Tumor detection using antibodies
conjugated magnetic
nanoparticles
Arie Levy, Israel Gannot
Biomedical 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 free
 Contact free
 Non Invasive
 Low Cost


Disadvantages
Low sensitivity for small & deep tumors [3]
 Not tumor specific
 Subjective

Detect & Treat
Approach
2. Tumor Detection
1. MNP Injection
Tumor +
accumulated
MNP
Tumor
IR Camera,
used as a
detector
Antibody
conjugated
MNP solution
3. Treatment
Low power
external AMF
IR camera used
as a sensor for
feedback
High power
external
AMF
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
[4]
Nanoparticles
Magnetic
Nanoparticles
Magnetic
Nanoparticles
Coil
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
DC power supply
DAQ unit
0.5mm polymeric cover
70x40mm glass cup
filled with US Gel
1KΩ SMT resistor
Micrometer stage
Experiment Setup – cont.
IR Camera
RF Generator
Coil
Tissue
Phantom
Tumor
Phantom
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).
D
2mm
Tumor
Tissue
Tissue
Surface
Tumor Detection
Challenge

The temperature difference at the tissue surface is
very low regarding measurement noise level
Without Noise
With Noise
Detection Protocol
1.
2.
3.
4.
Reference data is recorded.
Magnetic field/heat source is turned on.
Sequence of IR images is recorded.
The data is processed using MATLAB in
order to detect the tumor and its
location.
Detection Algorithm
Pre Processing
Input
Noise
Filtering
data set
Hot Spot
Detection
Hot Spot
Classification
Time
Averaging
Reference
data set
Pre calculated estimation
Tumor size &
location
Pre Processing
Original IR Data
Filtered Data
Original Data Minus Reference Data
Region of Interest Selection
Hot Spot Selection
“True” Hot Spot
Temperature change [Deg C]
“False” Hot Spot
ROI border
Hot Spot Classification
Temperature change [Deg C]
2mm Hot Spot
Hot Spot Classification
Temperature change [Deg C]
12mm Hot Spot
Hot spot classification


Normalization of each prediction to the hot
spot data.
Calculating matching value for each
prediction:
2
w

(
d

p
)
 k k i  j i , j , k i , j , k
Mv  1 
2
 k wk i  j (di, j ,k )



Thresholding.
Interpolation.
Depth estimation according to maximum
matching.
Hot Spot Classification
Best match:
4mm prediction
Normalized predicted
temperature change for
tumor depths 1-10[mm]
Recorded
Temperature change
Hot Spot Classification
Detection
Threshold
Max at 4mm
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.
Idle and worst case stats distribution of peak value
Tumor set
Tumor gaussian fit
Idle set
Idle gaussian fit
0.25
0.2
Probability

0.15
0.1
0.05
0
0
0.001
0.002
0.003
0.004
0.005 0.006
Peak value [K]
0.007
0.008
0.009
0.01
Sensitivity & Specificity

Specificity:98.68%
Depth Estimation
Other Results

Low power detection.

Procine model validation.
Magnetic Acoustic
Detection -MAD
Acoustic sensor
Magnetic coil
Pulsed magnetic field
Magnetically
marked tumor
Tissue
Acoustic
shock wave
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.


MAD
Proof of concept.
 Setup improvement.


Treatment.
Rotating Magnetic field.
 Double conjugation.

Thank You…
Reference
1. R. N. Lawson. Implications of surface temperature in the
diagnosis of breast cancer. Canada Med Assoc J, 75:309–
310, 1956
2. 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
Reference
5. 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