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The Laboratory for Quantitative Medicine
Massachusetts General Hospital/Harvard Medical School
James Michaelson PhD

Databases on Patients,
Mathematics of Cancer Outcome, Screening and Treatment
Analysis of Medical Usage and Cost among Cancer Patients
Communication of Cancer Outcome (The CancerMath.net web calculators)

Research Summary

For the past decade, the work of my group has been concerned with building very large databases on patients,
using this information to understand disease, and especially cancer, and its treatment, using mathematics to answer
practical questions about health, and building web-based tools for communicating this information to patients and medical
professionals (the CancerMath.net calculators).

Databases

We have built a number of very large databases on patients. Last June, we were tasked by the MGH Cancer Center
to build a database on all of the 173,301 Massachusetts General Hospital cancer patients,167,814 of whom were
diagnosed between 1968 and 2010. The database contains 559,921 pathology reports, 575,204 discharge reports,
10,938,444 encounter notes, 304,211 operative reports, 22,009,527 procedure notes, 9,159,232 radiology
reports,1,700,000 aggregated medical bills, and 250,000 images. The database contains all-cause survival information
from the Social Security Administration Death Master File (which provides information on all deaths of persons issued
social security numbers since 1937), and cause-of-death information from the Massachusetts Death Certificate Database
(which contains international classification of disease cause of death information on 1,984,790 people who died in the
state of Massachusetts between 1970 and 2008). The database is linked to the MGH SNAPSHOT gene sequence
dataset, thus providing a great wealth of genetic data on a large number of patients.

As far as we are aware, in terms of the total mass of data, this database is the largest source of clinical information on
cancer in the world.

Our group also maintains a great wealth of other data on patients, including the largest multi-institutional database on
the breast cancer in the world, with data on more than 33,000 breast cancer patients (and all the varieties of information
noted above). We have also built a similar multi-institutional data on melanoma patients. We also possess the Van Nuys
Breast Cancer breast cancer database. We also have our own MS SQL Server re-build of the SEER national database
with, for example 325,0000 breast cancer patients for who there is full tumor size and survival information. We also
possess the SEER/Medicare dataset for breast cancer. Finally, we also have build a number of specialized databases,
including our database on all MGH cancer patients who were treated by liver resection.

Medical Usage and Cost

Over the past year, we have served on the MGH Cancer Center's lung cancer and colon cancer redesign
committees, providing detailed and actionable data on medical use and cost for cancer patients. For example, our
analysis of the lung cancer patients has revealed that the total cost of treating lung cancer patients at the MGH is
million/year, but that standardization of care has the potential of saving million/year.

Studies are underway for generating data on treatment and cost for patients with many types of cancer.

A Mathematical Approach To Cancer Lethality

We have developed a mathematical framework for comprehending cancer lethality: the binary biological model of
metastasis. This mathematics has proven to provide highly specific method for estimating of the risk of death for individual
breast carcinoma patients, with an accuracy of 1%, This mathematics also provide estimates of the risk of death for
melanoma, renal cell carcinoma, sarcoma, and head and neck squamous cell carcinomas, and the applicability of this
mathematics in analysis of survival for other cancers is under analysis. This math also provides a basis for estimating the
risk of node positivity for breast carcinoma and melanoma patients, as well as providing insight into the nature of the
events of spread that underlie cancer lethality, as well as providing a way to address a whole range of questions in
oncology.

Our binary-biological mathematics has also formed the core of a computer simulation model of cancer screening,
which has made it possible to derive biologically plausible and testable estimates of the reduction in cancer death that can
be expected from screening various patients at various intervals. This work has been accompanied by a whole range of
studies on the operational details of the usage of breast cancer screening.

Our binary-biological mathematics has been used to create a series of web-based CancerMath.net calculators,
which provide patients with breast carcinoma, melanoma, and renal cell carcinoma with information on their likely
outcomes. For breast carcinoma, we also provide a CancerMath web-calculator that shows the benefit that they can
expect from the various adjuvant chemotherapy agents available to them. Analysis of the use of these CancerMath.net
calculators has taught us that they are very widely used, being consulted by 1-in-5 USA breast carcinoma patients, 1-in-2
renal cell carcinoma patients, and a large number of melanoma patients. More details can be found below.

