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

Ararat Chakhoyan; Catalina Raymond; Jason Chen; Jodi Goldman; Jingwen Yao; Tania B Kaprealian; Nader Pouratian; Benjamin M Ellingson

doi:10.1186/s40644-019-0201-0

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

Cancer Imaging. 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.

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.

MeSH terms

Aged
Brain / blood supply
Brain / diagnostic imaging*
Brain Neoplasms / diagnostic imaging*
Brain Neoplasms / secondary
Cerebrovascular Circulation
Contrast Media
Diffusion Magnetic Resonance Imaging*
Female
Humans
Magnetic Resonance Angiography*
Male
Middle Aged
Retrospective Studies
Tumor Burden*

Substances

Contrast Media

Abstract

MeSH terms

Substances

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