1. Prediction of Regional Tumor Spread Using Markov Models Megan S. Blackburn Monday, April 14, 2008

2. Background 2006 Conference Paper by Benson et al. Describes use of Markov Chains to model cancer spread in patients Specifically studied head and neck cancers Comparisons made to surgical data Goal: Try to reproduce model proposed by Benson et al.

3. Background Cancer impacts our society as a whole Everyone is affected in some way by cancer in their lifetime

4. Cancer Generalized name describing more than 100 different disorders Cancer cells can be considered immortal Divide many more times than normal cells thus growing out of control Do not interact normally with other cells resulting in invasion of regions of the body other cells could not

5. Cancer Treatment Because of the far-reaching effects, much effort has been put into the treatment of cancer Goal of treatments: Kill the maximum cancer cells while killing the minimum normal cells Typical Treatments: Radiation Therapy and/or Chemotherapy

6. Radiation Therapy Beam of photons is incident on the patients Photons deposit their energy within the body Kills both healthy and diseased cells http://www.srhc.com/services/oncology/image/Clinac.jpg

7. Radiation Therapy Before treatment can begin, CT scan is taken of the patient CT scan is used to plan the patient’s treatment http://asiaonc.com/files/images/H&N%20Lat2.img_assist_custom.jpg

8. Radiation Therapy ICRU 50 describes several definitions for treatment planning GTV CTV PTV Treated Volume Irradiated Volume

9. Room for Improvement In Radiation Therapy, many different margins must be used in order to assure the tumor gets a therapeutic dose Margins result in more healthy tissue being dosed Microscopic disease must also be accounted for Markov Model could help to determine where the cancer has spread

10. Head and Neck Cancers Unique compared to other cancers Typically very irregular in size Early treatment was surgical removal Radiation Therapy and Brachytherapy are now useful tools Diseased Lymph nodes must be treated as well

11. Lymph Node Regions RegionNodal Group ISubmental & Submandibular IIUpper Jugular IIIMiddle Jugular IVLower Jugular VPosterior Triangle VIAnterior Compartment http://www.iscb.org/rocky06/presentations_pdf/29Kalet-rev2.pdf

12. Cancer Staging Tumor-Node-Metastasis (TNM) Staging T – anatomical description of the primary tumor site N – involvement of the lymph nodes M – metastasis to other regions of the body Staging is very diverse for cancers of the head and neck due to varied anatomical regions

13. Markov Model Named after Russian Mathematician Andrey Markov Mathematical Model describing a random progression of a system S1S1 S5S5 S2S2 S3S3 S4S4 0.5 0.3 0.7 0.1 0.9 0.8 0.2

14. Markov Model Probabilities of transitioning from one state to another Discrete time steps Cannot be in more than one state at a time S1S1 S5S5 S2S2 S3S3 S4S4 0.5 0.3 0.7 0.1 0.9 0.8 0.2

15. Markov Model Clock – describing number of steps to take N States – number of locations in which one could be N Events – One event associated with each state S1S1 S5S5 S2S2 S3S3 S4S4 0.5 0.3 0.7 0.1 0.9 0.8 0.2

16. Markov Model Initial Probabilities – probability of starting state Transition Probabilities – probability of moving from one state to another S1S1 S5S5 S2S2 S3S3 S4S4 0.5 0.3 0.7 0.1 0.9 0.8 0.2

17. Applications to Cancer Apply Markov Models to the movement of cancer – specifically lymph nodal involvement Goal is to determine the lymphatic spread with only the starting location of the cancer and the staging of the cancer Used a series of Markov Chains rather than a single Markov Chain

18. Applications to Cancer Single Markov Model represents each nodal region Within each Model, there are 5 states – 0 (no cancer), 1, 2, 3, 4 (extensive disease) Unique aspect is the linking of the Markov Models Links represents future nodal involvement

20. Applications to Cancer s defined states (0,1,2,3,4) in each node region Probability q s of each state metastasizing to next nodal region (state 1) Probability distribution, p i, of being in a specific state within each lymphatic region Probability, p’, of the next nodal region becoming affected by microscopic disease

21. Applications to Cancer Initially, probability distribution for each lymphatic region is set For initial tumor region, probability is set to 1 for state 1, 0 in all other states For all other lymphatic region, probability is set to 1 for state 0, 0 in all other states

22. Applications to Cancer Initial Tumor Region –Transition Matrix –Metastasis Vector Lymphatic Regions –Transition Matrix –Metastasis Vector

24. Application to Cancer Model is iterated on 4*Cancer Staging Probability of microscopic cancer occuring in each nodal region uses: Value must be adjusted against the probability that metastasis has already occurred Added to the probability that the downstream nodal region is already in State 1

25. Implementation Implemented in MATLAB Benson et al. used FMA (Foundational Model of Anatomy) for all anatomical regions Unable to match the anatomical data that Benson et al. obtained Could not directly compare with his results

26. Results Assumed 6 nodal regions downstream from primary tumor Obtained probabilities of cancer spreading to this regions given an initial cancer stage Lymph Region A Lymph Region B Lymph Region C Lymph Region D Lymph Region E Lymph Region F Cancer Stage 128%22%17%12%9%7% 236%30%25%21%17%13% 343%37%32%27%23%19% 450%44%38%33%28%24%

27. Conclusions Much tweaking is needed to the concept Too many arbitrarily chosen values Interesting idea BUT very unlikely to be accepted by the medical community anytime soon