CancerMath.net Calculators

For the last decade, my research has concerned the development of a mathematical framework for predicting survival for
cancer patients, together with the impact that various treatment choices will have on that outcome and the creation of very
large databases on patients so that this mathematics can be accurate.

One of the applications of this work has been the creation of a series of Cancer Math.net web calculators, which appear to
have become the most widely used decision aids used by cancer patients (www.CancerMath.net). These calculators are
used by 1-in-5 breast carcinoma patients, 1-in-2 renal cell carcinoma patients. and large numbers of patients with other
cancers. This has happened quite spontaneously, without publicizing these tools.

The goal of this work has been to provide patients and their physicians with highly accurate, disinterested, information on
their survival expectations, together with information on the impact that can expected from the various treatment options
that are available to them.

This math also has applications in other fields, such as: generating accurate estimates of the benefit of cancer screening;
generating accurate estimates of life expectancy for the insurance industry; generating accurate estimates for legal
professionals of the harm caused (if any) in the detection and treatment of cancer.

We have also created a web-calculator, PreventiveMath.net (www.PreventiveMath.net), which provides individuals with a
list of the class A US Preventive Services Task Force recommendations, prioritized by the benefit that they can expect, so
that people can see the benefit, and choose those steps that will give them the greatest possible extension in life.

The current tools available at CancerMath.net website include:

1) Breast Cancer Outcome Calculator (Provides information on survival expectation [see below for definition], at the time
of diagnosis, assuming standard of care therapy)

2) Breast Cancer Therapy Calculator (Provides information on survival expectation, and the impact which various
adjuvant chemotherapy options can be expected to have on that outcome)

3) Breast Cancer Conditional Survival Calculator (Provides information on survival expectation, assuming standard of
care therapy, for patients who have remained disease free 2-15 years after diagnosis)

4) Breast Cancer Nodal Status Calculator (Provides information on the likelihood of cancer spread to the local lymph
nodes)

5) Breast Cancer Nipple Involvement Calculator Calculator (Provides information to the surgeon on the likelihood that
nipple sparing surgery can be carried out without leaving cancer behind)

6) Melanoma Outcome Calculator (Provides information on survival expectation, at the time of diagnosis, assuming
standard of care therapy) (Also includes Conditional Survival Information)

7) Renal Cell Carcinoma Outcome Calculator (Provides information on survival expectation, at the time of diagnosis,
assuming standard of care therapy) (Also includes Conditional Survival Information)

8) Colon Cancer Cancer Outcome Calculator (Provides information on survival expectation, at the time of diagnosis,
assuming standard of care therapy) (Also includes Conditional Survival Information)

9) Head & Neck Cancer Outcome Calculator (Provides information on survival expectation, at the time of diagnosis,
assuming standard of care therapy) (Also includes Conditional Survival Information)

10) Sarcoma Outcome Calculator (Provides information on survival expectation, at the time of diagnosis, assuming
standard of care therapy) (Also includes Conditional Survival Information) (completed, but not yet been posted, but
viewable at the hidden link:http://www.lifemath.net/cancer/sarco...come/index.php).

Notes

1) survival expectation measures provided by the CancerMath calculators are: risk of death, for each of the first 15 years
after diagnosis: 1) to cancer; 2) to causes of death other than cancer; 3) to all causes.

Also provided is the life expectancy with cancer, life expectancy without cancer, and the reduction in life expectancy that
is caused by cancer.

For the therapy calculator, the impact of the various breast cancer adjuvant chemotherapy regimens on these measures
are given.

2) The outcome calculators also provide Cancer Stage.

