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Research Papers

Effect of Tumor Volume on Drug Delivery in Heterogeneous Vasculature of Human Brain Tumors

[+] Author and Article Information
Ajay Bhandari

Department of Mechanical Engineering,
Indian Institute of Technology,
Kanpur 208016, India
e-mail: ajayb@iitk.ac.in

Ankit Bansal

Department of Mechanical and
Industrial Engineering,
Indian Institute of Technology,
Roorkee 247677, India
e-mail: abansfme@iitr.ac.in

Rishav Jain

Department of Mechanical Engineering,
Indian Institute of Technology,
Kanpur 208016, India;
Engineering and Design Section,
Mitsubishi Heavy Industries
Compressor Corporation,
Hiroshima 7330036, Japan
e-mail: rishav_jain@compressor.mhi.co.jp

Anup Singh

Centre for Biomedical Engineering,
Indian Institute of Technology,
Delhi 110016, India;
Department of Biomedical Engineering,
All Indian Institute of Medical Sciences,
Delhi 110016, India
e-mail: anupsm@cbme.iitd.ac.in

Niraj Sinha

Department of Mechanical Engineering,
Indian Institute of Technology,
Kanpur 208016, India
e-mail: nsinha@iitk.ac.in

1Corresponding author.

Manuscript received July 10, 2018; final manuscript received November 29, 2018; published online January 18, 2019. Assoc. Editor: Ali Sadegh.

ASME J of Medical Diagnostics 2(2), 021004 (Jan 18, 2019) (10 pages) Paper No: JESMDT-18-1031; doi: 10.1115/1.4042195 History: Received July 10, 2018; Revised November 29, 2018

Drug distribution in tumors is strongly dependent on tumor biological properties such as tumor volume, vasculature, and porosity. An understanding of the drug distribution pattern in tumors can help in enhancing the effectiveness of anticancer treatment. A numerical model is employed to study the distribution of contrast agent in the heterogeneous vasculature of human brain tumors of different volumes. Dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) has been done for a number of patients with different tumor volumes. Leaky tracer kinetic model (LTKM) is employed to obtain perfusion parameters from the DCE-MRI data. These parameters are used as input in the computational fluid dynamics (CFD) model to predict interstitial fluid pressure (IFP), interstitial fluid velocity (IFV), and distribution of the contrast agent in different tumors. Numerical results demonstrate that the IFP is independent of tumor volume. On the other hand, the IFV increases as the tumor volume increases. Further, the concentration of contrast agent also increases with the tumor volume. The results obtained in this work are in line with the experimental DCE-MRI data. It is observed that large volume tumors tend to retain a higher concentration of contrast agent for a longer duration of time because of large extravasation flux and slow washout as compared to smaller tumors. These results may be qualitatively extrapolated to chemotherapeutic drug delivery, implying faster healing in large volume tumors. This study helps in understanding the effect of tumor volume on the treatment outcome for a wide range of human tumors.

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Figures

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Fig. 2

(a) Rectangular computational domain in CFD and (b) segmented single slice enclosing tumor (shaded) and normal tissue (all dimensions in mm)

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Fig. 3

Mesh independence study of one slice of tumor shown in Fig. 2: (a) IFP, (b) IFV, and (c) contrast agent concentration at 60 s

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Fig. 4

(a) Precontrast and (b) Postcontrast image of MRI slice (outline shows manual tracing of tumor). Units of x-axis and y-axis are in mm.

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Fig. 5

Perfusion parameters (a) permeability (Ktrans) (s−1) and (b) porosity of tumor shown in Fig. 2. Units of x-axis and y-axis are in mm.

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Fig. 6

Contour maps of (a) IFP and (b) IFV of tumor shown in Fig. 2. Units of x-axis and y-axis are in mm. Unit of color scale bar for IFP is in Pa and for IFV is in m/s.

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Fig. 7

Variation of IFP and IFV with tumor volume

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Fig. 8

Contour maps of the time-averaged concentration of contrast agent of a particular slice of tumor (a) experimental and (b) simulated. Unit of the color scale bar for concentration is mmol/L. Units of x and y axis are same as shown in Fig. 2. (c) Averaged percentage error between experimental and simulated results.

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Fig. 9

Extravasation flux pattern with time at one tumor voxel shown in the zoomed contour plot

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Fig. 10

Variation of contrast agent concentration with tumor volume

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Fig. 11

Variation of permeability (Ktrans) and extravasation flux with tumor volume

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Fig. 12

Variation of plasma volume fraction with tumor volume

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Fig. 13

Variation of the total contrast agent concentration for all the voxels per unit volume with time

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Fig. 14

Variation of convective flux and diffusive flux with tumor volume

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