Grant Number:	1R21CA129393-01A1 Interpret this number
Primary Investigator:	Alagoz, Oguzhan
Organization:	University Of Wisconsin-Madison
Project Title:	Using Markov Decision Processes to Optimize Breast Biopsy Decision Making
Fiscal Year:	2008

DESCRIPTION (provided by applicant): Project Summary Early diagnosis through screening mammography is the most effective means of decreasing the death rate from breast cancer. While mammography is inexpensive, the interventional procedures that result from detected abnormalities (both false and true positives) increase the cost of this population-based screening program significantly. In fact, breast biopsy actually delivers a benign result in over 80% of cases making it the most costly per capita component of a breast cancer screening program. If a mammogram reports a suspicious finding, then a biopsy is required to decide whether an abnormality is in fact a breast cancer. A false positive mammogram exposes the patient to the anxiety, pain, and possible complications while the health care system bears the cost of potentially unnecessary biopsies. Our previous research has developed a probabilistic computer model called the Mammography Bayesian Network (MBN) that calculates the risk of breast disease based on demographic risk factors and mammography findings. The objective of this research is to optimize the biopsy decisions for breast-cancer patients such that the early diagnosis of invasive breast cancer is improved while unnecessary invasive procedures are minimized. We will calibrate our previously developed MBN, to accurately calculate the risk of breast cancer based on demographic risk factors and mammography findings. We will use Markov decision processes, an advanced decision analysis technique that is used for decision- making under uncertainty, to find the optimal probability thresholds for the decision to perform breast biopsy for patients with different age groups. We will determine whether these optimal probability thresholds change with patient age. Relevance of this research to Public Health: The proposed research will improve the interpretation of screening mammography, the most effective means of decreasing the death rate from breast cancer, which affects millions of women in the US. Any improvement in screening mammography will reduce the costs of unnecessary biopsies to the society.




Predicting invasive breast cancer versus DCIS in different age groups.
Authors: Ayvaci M.U. , Alagoz O. , Chhatwal J. , Munoz del Rio A. , Sickles E.A. , Nassif H. , Kerlikowske K. , Burnside E.S. .
Source: Bmc Cancer, 2014-08-11 00:00:00.0; 14, p. 584.
EPub date: 2014-08-11 00:00:00.0.
PMID: 25112586
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Optimal Policies for Reducing Unnecessary Follow-up Mammography Exams in Breast Cancer Diagnosis.
Authors: Alagoz O. , Chhatwal J. , Burnside E.S. .
Source: Decision Analysis : A Journal Of The Institute For Operations Research And The Management Sciences, 2013 Sep; 10(3), p. 200-224.
PMID: 24501588
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What is the optimal threshold at which to recommend breast biopsy?
Authors: Burnside E.S. , Chhatwal J. , Alagoz O. .
Source: Plos One, 2012; 7(11), p. e48820.
PMID: 23144986
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Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors.
Authors: Chhatwal J. , Alagoz O. , Burnside E.S. .
Source: Operations Research, 2010-11-01 00:00:00.0; 58(6), p. 1577-1591.
PMID: 21415931
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Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration.
Authors: Ayer T. , Alagoz O. , Chhatwal J. , Shavlik J.W. , Kahn C.E. , Burnside E.S. .
Source: Cancer, 2010-07-15 00:00:00.0; 116(14), p. 3310-21.
PMID: 20564067
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Computer-aided diagnostic models in breast cancer screening.
Authors: Ayer T. , Ayvaci M.U. , Liu Z.X. , Alagoz O. , Burnside E.S. .
Source: Imaging In Medicine, 2010-06-01 00:00:00.0; 2(3), p. 313-323.
PMID: 20835372
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Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings.
Authors: Burnside E.S. , Davis J. , Chhatwal J. , Alagoz O. , Lindstrom M.J. , Geller B.M. , Littenberg B. , Shaffer K.A. , Kahn C.E. , Page C.D. .
Source: Radiology, 2009 Jun; 251(3), p. 663-72.
PMID: 19366902
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A logistic regression model based on the national mammography database format to aid breast cancer diagnosis.
Authors: Chhatwal J. , Alagoz O. , Lindstrom M.J. , Kahn C.E. , Shaffer K.A. , Burnside E.S. .
Source: Ajr. American Journal Of Roentgenology, 2009 Apr; 192(4), p. 1117-27.
PMID: 19304723
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