References

1. Michaelson, J, Halpern, E, Kopans, D. A Computer Simulation Method For Estimating The Optimal Intervals For
Breast Cancer Screening. Radiology. 212:551-560 1999

2. Michaelson, JS, Kopans, DB, Cady, B. The Breast Cancer Screening Interval is Important. Cancer 2000 88:1282-
1284

3. Michaelson JS Using Information on Breast Cancer Growth, Spread, and Detectability to Find the Best Ways To Use
Screening to Reduce Breast Cancer Death Woman’s Imaging 3:54-57 2001

4. Michaelson JS Satija S Moore R Weber G Garland G Kopans DB, Observations on Invasive Breast Cancers
Diagnosed in a Service Screening and Diagnostic Breast Imaging Program Journal of Woman’s Imaging 3:99-104
2001

5. Michaelson JS Satija S, Moore R Weber G Garland G Phuri, D. Kopans DB The Pattern of Breast Cancer Screening
Utilization and its Consequences CANCER 94:37-43 2002

6. Michaelson JS Silverstein M, Wyatt J Weber G Moore R Kopans DB, Hughes, K. Predicting the survival of patients
with breast carcinoma using tumor size CANCER 95: 713-723 2002

7. Beckett JR, Kotre CJ, Michaelson JS Analysis of benefit:risk ratio and mortality reduction for the UK Breast Screening
Programme. Br J Radiol 76:309-20 2003

8. Michaelson JS Satija S, Moore R Weber G Garland G Kopans DB Estimates of the Breast Cancer Growth Rate and
Sojourn Time from Screening Database Information Journal of Women’s Imaging 5:3-10 2003

9. Michaelson JS Satija S, Moore R Weber G Garland G Kopans DB, Hughes, K. Estimates of the Sizes at which Breast
Cancers Become Detectable on Mammographic and on Clinical Grounds Journal of Women’s Imaging 5:10-19 2003

10. del Carmen MG, Hughes KS, Halpern E, Rafferty E, Kopans D, Parisky YR, Sardi A, Esserman L, Rust S, Michaelson
J Racial differences in mammographic breast density. CANCER 98:590-6 2003

11. Michaelson JS, Satija S, Kopans DB, Moore RA, Silverstein, M, Comegno A, Hughes K, Taghian A, Powell S, Smith,
B Gauging the Impact of Breast Cancer Screening, in Terms of Tumor Size and Death Rate Cancer 98:2114-24 2003

12. Michaelson JS, Silverstein M, Sgroi D, Cheongsiatmoy JA, Taghian A, Powell S, Hughes K, Comegno A, Tanabe KK,
Smith B The effect of tumor size and lymph node status on breast carcinoma lethality. CANCER 98:2133-43 2003

13. Chen Y, Taghian A, Goldberg S, Assaad S, Abi Raad R, Michaelson J, Powell S Influence of margin status and tumor
bed boost dose on local recurrence rate in breast-conserving therapy: does a higher radiation dose to the tumor bed
overcome the effect of close or positive margin status in breast-conserving therapy? Int J Radiat Oncol Biol Phys
57:S358 2003

14. Jagsi R, Powell S, Raad RA, Goldberg S, Michaelson J, Taghian A Loco-regional recurrence rates and prognostic
factors for failure in node-negative patients treated with mastectomy alone: implications for postmastectomy radiation.
Int J Radiat Oncol Biol Phys 57:S128-9 2003

15. Blanchard K, Weissman J, MoyB, PuriD, Kopans D,, , Kaine E, MooreR, Halpern E, Hughes K, Tanabe K, Smith B
Michaelson J, Mammographic screening: Patterns of use and estimated impact on breast carcinoma survival Cancer
101, 495-507 2004

16. Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, Fuller A, Muir B, Mohapatra G, Salunga R, Tuggle JT, Tran Y,
Tran D, Tassin A, Amon P, Wang W, Wang W, Enright E, Stecker K, Estepa-Sabal E, Smith B, Younger J, Balis U,
Michaelson J, Bhan A, Habin K, Baer TM, Brugge J, Haber DA, Erlander MG, Sgroi DC. A two-gene expression ratio
predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell. 2004 Jun;5(6):607-16.

17. Jones JL, Hughes KS, Kopans DB, Moore RH, Howard-McNatt M, Hughes SS, Lee NY, Roche CA, Siegel N, Gadd
MA, Smith BL, Michaelson JS. Evaluation of hereditary risk in a mammography population. Clin Breast Cancer. 2005
Apr;6(1):38-44.

