A MDP model for breast and ovarian cancer intervention strategies for BRCA1/2 mutation carriers

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

The most appropriate next step in depression treatment after the initial treatment fails is unclear. This study explores the suitability of the Markov decision process for optimizing sequential treatment decisions for depression. We conducted a formal comparison of a Markov decision process approach and mainstream state-transition models as used in health economic decision analysis to clarify differences in the model structure. We performed two reviews: the first to identify existing applications of the Markov decision process in the field of healthcare and the second to identify existing health economic models for depression. We then illustrated the application of a Markov decision process by reformulating an existing health economic model. This provided input for discussing the suitability of a Markov decision process for solving sequential treatment decisions in depression. The Markov decision process and state-transition models differed in terms of flexibility in modeling actions and rewards. In all, 23 applications of a Markov decision process within the context of somatic disease were included, 16 of which concerned sequential treatment decisions. Most existing health economic models relating to depression have a state-transition structure. The example application replicated the health economic model and enabled additional capacity to make dynamic comparisons of more interventions over time than was possible with traditional state-transition models. Markov decision processes have been successfully applied to address sequential treatment-decision problems, although the results have been published mostly in economics journals that are not related to healthcare. One advantage of a Markov decision process compared with state-transition models is that it allows extended action space: the possibility of making dynamic comparisons of different treatments over time. Within the context of depression, although existing state-transition models are too basic to evaluate sequential treatment decisions, the assumptions of a Markov decision process could be satisfied. The Markov decision process could therefore serve as a powerful model for optimizing sequential treatment in depression. This would require a sufficiently elaborate state-transition model at the cohort or patient level.

Optimal breast cancer risk reduction policies tailored to personal risk level

Depending on personal and hereditary factors, each woman has a different risk of developing breast cancer, one of the leading causes of death for women. For women with a high-risk of breast cancer, their risk can be reduced by two main therapeutic approaches: 1) preventive treatments such as hormonal therapies (i.e., tamoxifen, raloxifene, exemestane); or 2) a risk reduction surgery (i.e., mastectomy). Existing national clinical guidelines either fail to incorporate or have limited use of the personal risk of developing breast cancer in their proposed risk reduction strategies. As a result, they do not provide enough resolution on the benefit-risk trade-off of an intervention policy as personal risk changes. In addressing this problem, we develop a discrete-time, finite-horizon Markov decision process (MDP) model with the objective of maximizing the patient's total expected quality-adjusted life years. We find several useful insights some of which contradict the existing national breast cancer risk reduction recommendations. For example, we find that mastectomy is the optimal choice for the border-line high-risk women who are between ages 22 and 38. Additionally, in contrast to the National Comprehensive Cancer Network recommendations, we find that exemestane is a plausible, in fact, the best, option for high-risk postmenopausal women.

Deep Reinforcement Learning for Weakly-Supervised Lymph Node Segmentation in CT Images

Accurate and automated lymph node segmentation is pivotal for quantitatively accessing disease progression and potential therapeutics. The complex variation of lymph node morphology and the difficulty of acquiring voxel-wise manual annotations make lymph node segmentation a challenging task. Since the Response Evaluation Criteria in Solid Tumors (RECIST) annotation, which indicates the location, length, and width of a lymph node, is commonly available in hospital data archives, we advocate to use RECIST annotations as the supervision, and thus formulate this segmentation task into a weakly-supervised learning problem. In this paper, we propose a deep reinforcement learning-based lymph node segmentation (DRL-LNS) model. Based on RECIST annotations, we segment RECIST-slices in an unsupervised way to produce pseudo ground truths, which are then used to train U-Net as a segmentation network. Next, we train a DRL model, in which the segmentation network interacts with the policy network to optimize the lymph node bounding boxes and segmentation results simultaneously. The proposed DRL-LNS model was evaluated against three widely used image segmentation networks on a public thoracoabdominal Computed Tomography (CT) dataset that contains 984 3D lymph nodes, and achieves the mean Dice similarity coefficient (DSC) of 77.17% and the mean Intersection over Union (IoU) of 64.78% in the four-fold cross-validation. Our results suggest that the DRL-based bounding box prediction strategy outperforms the label propagation strategy and the proposed DRL-LNS model is able to achieve the state-of-the-art performance on this weakly-supervised lymph node segmentation task.

Hierarchical Gaussian Process Modeling and Estimation of State-action Transition Dynamics in Breast Cancer

Breast cancer is the most prevalent type of cancer in the US. Available treatments, including mastectomy, radiation, and chemotherapy, vary in curability, cost, and mortality probability of patients. This research aims at tracking the result of post-treatment for evidence-based decision making in breast cancer. Based on available big data, we implemented conditional probability to estimate multi-age transition probability matrices to predict the progression of disease conditions. The patient state is defined based on patients' age, cancer stage, and treatment history. To tackle the incomplete data in the matrix, we design a novel Hierarchical Gaussian Distribution (HGP) to estimate the missing part of the table. The HGP model leads to the lowest Root Mean Square Error (RMSE), which is 35% lower than the Gaussian Process and 40% lower than Linear Regression. Results of transition probability estimation show that the chance of survival within a year for 40 to 50 years old patient with the distant stage of cancer is 96.5%, which is higher than even younger age groups.

