Dr. Thomas Yankeelov:  Integrating Advanced Imaging and Biophysical Models to Predict Tumor Growth
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Dr. Thomas Yankeelov: Integrating Advanced Imaging and Biophysical Models to Predict Tumor Growth

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This is a talk from the Technology Association of Louisville Kentucky. Dawn Yankeelov is co-chair of TALK, and Dr. Thomas Yankeelov is the director for the Institute of Imaging Science at Vanderbilt ...

This is a talk from the Technology Association of Louisville Kentucky. Dawn Yankeelov is co-chair of TALK, and Dr. Thomas Yankeelov is the director for the Institute of Imaging Science at Vanderbilt University. He presented his latest research in June 2013, "Integrating Advanced Imaging and Biophysical Models to Predict Tumor Growth."

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Dr. Thomas Yankeelov:  Integrating Advanced Imaging and Biophysical Models to Predict Tumor Growth Dr. Thomas Yankeelov: Integrating Advanced Imaging and Biophysical Models to Predict Tumor Growth Presentation Transcript

  • Tom Yankeelov, Ph.D. Ingram Associate Professor of Cancer Research Institute of Imaging Science Departments of Radiology, Biomedical Engineering, Physics, and Cancer Biology Vanderbilt University 19 June 2013 Integrating Advanced Imaging and Biophysical Models to Predict Tumor Growth
  • 2  Weather models use observations of the atmosphere to predict how wind, temperature, and humidity evolve in time DW-MRI Cell Number DCE-MRI Perfusion FDG PET Metabolism  Noninvasive, quantitative imaging enables the same approach for predicting how tumors evolve in time Yankeelov et al. Sci Transl Med. 2013;5:187ps9
  • Visit 2 25 15 40 +5% Visit 3 28 16 44 +16% Visit 4 32 18 50 +32% Visit 5 48 23 71 +89% Baseline 24 14 38 T1: T2: SLD: (% ) Courtesy of Rick Abramson, M.D. 8 weeks 16 weeks 24 weeks T2 T1 3  
  • 4   Working hypothesis: Readily-available, multi-scale imaging techniques can provide the data to initialize/constrain predictive models of tumor growth and treatment response for clinical application.
  • 5 Outline 1.  What can quantitative imaging offer oncology? 2.  Specialize to neoadjuvant therapy 3.  Lessons from weather forecasting 4.  Towards a science of tumor forecasting
  • 6 Outline 1.  What can quantitative imaging offer oncology? 2.  Specialize to neoadjuvant therapy 3.  Lessons from weather forecasting 4.  Towards a science of tumor forecasting
  • 7   Magnetic resonance imaging (MRI)
  • 8 Diffusion  weighted  MRI   •  Boundaries may reduce distance molecules travel when compared to free molecules •  Thus, the Apparent Diffusion Coefficient (ADC) is lowered ~√t Distance from original position Free Restricted • Water molecules wander about randomly in tissue (Brownian Motion) • In a free solution, after a time t, molecules travel (on average) a distance L from where they started • But in tissue, compartment effects may hinder movement = restricted diffusion
  • 9 • Increasing cell density (cellularity); more cell membranes per unit distance to hinder diffusion  lower ADC ADC • ADC depends on cell volume fraction • Tumor cellularity may be monitored by DWI Hall et al. Clin Canc Res 2004;10:7852Anderson et al. Magn Reson Imaging. 2000;18:689-95. Diffusion  weighted  MRI  
  • 10Arlinghaus et al. Diffusion  weighted  MRI,  clinical  example   Responder ΔADC = 11.8% Non-responder ΔADC = -11.6% Pre-NAC Post-1 cycle ROC Analysis Sensitivity = 0.64 Specificity = 0.93 AUC = 0.70  Sensitivity = true positive rate = TP/(TP+FN)  Specificity = true negative rate = TN/(FP + TN)
  • 11   Dynamic  contrast  enhanced  MRI  (DCE-­‐MRI)   •   Each  voxel  yields  a  signal  intensity  time  course  that  can  be  analyzed  with  a    mathematical  model     •   By  fitting  data  to  model,  extract  parameters  that  report  tissue  characteristics   •   Serial  acquisition  of  images  before,  after  an  injection  of  contrast  agent  (CA)   •   As  CA  perfuses  into  tissue,  changes  the  measured  signal  intensity plasma   space   tissue  space   Ktrans   Ktrans/ve   Cp(t)   Ct(t)   Ktrans  =  transfer  rate  constant   ve  =  extravascular  extracellular    volume  fraction   Tofts,  et  al.  J  Magn  Reson  Imaging  1999;10:223-­‐232.    