18. Colbert, J Bigby JA, Smith D, Moore R, Rafferty E, Georgian-Smith D, D’Alessandro HA, Yeh E, Kopans DB, Halpern
E, Hughes K, Smith BL, Tanabe KK, Michaelson J. The Age at Which Women Begin Mammographic Screening.
CANCER: 101, 1850-1859

19. Blanchard K, Colbert J, Kopans D, Moore R, Halpern E, Hughes K, Tanabe K, Smith BL, Michaelson JS. The Risk of
False Positive Screening Mammograms, as a Function of Screening Usage. RADIOLOGY, 240: 335 – 342 2006

20. Jagsi R, Raad RA, Goldberg S, Sullivan T, Michaelson J, Powell SN, Taghian AG. Locoregional recurrence rates and
prognostic factors for failure in node-negative patients treated with mastectomy: implications for postmastectomy
radiation. Int J Radiat Oncol Biol Phys. 2005 Jul 15;62(4):1035-9.

21. Dominguez FJ, Jones JL, Zabicki K, Smith BL, Gadd MA, Specht M, Kopans DB, Moore RH, Michaelson JS, Hughes
KS. Prevalence of hereditary breast/ovarian carcinoma risk in patients with a personal history of breast or ovarian
carcinoma in a mammography population. Cancer. 2005 Nov 1;104(9):1849-53.

22. Livestro DP, Muzikansky A, Kaine EM, Flotte TJ, Sober AJ, Mihm MC Jr, Michaelson JS, Cosimi AB, Tanabe KK. A
Case-Control Study of Desmoplastic Melanoma J Clin Oncol. 2005 Sep 20;23(27):6739-46.

23. Michaelson JS, Cheongsiatmoy JA Dewey F, Silverstein M, Sgroi D Smith B. Tanabe KK, The Spread of Human
Cancer Cells Occurs with Probabilities Indicative of A Non Genetic Mechanism British Journal of Cancer 93:1244-
1249 2005

24. Zabicki K, Colbert JA, Dominguez FJ, Gadd MA, Hughes KS, Jones JL, Specht MC, Michaelson JS, Smith BL. Breast
cancer diagnosis in women < or = 40 versus 50 to 60 years: increasing size and stage disparity compared with older
women over time. Ann Surg Oncol. 2006 Aug;13(8):1072-7

25. Rusby JE, Brachtel EF, Michaelson JS, Koerner FC, Smith BL. Breast Duct Anatomy in the Human Nipple: Three-
Dimensional Patterns and Clinical Implications Breast Cancer Research and Treatment Jan 2007

26. Livestro DP, Kaine EM, Michaelson JS, Mihm MC, Haluska FC, Muzikansky A, Sober AJ, Tanabe KK, M.D.
Melanoma of the young: differences and similarities with adult melanoma, a case-matched controlled analysis Cancer
Aug 1;110(3):614-24 2007

27. Pawlik TM, Gleisner AL, Bauer TW, Adams RB, Reddy SK, Clary BM, Martin RC, Scoggins CR, Tanabe KK,
Michaelson JS, Kooby DA, Staley CA, Schulick RD, Vauthey JN, Abdalla EK, Curley SA, Choti MA, Elias D. Liver-
Directed Surgery for Metastatic Squamous Cell Carcinoma to the Liver: Results of a Multi-Center Analysis. Ann Surg
Oncol. Jun 6 2007

28. Virani S, Michaelson JS, Hutter MM, Lancaster RT, Warshaw AL, Henderson WG, Khuri SF, Tanabe KK. Morbidity
and mortality after liver resection: results of the patient safety in surgery study. J Am Coll Surg. Jun;204(6):1284-92
2007.

