Tumor detection using antibodies conjugated magnetic nanoparticles

Arie Levy, Israel GannotBiomedical Engineering Department Tel Aviv University Israel

Thermography

First Introduced at 1956 [1] Increased angiogenesis and

metabolism around tumors [2] Temperature rise at the skin surface

above the tumor. Detection by IR cameras. Computer aided

detection

Thermography - cont

Advantages: [2]Radiation freeContact freeNon InvasiveLow Cost

DisadvantagesLow sensitivity for small & deep tumors [3]Not tumor specificSubjective

Detect & Treat Approach

Antibody conjugated MNP solution

Tumor

Low power external AMF

IR Camera, used as a detector

Tumor + accumulated MNP

1. MNP Injection 2. Tumor Detection

High power external AMF

IR camera used as a sensor for feedback

3. Treatment

TBT vs. Thermography

The heat can be turned on and off – good reference can be achieved

The heat emanating from the tumor is considerably larger.

The heat source is tumor specific. Objective- no need for special skills. Treatment can be combined at the

same session.

Magnetic Nanoparticles[4]

Magnetic Nanoparticles

Coil

Magnetic Nanoparticl

es

Magnetic Nanoparticles

Targeting Small enough to diffuse from blood

vessel Antibodies targeting Binding sites

HER2 – Breast Cancer[5]. MN – renal cell carcinoma [6] U251-SP (G22 antibody) – Glioma [7]

Antibody

Coating

Magnetic Nanoparticle

Experiment Setup

DAQ unit

70x40mm glass cup filled with US Gel

0.5mm polymeric cover

DC power supply

Micrometer stage

1KΩ SMT resistor

Experiment Setup – cont.

Tumor Phantom

Tissue Phantom

IR Camera

RF Generator

Coil

Problem Definition & Assumptions

Small tumor (<5mm) – point heat source. The tissue was numerically modeled using

COMSOL according the Pennes bioheat equation [8]:

Thermal properties – conductivity , perfusion . metabolism – are assumed.

Unknown Location (X,Y, Depth).

Tumor

Tissue Surface

Tissue

D2mm

Tumor Detection Challenge

The temperature difference at the tissue surface is very low regarding measurement noise level

Without Noise

With Noise

Detection Protocol

1. Reference data is recorded.2. Magnetic field/heat source is turned

on.3. Sequence of IR images is recorded.4. The data is processed using MATLAB in

order to detect the tumor and its location.

Detection Algorithm

Time Averaging

Pre calculated estimation

Input

data set

Reference

data set

Hot Spot Detection

Noise Filtering

Hot Spot Classification

Tumor size & location

Pre Processing

Pre Processing

Original IR Data Original Data Minus Reference Data

Region of Interest SelectionFiltered Data

Hot Spot Selection

ROI border

“True” Hot Spot

“False” Hot Spot

Y

X

Tem

pera

ture

cha

nge

[Deg

C]

Hot Spot Classification

Tem

pera

ture

cha

nge

[Deg

C]

YX

2mm Hot Spot

Hot Spot Classification T

empe

ratu

re c

hang

e [D

eg C

]

YX

12mm Hot Spot

Hot spot classification

Normalization of each prediction to the hot spot data.

Calculating matching value for each prediction:

Thresholding. Interpolation. Depth estimation according to maximum

matching.

2),,(

2),,,,(1

k i j kjidkw

kjipk i j kjidkwMv

Hot Spot Classification

Recorded Temperature change

Normalized predicted temperature change for tumor depths 1-10[mm]

Best match: 4mm prediction

Hot Spot Classification

Max at 4mmDetection Threshold

Prediction Depth [mm]

Experiments

Setup 1 (US gel):3 different emitted powers. Up to 14mm depth.Idle (“no tumor”) measurement.

Setup 2 (Procine).Validation using 3mm depth tumor.

Training

140 measurements for idle (“no tumor”) and worst case (13mm 400mW) states.

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.010

0.05

0.1

0.15

0.2

0.25

Peak value [K]

Pro

babi

lity

Idle and worst case stats distribution of peak value

Tumor set

Tumor gaussian fitIdle set

Idle gaussian fit

Sensitivity & Specificity

Specificity:98.68%

Depth Estimation

Other Results Low power detection.

Procine model validation.

Magnetic Acoustic Detection -MAD

Magnetic coil

Pulsed magnetic field

Tissue

Magnetically marked tumor

Acoustic shock wave

Acoustic sensor

MAD - Simulation

MAD – Experimental Setup

MAD - Results

Summary

TBT Up to 14mm detection was demonstrated. Sub-millimeter tumors can be detected. Highly specific detection. Limited to near to surface tumors.MAD Potentially could detect deeper tumors. Simple setup.

Future work TBT:

Algorithm refinement.In vivo validation.

MADProof of concept.Setup improvement.

Treatment.Rotating Magnetic field.Double conjugation.

Treatment. Additional imaging modalities. Endoscopic Imaging. Subsurface imaging. In Vivo Experiments.

Thank You…

Reference1. R. N. Lawson. Implications of surface temperature in thediagnosis of breast cancer. Canada Med Assoc J, 75:309–310, 19562. WC Amalu. Infrared imaging of the breast – an overview.

Medical device and systems, cahpter 25, 2006 3. Statement on use thermography to detect breast cancer,

NBCC, 1999, www.nbcc.org.au.4. Kalambur V S, Han B, Hammer B E, Shield T W and

Bischof J C 2005 In vitro characterization of movement,heating and visualization of magnetic nanoparticles for biomedical applications Nanotechnology 16 1221–33

Reference5. Akira Ito et al. Magnetite nanoparticle-loaded anti-HER2

immunoliposomes, for combination of antibody therapy with hyperthermia, Cancer Letters 212 (2004) 167–175

6. M Shinkai et al. Targeting Hyperthermia for Renal Cell Carcinoma Using Human MN Antigenspecific Magnetoliposomes. Jpn. J. Cancer Res. 92, 1138–1146, 2001

7. Biao LE et al , Preparation of tumor-specific magnetoliposomes and their application for hyperthermia, Chem. Eng. Jpn, 2001

8. HH Pennes. Analysis of Tissue and Arterial Blood Temperatures in the Resting Human Forearm. Journal of Applied Physiology, 1948