Markov Decision Process Modeling for Multi-stage Optimization of Intervention and Treatment Strategies in Breast Cancer

The breast cancer is a prevalent problem that undermines quality of patients' lives and causes significant impacts on psychosocial wellness. Advanced sensing provides unprecedented opportunities to develop smart cancer care. The available sensing data captured from individuals enable the extraction of information pertinent to the breast cancer conditions to construct efficient and personalized intervention and treatment strategies. This research develops a novel sequential decision-making framework to determine optimal intervention and treatment planning for breast cancer patients. We design a Markov decision process (MDP) model for both objectives of intervention and treatment costs as well as quality adjusted life years (QALYs) with the data-driven and state-dependent intervention and treatment actions. The state space is defined as a vector of age, health status, prior intervention, and treatment plans. Also, the action space includes wait, prophylactic surgery, radiation therapy, chemotherapy, and their combinations. Experimental results demonstrate that prophylactic mastectomy and chemotherapy are more effective than other intervention and treatment plans in minimizing the expected cancer cost of 25 to 60 years-old patient with in-situ stage of cancer. However, wait policy leads to an optimal quality of life for a patient with the same state. The proposed MDP framework can also be generally applicable to a variety of medical domains that entail evidence-based decision making.

A Systematic Review on Cost-effectiveness Studies Evaluating Ovarian Cancer Early Detection and Prevention Strategies

Ovarian cancer imposes a substantial health and economic burden. We systematically reviewed current health-economic evidence for ovarian cancer early detection or prevention strategies. Accordingly, we searched relevant databases for cost-effectiveness studies evaluating ovarian cancer early detection or prevention strategies. Study characteristics and results including quality-adjusted life years (QALY), and incremental cost-effectiveness ratios (ICER) were summarized in standardized evidence tables. Economic results were transformed into 2017 Euros. The included studies (N = 33) evaluated ovarian cancer screening, risk-reducing interventions in women with heterogeneous cancer risks and genetic testing followed by risk-reducing interventions for mutation carriers. Multimodal screening with a risk-adjusted algorithm in postmenopausal women achieved ICERs of 9,800-81,400 Euros/QALY, depending on assumptions on mortality data extrapolation, costs, test performance, and screening frequency. Cost-effectiveness of risk-reducing surgery in mutation carriers ranged from cost-saving to 59,000 Euros/QALY. Genetic testing plus risk-reducing interventions for mutation carriers ranged from cost-saving to 54,000 Euros/QALY in women at increased mutation risk. Our findings suggest that preventive surgery and genetic testing plus preventive surgery in women at high risk for ovarian cancer can be considered effective and cost-effective. In postmenopausal women from the general population, multimodal screening using a risk-adjusted algorithm may be cost-effective.

The Effect of Prophylactic Adnexectomy on the Quality of Life and Psychosocial Functioning of Women with the BRCA1/BRCA2 Mutations

The main purpose of this study was to analyze the effect of risk-reducing salpingo-oophorectomy (RRSO) on the quality of life (QoL) and psychosocial functioning of patients with the BRCA1/BRCA2 mutations. This survey-based study was conducted using the Blatt-Kupperman Index, the Women’s Health Questionnaire, the Perceived Stress Scale, the State-Trait Anxiety Inventory, the Beck Depression Inventory-II, and the authors’ questionnaire. All calculations were done using Statistica 13.3. The QoL after RRSO was statistically significantly lower in most domains compared with the state before surgery. The greatest decline in the QoL was observed in the vasomotor symptoms domain (d = 0.953) and the smallest in the memory/concentration domain (d = 0.167). We observed a statistically significant decrease in the level of anxiety as a state (d = 0.381), as well as a statistically significant increase in the severity of climacteric symptoms (d = 0.315) and depressive symptoms (d = 0.125). Prophylactic surgeries of the reproductive organs have a negative effect on the QoL and psychosocial functioning of women with the BRCA1/2 mutations, as they increase the severity of depressive and climacteric symptoms. At the same time, these surgeries reduce anxiety as a state, which may be associated with the elimination of cancerophobia.

Decision making for breast cancer prevention among women at elevated risk

Several medical management approaches have been shown to be effective in preventing breast cancer and detecting it early among women at elevated risk: 1) prophylactic mastectomy; 2) prophylactic oophorectomy; 3) chemoprevention; and 4) enhanced screening routines. To varying extents, however, these approaches are substantially underused relative to clinical practice recommendations. This article reviews the existing research on the uptake of these prevention approaches, the characteristics of women who are likely to use various methods, and the decision-making processes that underlie the differing choices of women. It also highlights important areas for future research, detailing the types of studies that are particularly needed in four key areas: documenting women's perspectives on their own perceptions of risk and prevention decisions; explicit comparisons of available prevention pathways and their likely health effects; the psychological, interpersonal, and social processes of prevention decision making; and the dynamics of subgroup variation. Ultimately, this research could support the development of interventions that more fully empower women to make informed and values-consistent decisions, and to move towards favorable health outcomes.