Probabilistic independent component analysis of dynamic susceptibility contrast perfusion MRI in metastatic brain tumors

Ararat Chakhoyan^{1

2}, Catalina Raymond^{1

2}, Jason Chen³, Jodi Goldman⁴, Jingwen Yao^{1

2

5}, Tania B Kaprealian⁶, Nader Pouratian^{3

6

7}, Benjamin M Ellingson^{8

9

10

11}

Affiliations

¹ UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
² Departments of Radiological Sciences and Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
³ Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁴ Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
⁵ Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA.
⁶ Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁷ Brain Research Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁸ UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu.
⁹ Departments of Radiological Sciences and Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA. bellingson@mednet.ucla.edu.
¹⁰ Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu.
¹¹ UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu.

PMID: 30885275
PMCID: PMC6423873
DOI: 10.1186/s40644-019-0201-0

Probabilistic independent component analysis of dynamic susceptibility contrast perfusion MRI in metastatic brain tumors

Ararat Chakhoyan et al. Cancer Imaging. 2019.

. 2019 Mar 18;19(1):14.

doi: 10.1186/s40644-019-0201-0.

Authors

Ararat Chakhoyan^{1

2}, Catalina Raymond^{1

2}, Jason Chen³, Jodi Goldman⁴, Jingwen Yao^{1

2

5}, Tania B Kaprealian⁶, Nader Pouratian^{3

6

7}, Benjamin M Ellingson^{8

9

10

11}

Affiliations

¹ UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
² Departments of Radiological Sciences and Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
³ Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁴ Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
⁵ Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA.
⁶ Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁷ Brain Research Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁸ UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu.
⁹ Departments of Radiological Sciences and Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA. bellingson@mednet.ucla.edu.
¹⁰ Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu.
¹¹ UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu.

PMID: 30885275
PMCID: PMC6423873
DOI: 10.1186/s40644-019-0201-0

Abstract

Purpose: To identify clinically relevant magnetic resonance imaging (MRI) features of different types of metastatic brain lesions, including standard anatomical, diffusion weighted imaging (DWI) and dynamic susceptibility contrast (DSC) perfusion MRI.

Methods: MRI imaging was retrospectively assessed on one hundred and fourteen (N = 114) brain metastases including breast (n = 27), non-small cell lung cancer (NSCLC, n = 43) and 'other' primary tumors (n = 44). Based on 114 patient's MRI scans, a total of 346 individual contrast enhancing tumors were manually segmented. In addition to tumor volume, apparent diffusion coefficients (ADC) and relative cerebral blood volume (rCBV) measurements, an independent component analysis (ICA) was performed with raw DSC data in order to assess arterio-venous components and the volume of overlap (AVOL) relative to tumor volume, as well as time to peak (TTP) of T₂* signal from each component.

Results: Results suggests non-breast or non-NSCLC ('other') tumors had higher volume compare to breast and NSCLC patients (p = 0.0056 and p = 0.0003, respectively). No differences in median ADC or rCBV were observed across tumor types; however, breast and NSCLC tumors had a significantly higher "arterial" proportion of the tumor volume as indicated by ICA (p = 0.0062 and p = 0.0018, respectively), while a higher "venous" proportion were prominent in breast tumors compared with NSCLC (p = 0.0027) and 'other' lesions (p = 0.0011). The AVOL component was positively related to rCBV in all groups, but no correlation was found for arterial and venous components with respect to rCBV values. Median time to peak of arterial and venous components were 8.4 s and 12.6 s, respectively (p < 0.0001). No difference was found in arterial or venous TTP across groups.

Conclusions: Advanced ICA-derived component analysis demonstrates perfusion differences between metastatic brain tumor types that were not observable with classical ADC and rCBV measurements. These results highlight the complex relationship between brain tumor vasculature characteristics and the site of primary tumor diagnosis.

Keywords: Biomarker; Brain metastasis; Diffusion; ICA; Perfusion.

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Conflict of interest statement

Ethics approval and consent to participate

This retrospective study was approved by our institutional review board (IRB) with an informed consent waiver.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

**Fig. 1**
Independent component analysis (ICA) of dynamic susceptibility contrast (DSC-MRI). Left - Temporal evolution of normalized T₂* from arterial and venous component. Right – ICA derived Z-score probability maps (p < 0.5) for respective components for one representative patient. Results are overlaid on anatomical T1-weigthed post-contrast image. Sagittal plane representing selected slices (from left to right) to cover polygon of Willis, sagittal sinus and tumor region. Binarized arterio-venous (positive Z-score values) and overlap mask (AVOL) are overlaid to the T1-weigthed post-contrast image

**Fig. 2**
Multiparametric MRI images in patients with secondary brain metastasis from a) breast, b) NSCLC and c) clear cell kidney carcinoma. Pre- and post-contrast T₁-weigthed, T₂-weighted FLAIR, T1 subtraction (ΔT₁ map), ADC, normalized rCBV and arterio-venous overlap (AVOL) maps for each representative patient. Inhomogeneous tumor lesions were defined and overlaid on ΔT₁ map excluding central necrotic areas (red rectangles). T₂-weighted FLAIR shows peri-enhancing edema on both breast and clear cell kidney carcinoma cases. ADC maps shows reduced diffusion values within solid component of the tumor (contrast enhancement) and increased diffusion in edematous component. This former region is characterized by hypoperfused blood volume (low rCBV) while solid component is mostly hyperperfused. AVOL maps are heterogeneous for both arterial and venous components while in clear cell kidney carcinoma case, tumor region is predominated by overlap map

**Fig. 3**
Quantitative measures of a) tumor volume (μL), b) ADC (μm2/ms) and c) rCBV (a.u.) for all three groups. Statistical analyses (p values) between groups are reported from each pair Nonparametric-Wilcoxon test. Bold text represents statistical differences (bottom), especially for tumor volume between ‘Other’ vs. Breast (p = 0.0056) and NSCLC groups (p = 0.0003)

**Fig. 4**
Results of arterio-venous and overlap maps from each subsequent brain metastatic group. Red, yellow and blue box plots representing respectively arterial, overlap and venous components. Statistical analyses are reported in bottom part for each component, across groups (left), as well as within each group and between components (right)

**Fig. 5**
Correlation between tumor rCBV (whole ΔT1 lesion mask) and proportion of ICA-derived components. No correlation was observed between arterial component and rCBV (a), venous component and rCBV (c). The overlap proportion (b) significantly correlates with rCBV in each subtype of metastatic brain lesion

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