Chia-Feng Lu1,2,3, Fei-Ting Hsu4, Li-Chun Hsieh4, Yu-Chieh Jill Kao1,2, Hua-Shan Liu4,5, Ping-Huei Tsai2,4, Pen-Yuan Liao4, and Cheng-Yu Chen1,2,4
1Translational Imaging Research Center, College of Medicine, Taipei Medical University, Taipei, Taiwan, 2Department of Radiology, School of Medicine, Taipei Medical University, Taipei, Taiwan, 3Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan, 4Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan, 5Graduate Institute of Clinical Medicine, Taipei Medical University, Taipei, Taiwan
Synopsis
The multi-modality and multi-radomic-feature MRI may provide a more efficient regression model for imaging gene expressions than the conventional radiogenomic approach. Background and Purpose
Glioblastoma
(GBM) is one of the most common and aggressive malignant brain tumor resulting
in a poor prognosis after diagnosis. Recently, imaging gene expression of GBM
by using the clinical MRI shows the possibility to noninvasively probe the
tumor characteristics.1 Previous studies showed that the perfusion
maps of cerebral blood volume (CBV) can be a surrogate image biomarker of
angiogenesis, the hallmark of GBM that generates blood vessels and facilitates
tumor progression.2,3 In addition to the hemodynamic CBV, angiogenesis-related
physiologic changes in GBM, such as the vascular leakiness, cellularity, and
tumor appearance, can be characterized by several other MR modalities. This
study aims to determine whether the multivariate analyses based on the
multi-modality MRI can further improve the efficacy in assessing the gene
expression by using the MR radiomic features.
Materials and Methods
This study is approved by the local
Institutional Review Board and the written informed consents were given before
the patient enrollment. Four patients (2 men and 2 women) with primary GBM were
recruited in this study. Preoperative clinical MR data, including post-contrast
T1-weighted (T1+C), T2 FLAIR, dynamic susceptibility contrast (DSC)
perfusion-weighted, and diffusion-weighted imaging (DWI), with default
parameters were acquired from each patient using a 1.5 T MRI scanner (GE Signa
HDxt, USA) in Taipei Medical University Hospital. Relative CBV (rCBV) maps with correction for
vascular leakage and K2 permeability maps were derived from DSC images,4
and the apparent diffusion coefficient (ADC) was calculated from DWI. Three site-specific tissue specimens located
in tumor enhancement, necrosis, and edema respectively were extracted from each
patient using MRI-based stereotaxic biopsy before surgical resection. Three of
overall 12 samples were excluded from the subsequent microarray RNA profiling because
of the insufficient RNA quality. Three hundred and thirty-eight genes annotated
as positive regulation of angiogenesis function based on the Gene Ontology
database5 (GO term, GO:00001525) were selected (Table 1), and the normalized
log2 mean of the selected genes was used to measure the gene expression.
In order to acquire the
site-specific imaging features from different MR modalities, image
co-registration and interpolation reference to the T1+C images were first employed
to ensure the concordance of imaging location and spatial resolution (0.43 x
0.43 x 6.00 mm3) between imaging modalities. The region of interests
(ROI) in tumor enhancement, necrosis, and edema were then determined respectively
by manual delineations with intensity thresholding on T1+C and T2 FLAIR images (Fig.1). Three
categories, including intensity-based, geometry-based, and textural features, with
overall 55 quantitative features can be derived from each ROI and in each
imaging modality based on the previous described radiomics approach.6
Finally, the multivariate linear regression was applied to estimate the fitting
efficacy of gene expression based on the radiomic features of MRI modalities,
including T1+C, rCBV, K2, and ADC maps. The coefficient of determination, R2, was calculated to assess how well the imaging features of multi-modality MRIs
fit the gene expression.
New vessel formations during tumor angiogenesis increase local blood supply to promote tumor proliferation. These rapidly formed vessels
are characterized by the disrupted structure of blood brain barrier,7
leading to the vascular leakage revealed by the hyperintensity in T1+C images or
K2 maps. After the co-registration between imaging modalities, the enhancing ROI
defined by the hyperintensity tumor regions in T1+C images showed high cellularity
depicted by low ADC values and high rCBV values (Fig. 1). The necrotic regions
exhibited opposite phenomena with low cellularity (high ADC values) and low
rCBV values (Fig. 1). The results of multivariate linear regressions demonstrated
that using all 4 modalities (i.e., T1+C, rCBV, K2, and ADC) as regressors can
identify the best fitting model with the highest coefficients of determination,
R2, in estimating gene expression by imaging features (Fig. 2). This
finding suggested that using multi-modality MRI associated with physiologic
changes in angiogenesis (Fig. 2E) can better predict the relevant gene expressions
than using single parametric CBV map (Fig. 2B). Table 2 lists the number of
genes that exhibited significantly improved R2 values based on the
multi-modality approach for each radiomic category. It is worth pointing out that
the utilized radiomic features can also be a critical factor on fitting
efficacy. Both intensity-based and textural features achieved sufficient R2
and significant R2 improvement using multi-modality MRI approach in
assessing gene expressions (Fig. 2 and Table 2). Future studies with larger sample
size are warrant to investigate the benefit of combing multi-modality and multi-radiomic-feature
MRI in imaging genotype in GBM.
Acknowledgements
This study was funded in part by the Taipei Medical University (TMU103-AE1-B20) and the Ministry of Science and Technology (MOST 104-2221-E-038-007, MOST 104-2314-B-038 -051 -MY3), Taipei Medical University Hospital (104TMU-TMUH-23, 104TMUH-SP-02), and Health and Welfare Surcharge of Tobacco Products supported for Comprehensive Cancer Center of Taipei Medical University (MOHW104-TDU-B-212-124-001), Taipei, Taiwan.
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