Hatzoglou, V., Goh, A. C., Bochner, B. H., Rosenberg, J., Wong, R. J., Lee, N. Y., Schwartz, L. H., & Shukla-Dave, A. (2023). A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology. Tomography, 9(6), 2052-2066. https://doi.org/10.3390/tomography9060161

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