1. Tumor detection using antibodies conjugated magnetic nanoparticles Arie Levy, Israel Gannot Biomedical Engineering Department Tel Aviv University Israel

2. 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

3. Thermography - cont Advantages: [2] Radiation free Contact free Non Invasive Low Cost Disadvantages Low sensitivity for small & deep tumors [3] Not tumor specific Subjective

4. 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

5. 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.

6. Magnetic Nanoparticles [4]

7. Magnetic Nanoparticles CoilMagnetic Nanoparticles

9. 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

10. Experiment Setup DAQ unit 70x40mm glass cup filled with US Gel 0.5mm polymeric cover DC power supply Micrometer stage 1KΩ SMT resistor

11. Experiment Setup – cont. Tumor Phantom Tissue Phantom IR Camera RF Generator Coil

12. 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 D 2mm

13. Tumor Detection Challenge The temperature difference at the tissue surface is very low regarding measurement noise level Without Noise With Noise

14. 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.

15. Detection Algorithm Time Averaging Pre calculat ed estimation Input data set Reference data set Hot Spot Detection Noise Filtering Hot Spot Classification Tumor size & location Pre Processing

16. Reference averaging and subtraction. Space domain filtering: Low pass (gaussian). Camera noise (Vertical lines). Time domain filtering. 5 taps FIR for each pixel. Slow base line drift correction. Region of interest selection.

17. Pre Processing Original IR Data Original Data Minus Reference Data Region of Interest SelectionFiltered Data

18. Hot Spot Selection ROI border “True” Hot Spot “False” Hot Spot Y X Temperature change [Deg C]

19. Hot Spot Classification Temperature change [Deg C] Y X 2mm Hot Spot

20. Hot Spot Classification Temperature change [Deg C] Y X 12mm Hot Spot

21. 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.

22. Hot Spot Classification Recorded Temperature change Normalized predicted temperature change for tumor depths 1-10[mm] Best match: 4mm prediction

23. Hot Spot Classification Max at 4mm Detection Threshold Prediction Depth [mm]

24. 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.

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

26. Sensitivity & Specificity Specificity:98.68%

27. Depth Estimation

28. Other Results Low power detection. Procine model validation.

29. Magnetic Acoustic Detection -MAD Magnetic coil Pulsed magnetic field Tissue Magnetically marked tumor Acoustic shock wave Acoustic sensor

30. MAD - Simulation

31. MAD – Experimental Setup

32. MAD - Results

33. 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.

34. Future work TBT: Algorithm refinement. In vivo validation. MAD Proof of concept. Setup improvement. Treatment. Rotating Magnetic field. Double conjugation. Treatment. Additional imaging modalities. Endoscopic Imaging. Subsurface imaging. In Vivo Experiments.

35. Thank You…

36. 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.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

37. 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