  • 12 Sensitivity ROC Analysis Sensitivity = 0.81 Specificity = 0.75 AUC = 0.80 When combining DW-MRI & DCE-MRI data: Sensitivity = 0.88 Specificity = 0.82 AUC = 0.86 Pre-therapy Post-1 cycle Post therapy Responder Non- Responder DCE-­‐MRI,  Clinical  Example   Li et al. Magn Reson Med 2013 (epub ahead of print; Mani et al. J Am Med Inform Assoc. 2013;20:688-95.
  • 13   Positron emission tomography (PET)
  • 14 18FDG (blood) 18FDG (tissue) 18FDG-6-PO4 (cells)X Pre-NAC Post-1 cycle Post-all NAC Non-responder pCR Li et al. Eur J Nuc Med &Mol Imaging Res 2012;2:62
  • Pre- NAC extravascular extracellular volume fraction plasma volume fraction cellularity FDG-PET (glucose metabolism) blood perfusion and permeability Post-1 cycle Post-all NAC Xia Li, Nkiruka Atuegwu, Lori Arlinghaus
  • 16 •  Dramatic increases in quality of data available from non-invasive imaging  Imaging is moving from qualitative anatomical measurements to quantitative functional measurements at physiological, cellular, & molecular levels •  We talked about: MRI—anatomy, blood vessels, blood flow, cellularity PET—metabolism Imaging  Summary   •  Other imaging measurements we did not talk about:  cell proliferation, pH, pO2 (MRI)  Receptor expression, apoptosis (PET & SPECT) What is the best way to use such data to assist oncology?
  • 17 Outline 1.  What can quantitative imaging offer oncology? 2.  Specialize to neoadjuvant therapy 3.  Lessons from weather forecasting 4.  Towards a science of tumor forecasting
  • 18 • Pre-operatively treated locally advanced cancers  chemo-, radiation, endocrine, and targeted therapies • Five theoretical advantages to NAT: 1)  Earlier treatment of occult metastatic despite “curative” resection 2)  Determine sensitivity to treatment while tumor is in situ 3)  Complete a full course of treatment (more likely pre-operatively than post-operatively) 4)  Identify patients who develop metastases on NAT as they are unlikely to benefit from resection 5)  Decrease primary tumor volume Sener. JSO 2010;101:282
  • 19 • Key point: several disease sites where if you can get the patient to respond in the neoadjuvant setting  improved outcome • How to identify non-responders from responders early in therapy?  Develop predictive models that use data obtained from individuals • But more than that—would enable patient specific “clinical trialing”  Can perform individualized, in silico clinical trials to optimize the drug regiment, order, timing, dose, etc.  Models must make specific predictions
  • 20 Outline 1.  What can quantitative imaging offer oncology? 2.  Specialize to neoadjuvant therapy 3.  Lessons from weather forecasting 4.  Towards a science of tumor forecasting
  • 21 • In order to have meaningful weather predictions, needed: 1) Rudimentary understanding of atmospheric dynamics 2) Regular radiosonde measurements (...and then satellite data) 3) Stable numerical methods 4) Electronic computers • 100 years ago, none of this existed Lynch. J Computational Physics 2008;227:3431-44. “A century ago, weather forecasting was a haphazard process, very imprecise and unreliable. Observations were sparse and irregular, especially for the upper air and over the oceans. The principals of theoretical physics played little or no role in practical forecasting: the forecaster used crude techniques of extrapolation, knowledge of local climatology and guesswork on intuition; forecasting was more an art than a science.”