29. Michaelson J, Reducing Delay in the Detection and Treatment of Breast Cancer. Adv Imag Onc In 2007

30. Michaelson J, Mammographic Screening: Impact on Survival in CANCER IMAGING Ed: M.A. Hayat in 2007

31. Dominguez FJ, Golshan M, Black DM, Hughes KS,Gadd MA, Christian R, Lesnikoski B, Specht M, Michselson JS,
Smith BL Sentinel Node Biopsy is Important in Mastectomy for Ductal Carcinoma in Situ Ann Surgical Oncology
2008 Jan;15(1):268-73.

32. Rusby JE, Kirstein LJ, Brachtel EF, Michaelson JS, Koerner FC, Smith BL Nipple-sparing mastectomy: Lessons from
ex-vivo procedures The Breast Journal. 2008 Sep-Oct;14(5):464-70

33. Rusby JE, Brachtel EF, Taghian AG, Michaelson JS, Koerner FC, Smith BL. Microscopic anatomy within the nipple:
Implications for nipple sparing mastectomy. American Journal of Surgery 2007 Oct;194(4):433-7

34. Murphy CD, Jones JL, Javid SJ, Michaelson JS, Nolan ME, Lipsitz SR, Specht MC, Lesnikoski B, Hughes KS, Gadd
MA, Smith BL,Do Sentinel Node Micrometastases Predict Recurrence Risk in Ductal Carcinoma in Situ and Ductal
Carcinoma in Situ with Microinvasion? American Journal of Surgery Volume 196, Issue 4, Pages 566-568 2008

35. Cady B, Nathan, NR , Michaelson JS, Golshan M , Smith BL , Matched Pair Analyses of Stage IV Breast Cancer With
or Without Resection of Primary Breast Site J Surgical Oncology 2008 Dec;15(12):3384-95 2008

36. Rusby JE, Brachtel EF, Othus M, Michaelson JS, Koerner FC and Smith BL, Development and validation of a model
predictive of occult nipple involvement in women undergoing mastectomy British Journal of Surgery 2008; 95: 1356–
1361

37. Samphao S, Wheeler AJ, Rafferty E, Michaelson JS, Specht MC, Gadd MA, Hughes KS, Smith BL. Diagnosis of
breast cancer in women age 40 and younger: delays in diagnosis result from underuse of genetic testing and breast
imaging. Am J Surg. 2009 Oct;198(4):538-43

38. Michaelson JS,Chen LL, Silverstein M Mihm MV, Jr., Sober AJ, Tanabe KK, Smith BL, Younger J. How Cancer at
The Primary Site And In The Nodes Contributes To The Risk Of Cancer Death CANCER Nov 1;115(21):5095-107
2009

39. Michaelson JS,Chen LL, Silverstein M, Cheongsiatmoy JA, Mihm MV, Jr., Sober AJ, Tanabe KK, Smith BL, Younger
J. Why Cancer at The Primary Site And In The Nodes Contributes To The Risk Of Cancer Death CANCER Nov
1;115(21):5084-94 2009

40. Chen LL, Nolan, M, Silverstein M, Mihm MV, Jr., Sober AJ, Tanabe KK, Smith BL, Younger J., Michaelson JS, The
Impact Of Primary Tumor Size, Nodal Status, And Other Prognostic Factors On The Risk Of Cancer Death CANCER
2Nov 1;115(21):5071-83 2009

41. Tanabe KK, Jara S, Michaelson J. Creating and providing predictions of melanoma outcome. Ann Surg Oncol. 2010
Aug;17(8):1981-2.

42. Pandalai PK, Dominguez FJ, Michaelson J, Tanabe KK. Clinical Value of Radiographic Staging in Patients
Diagnosed With AJCC Stage III Melanoma. Ann Surg Oncol. 2011 Feb;18(2):506-13

43. Cady B, Michaelson JS Chung MA, The “Tipping Point” for breast cancer mortality decline has resulted from size
reductions due to mammographic screening Annals of Surgical Oncology in Press 2011

44. Bush D, Smith B, Younger J, Michaelson JS. The non-breast-cancer death rate among breast cancer patients. Breast
Cancer Res Treat. 2010 Oct 7

45. Michaelson JS, Chen L, Bush D, Smith B, Younger J, Improved web-based calculators for predicting breast
carcinoma outcomes. Breast Cancer Res Treat. In Press 2011

