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

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. DCE MRI Pharmacokinetic Modeling

^{−1}]) due to CA relaxivity, ${\mathrm{r}}_{1}$ [(mM)

^{−1}s

^{−1}], is linearly related to the tissue CA concentration, ${C}_{t}$ (mM),

_{10}is the pre-contrast longitudinal relaxation rate.

_{10}(=1/${T}_{10}$) and arterial input function (AIF) from the major artery within the field of view (FOV). DCE pharmacokinetic analysis can be performed to estimate physiological parameters via compartmental models ranging from complex to simple, providing plasma (blood) flow (${F}_{\mathrm{p}}$), permeability surface area product (PS), volume fractions of blood plasma (${v}_{\mathrm{p}}$), and extracellular extravascular space (EES) (${v}_{\mathrm{e}}$).

#### 2.1.1. Extended Tofts Model (ETM)

#### 2.1.2. Patlak Model (PM)

#### 2.1.3. Fast Exchange Regime (FXR) or Shutter Speed Model (SSM)

_{,}) incorporating EES CA concentration (${C}_{e}\left(t\right)={C}_{t}\left(t\right)/{v}_{e}$) from Equation (3) into Equation (5), rather than using the linear relationship between the change in ${R}_{1}$ (Δ${R}_{1}$) with C

_{t}(Equation (1)). The equation for FXR is given by

#### 2.1.4. Arterial Input Function (AIF) Selection

#### 2.2. DW MRI Data Modeling

^{2}) and intermediate to high b-values (b ≥ 100–2000 s/mm

^{2}) exhibit two distinct curvature signals. The standard monoexponential model calculates the apparent diffusion coefficient (ADC, Equation (6)), reflecting tumor cellularity, from imaging with at least ≥ 2 b-values using a straightforward linear regression fitting [39]. ADC is a composite metric accounting for molecular diffusion and microcapillary perfusion. LeBihan formulated a biexponential model (intravoxel incoherent motion (IVIM)), fitting multi-b-value signal with estimated metrics of the capillary network, i.e., pseudo-diffusion coefficient (${D}^{\ast}$), perfusion fraction ($f$), and tissue true diffusion coefficient ($D$) (Equation (8)) [29]. IVIM estimation of f provides insight into vascular dynamics without CA injection. ADC and IVIM models assume a Gaussian probability distribution function for the displacement of the water molecules. The presence of underlying tissue microstructures in a tumor can alter the distribution of water diffusion from Gaussian to non-Gaussian (NG). Diffusion kurtosis imaging (DKI) (Equation (7)) was introduced to capture NG effects by expanding the DW signal to second-order higher b-values [30], represented by apparent kurtosis coefficient (${K}_{app}$), a surrogate QIB of tissue microstructure, in addition to the apparent diffusion coefficient (${D}_{\mathrm{a}\mathrm{p}\mathrm{p}}$). NG-IVIM describes simultaneous perfusion and restricted diffusion by incorporating microstructure, $K$, the signal deviation from Gaussianity [25,29], providing estimates of the $f$, ${D}^{\ast}$, $D$, and $K$. Figure 2 shows QAMPER’s available DW models, which can be used to analyze data and to identify the optimal model based on tumor heterogeneity. The DW MRI model equations are as follows:

^{2}), respectively.

#### 2.3. ${T}_{1}$ and ${T}_{2}$ Relaxometry

#### 2.4. Optimal Model Mapping (OMM)

^{2}) [31], chi-square (χ

^{2}) [47], corrected Akaike information criteria (AICc) [19,48,49,50], and Bayesian information criteria (BIC) [25,51] have been used to select the model that most accurately reflects the tumor physiology and can be used to quantify values of imaging biomarkers in each voxel.

#### 2.5. Imaging Formats and Conversion

^{2}. In addition to MATLAB, two open-source external dependencies are currently required to run MRI-QAMPER: dcm2niix [26] and NIfTI Toolbox for MATLAB.

## 3. Results

#### 3.1. QAMPER QIN Software Validation: DROs and CCPs

#### 3.2. DCE MRI DRO (RSNA)

#### 3.3. DW MRI DRO (University of Michigan)

#### 3.4. Collaborative Challenge Projects

#### 3.4.1. DCE CCP (ISMRM, Open Science Initiative for Perfusion Imaging (OSIPI))

#### 3.4.2. DW MRI CCP (QIN, MCW, Prostate)

_{K}), and ADC showed the least percent difference among sites and the highest correlation. QAMPER’s ability to customize and set lower- and upper-threshold bounds was a feature that grouped it among the top five out of fourteen tools with the highest area under the curve (AUC) for prostate cancer detection [20].

#### 3.5. DCE and DW MRI in Clinical Trial (Oropharyngeal Cancer)

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

#### Appendix A.1. Precontrast ${T}_{10}$ Fitting

#### Appendix A.2. Signal-to-CA Concentration Calculation

^{−1}]) due to CA relaxivity, ${\mathrm{r}}_{1}$ [(mM)

^{−1}s

^{−1}], is linearly related to the tissue CA concentration, ${C}_{t}$ (mM),

#### Appendix A.3. Compartmental Tissue Uptake Model (CTUM)

#### Appendix A.4. Two-Compartment Exchange Model (2CXM)

_{,}and ${v}_{\mathrm{e}}$ [5].

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**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.**Schematic of image processing routines included with MRI-QAMPER v3.0. The software provides methods for: multi-flip angle ${T}_{1}$ mapping, multi-echo ${T}_{2}$ relaxometry, multi-compartmental methods for DCE, and fitting for multi-b-value DW imaging.

**Figure 3.**Representative output of MRI-QAMPER v3.0: input images (DW b = 0, ${T}_{1}$-weighted base image) and output quantitative parametric maps (ADC, ${K}^{trans}$), computed for images in brain, head and neck, pancreas and bladder. Visualization of parametric map overlay was created with MRIcron (v1.0.20190902) software.

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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