  • 22 •  So how did meteorology make such dramatic advances?  Advances in atmospheric physics, numerical methods, computing  Better data! Lynch. J Computational Physics 2008;227:3431-44. National Climate Data Center NEXRAD (NEX generation RADar) Data Sites ~160 sites
  • 23 Outline 1.  What can quantitative imaging offer oncology? 2.  Specialize to neoadjuvant therapy 3.  Lessons from weather forecasting 4.  Towards a science of tumor forecasting
  • 24Preziosi. Cancer Modeling and Simulation. • Let the tumor cells proliferate up to a certain “carrying capacity” = " • Solution is given by: • The last equation states that the tumor cells will continue to grow (exponentially) up to the carrying capacity of the system determined by "
  • 25Atuegwu et al. Transl Oncol. 2013;6:256-64 Experimental Simulated r = 0.90 Experimental Simulated Pearson’s correlation = 0.83 (p = 0.01) Concordance correlation = 0.80 Nsimulated(t3) x106 Nexperimental(t3)x106 Some summary stats (n = 27): • k (proliferation rate) separated pCR and non- pCR after 1 cycle of NAC (p = 0.019)  sensitivity = 82%, specificity 73%
  • 26   • Going forward, need to make greater use of available data • An example mathematical model : Random dispersal of tumor cells (diffusion) Proliferation of tumor cells Rate of chance of # of tumor cells  ADC values from DW-MRI to assign NTC(r,t) and extract k(r)  Everything on the right hand side is known
  • 27   • Going forward, need to make greater use of quantitative, multi-modality data • An example mathematical model : Random dispersal of tumor cells (diffusion) Proliferation of tumor cells Rate of chance of # of tumor cells Rate of change of # of endothelial cells Diffusion of EC Chemotaxis of EC Assume chemotaxis is in direction of areas of proliferating cells of higher density; can also estimate this from DW-MRI data
  • 28   • Going forward, need to make greater use of quantitative, multi-modality data • An example mathematical model : Rate of change of # of endothelial cells Diffusion of EC Chemotaxis of EC Rate  of  change   of  O2   Random dispersal of tumor cells (diffusion) Proliferation of tumor cells Rate of chance of # of tumor cells Perfusion  data   from  DCE-­‐MRI   ADC    &  PET   data  
  • • Going forward, need to make greater use of quantitative, multi-modality data • An example mathematical model : Rate of change of O2 Rate of change of glucose Rate of change of # of endothelial cells Diffusion of EC Chemotaxis of EC Random dispersal of tumor cells (diffusion) Proliferation of tumor cells Rate of chance of # of tumor cells
  • Experimental system -- C6 glioma Anatomical (Registration) DW-MRI Cell Number DCE-MRI Ktrans,ve, and vp 18F-FDG PET MRI on days 9, 10, 11, 13, 15, and 17 PET on days 9, 15, and 17 Hormuth et al. 2013 Oak Ridge National Lab BSEC conference.
  • Predicted tumor cell number Observed tumor cell number Hormuth et al. 2013 Oak Ridge National Lab BSEC conference.
  • 32 • Having a model, driven by patient specific data would enable personalized, in silico therapy modeling  theoretical/predictive oncology • Could “give” the patient therapy in silico, then see how they “respond”  Could systematically adjust therapies, order of combination therapy, dosing scheduling, etc.  Since the quantitative imaging data can be acquired in 3D, at multiple time points and noninvasively, it is the only game in town   Could  enable  (more)  rational  clinical  trials  design/execution     Eminently  testable  in  pre-­‐clinical  setting…  and  is  translatable  
  • 33   Squad • Lori Arlinghaus, PhD • Nkiruka Atuegwu, PhD • Richard Baheza, MS • Stephanie Barnes, PhD • Jacob Fluckiger, PhD • David Hormuth, BS • Xia Li, PhD • Mary Loveless, PhD • David Smith, PhD • Jared Weis, PhD • Jennifer Whisenant, MS • Jason Williams, PhD Collaborators • Vandana Abramson, MD • Bapsi Chak, MD • Ingrid Mayer, MD • Mark Kelley, MD • Brian Welch, PhD • Rick Abramson, MD • Mike Miga, PhD • Vito Quaranta, MD Funding • NCI U01 CA142565 • NCI R01 CA138599 • NCI R01 CA129661 • NCI R25 CA092043 • NCI R25 CA136440 • NCI P30 CA68485 & VICC Very sincere thank you to the women who participate in our studies.