Personalizing Cancer Screening Using OR

I have had a strong interest in answering screening policy questions associated with breast cancer since 2006. Questions such as when to start and screening or how often to screen women are among the most controversial issues regarding breast cancer, the most common non-skin cancer in US women. Although mammography is the most effective modality for breast cancer screening, it has several potential risks including high false-positive rates. Therefore, the balance of benefits and risks is critical in designing a mammography screening schedule, which requires a formal framework to evaluate these effects such as simulation modeling.

After I got involved in the breast cancer screening problem, I noticed that most of the existing cancer screening guidelines simply provide very generic and static instructions. For example, many medical organizations recommend that women over 40 (over 50, according to some organizations) should have a screening mammogram every 1 to 2 years and should continue to do so for as long as they are in good health.

On the other hand, there are some factors suggesting that a more precise screening program would perform better than population-based screening programs as suggested by existing guidelines. For example, women with a family history of breast cancer is about twice more likely to develop breast cancer than a woman who does not have a family history of breast cancer. Therefore, women with a family history of cancer may need a more aggressive screening protocol than those without a family history. Furthermore, there is strong evidence that breast cancer is less aggressive in older women which suggests a dynamic personalized breast-cancer screening policy, i.e., a policy that prescribes different screening intervals depending on the woman's age and personalized risk of cancer, might be preferable to a population-based screening strategies recommended by the medical organizations.

Obviously, tailoring clothing to fit a woman is straightforward: take measurements and alter accordingly. Similarly, tailoring breast cancer screening to fit a woman needs to account for individual risk factors, such as age, parity, breast density, obesity, family history, etc. and determine a mammogram schedule accordingly. In fact, individualizing mammography screening decisions based on personal risk characteristics of women is identified as crucial to improve breast cancer diagnosis by numerous researchers and several health organizations. On the other hand, no structured protocol exists to individualize this process.

To this end, along with my students I developed a partially observable Markov decision process (POMDP) model that determines the optimal individualized cancer screening strategies for women with different risk groups. Mammography screening is a very natural application area for POMDPs, that are underutilized in health-care area. More specifically, unlike Markov decision processes (MDPs), POMDPs assume a probability distribution around the true states (i.e. true cancer stage of a patient) and use belief states (i.e. probability of cancer estimate) and observations (i.e. screening test result) to update the belief states. Similar to MDPs, POMDPs also consider sequential decision making which is the case in breast cancer screening since screening decisions for patients need to be made every year.

We then used a previously developed and validated simulation model to estimate the input parameters of the model and solved the POMDP optimally, which is rarely done in the literature due to extensive computational requirements. We found several interesting results such as dynamic screening policies generated by our POMDP increase the societal benefit of mammography significantly. For example, the use of personalized screening strategies would save more than two million life-years for the 40-44 age group in the US alone while recommending 60% fewer mammograms than the existing guidelines.

Figure 1. Optimal Probability of Cancer Threshold to Recommend Mammography for Various Ages

We also found that the mammography screening threshold risk changes with age, being lower in younger women and higher in older women (Figure 1). This is consistent with the knowledge that older women are more likely to suffer from other comorbidities; hence, further invasive tests are often less beneficial for these women. On the other hand, while one would expect that older women need to be screened less aggressively than younger women, we find that this is not always the case. Although this finding appears counterintuitive, because breast cancer risk is also higher in older women, mammography decisions should be determined considering this trade-off. We showed that, under this trade-off, screening is less beneficial for most women over age 74 and provides significant QALY gains especially for the high-risk women in the controversial age group 40-49. In addition, one of the key features of the personalized optimal mammography screening strategy proposed in our study is that it considers not only personal risk characteristics but also the personal history of screening when making recommendations for mammography decisions.

To individualize the mammography screening process, I propose a statistic, i.e. the belief state of the POMDP, which captures possible risk factors and screening history. From a clinical standpoint, this new statistic and our results might be useful for communication between the radiologist, patient, and referring physician. Our findings may in turn facilitate shared decision making, decision making within a patient-–clinician partnership, which is especially recommended for complex decisions such as mammography screening.

My research on mammography screening may not only improve breast cancer screening, but also has the potential to provide a framework for developing better screening policies for other cancers such as prostate and colorectal cancers as well as other diseases such as Human papillomavirus (HPV) with some modifications. Any improvement on cancer screening would directly affect millions of people being screened for cancer and indirectly affect almost the whole population being screened for other diseases. Furthermore, the potential life savings and dollar savings of this research are substantial. In summary, OR has a lot to offer to optimize disease screening & diagnosis and improve health outcomes.

Link to the papers related to this research:

http://homepages.cae.wisc.edu/~alagoz/OR-BCPOMDP.pdf

http://www.informs.org/Pubs/Tutorials-in-OR/2011-TutORials-in-Operations-Research-ONLINE/Chapter-5

alagoz at engr dot wisc dot edu

Research supported in part by NSF Grant CMMI-0844423.

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