46. Emmons KM, Cleghorn D, Tellez T, Greaney ML, Sprunck KM, Bastani R, Battaglia T, Michaelson JS, Puleo E.
Prevalence and implications of multiple cancer screening needs among Hispanic community health center patients.
Cancer Causes Control. 2011 011 Sep;22(9):1343-9

47. Michaelson JS, Chen LL, Bush D, Fong A, Smith B, Younger J. Improved web-based calculators for predicting breast
carcinoma outcomes. Breast Cancer Res Treat. 2011 Aug;128(3):827-35

48. Barnes JA, Lacasce AS, Feng Y, Toomey CE, Neuberg D, Michaelson JS, Hochberg EP, Abramson JS. Evaluation of
the addition of rituximab to CODOX-M/IVAC for Burkitt's lymphoma: a retrospective analysis. Ann Oncol. 2011
Aug;22(8):1859-64. Epub 2011 Feb 21.

49. Rich, S, Ali S Calkins, J, Michaelson J. Survival trends in childhood hematological malignancies Int. J. Biomath.B 05,
1250053 (2012)

50. Wender R, Fontham ET, Barrera E Jr, Colditz GA, Church TR, Ettinger DS, Etzioni R, Flowers CR, Scott Gazelle G,
Kelsey DK, Lamonte SJ, Michaelson JS, Oeffinger KC, Shih YC, Sullivan DC, Travis W, Walter L, Wolf AM, Brawley
OW, Smith RA American Cancer Society lung cancer screening guidelines.CA Cancer J Clin. 2013 Jan 11.

51. Valsangkar NP, Bush DM, Michaelson JS, Ferrone CR, Wargo JA, Lillemoe KD, Fernández-Del Castillo C, Warshaw
AL, Thayer SP. The Effect of Lymph Node Number on Accurate Survival Prediction in Pancreatic Ductal
Adenocarcinoma. J Gastrointest Surg. 2013 Feb;17(2):257-66.

Laboratory of Quantitative Medicine Technical Reports

(Available at http://www.lifemath.net/cancer/about...orts/index.php)

1. Technical Report #1 - Mathematical Methods (March 9, 2009)

2. Technical Report #2 - Equation Parameters (March 9, 2009)

3. Technical Report #3 - Validation: SizeOnly Equation (June 24, 2008)

4. Technical Report #4 - Validation: Size+Nodes Equation (June 26, 2008)

5. Technical Report #5 - Validation: Size+Nodes+PrognosticFactors Equation (July 3, 2008)

6. Technical Report #6 - Comparisons with AdjuvantOnline (July 7, 2008)

7. Technical Report #7a - Partners Breast Cancer Database (May 12, 2008)

8. Technical Report #7b - SEER Breast Cancer Database (May 12, 2008)

9. Technical Report #8 - How and Why Primary Tumor Size, Nodal Status, and Other Prognostic Factors Contribute to
the Risk of Cancer Death (March 9, 2009)

10. Technical Report #9 - Adjuvant Multi-agent Chemotherapy and Tamoxifen Usage Trends for Breast Cancer in the
United States (March 27, 2009)

11. Technical Report #10 - How the CancerMath.net Breast Cancer Calculators Work (April 6, 2009, Updated Nov 28
2009)

12. Technical Report #11 - Comparative Effectiveness Calculators For Predicting Melanoma Death (August 19, 2009)

13. Technical Report #12 - Accuracy of the CancerMath.net Breast Cancer Calculators over 15 years following diagnosis
(August 27, 2009)

14. Technical Report #12b - Accuracy of the CancerMath.net Breast Cancer Calculators (version 2) over 15 years
following diagnosis (August 29, 2009)

15. Technical Report #13 - Computer Simulation Estimation of the Impact of Various Breast Cancer Screening Intervals in
Women of Various Ages (April 5, 2009)

16. Technical Report #14 - Computer Simulation Estimation of the Benefits and Costs of Breast Cancer Chemoprevention
(April 5, 2009)Pre-Course Survey



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