Staging MR Lymphangiography of the Axilla for Early Breast Cancer: Cost-Effectiveness Analysis
Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St., 10th Fl., Boston, MA 02114, USA. American Journal of Roentgenology
(Impact Factor: 2.73).
12/2008; 191(5):1308-19. DOI: 10.2214/AJR.07.3861
The purpose of this study was to compare the cost-effectiveness of MR lymphangiography-based strategies with that of sentinel lymph node (SLN) biopsy alone in the axillary staging of early breast cancer.
A decision-analytic Markov Model was developed to estimate quality-adjusted life expectancy and lifetime costs among 61-year-old women with clinically node-negative early breast cancer. Three axillary staging strategies were compared: MR lymphangiography alone, combined MR lymphangiography-SLN biopsy, and SLN biopsy alone. The model incorporated treatment decisions, outcome, and costs consequent to axillary staging results. An incremental cost-effectiveness analysis was performed to compare strategies. The effect of changes in key parameters on results was addressed in sensitivity analysis.
In the base-case analysis, combined MR lymphangiography-SLN biopsy was associated with the highest quality-adjusted life expectancy (13.970 years) and cost ($63,582), followed by SLN biopsy alone (13.958 years, $62,462) and MR lymphangiography alone (13.957 years, $61,605). MR lymphangiography-SLN biopsy and SLN biopsy both were associated with higher life expectancy and cost relative to those of MR lymphangiography. MR lymphangiography-SLN biopsy, however, was associated with greater overall life expectancy and greater added life expectancy per dollar than was SLN biopsy. SLN biopsy alone therefore was not considered cost-effective, but MR lymphangiography and MR lymphangiography-SLN biopsy remained competing choices. Preference of MR lymphangiography strategies was most dependent on the sensitivity of MR lymphangiography and SLN biopsy and on the quality-of-life consequences of SLN biopsy and axillary lymph node dissection, but otherwise was stable across most parameter ranges tested.
From a cost-effectiveness perspective, MR lymphangiography strategies for axillary staging of early breast cancer are preferred over SLN biopsy alone. The sensitivity of MR lymphangiography is a critical determinant of the cost-effectiveness of MR lymphangiography strategies and merits further investigation in the care of patients with early breast cancer.
Available from: Jagpreet Chhatwal
- "In the past, decision analytic modeling has been used in the breast imaging literature, primarily for cost-effectiveness analysis in order to determine the optimal use of competing healthcare interventions.– These manuscripts have used a technique called Markov modeling to evaluate interventions like staging MR lymphangiography , computer-aided detection , breast MRI with core biopsy  and MRI screening in patients with BRCA1 mutations . However, standard Markov models can evaluate only one set of decision rules at a time and a single model must be created for each strategy being analyzed. "
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A 2% threshold, traditionally used as a level above which breast biopsy recommended, has been generalized to all patients from several specific situations analyzed in the literature. We use a sequential decision analytic model considering clinical and mammography features to determine the optimal general threshold for image guided breast biopsy and the sensitivity of this threshold to variation of these features.
We built a decision analytical model called a Markov Decision Process (MDP) model, which determines the optimal threshold of breast cancer risk to perform breast biopsy in order to maximize a patient’s total quality-adjusted life years (QALYs). The optimal biopsy threshold is determined based on a patient’s probability of breast cancer estimated by a logistic regression model (LRM) which uses demographic risk factors (age, family history, and hormone use) and mammographic findings (described using the established lexicon–BI-RADS). We estimate the MDP model's parameters using SEER data (prevalence of invasive vs. in situ disease, stage at diagnosis, and survival), US life tables (all cause mortality), and the medical literature (biopsy disutility and treatment efficacy) to determine the optimal “base case” risk threshold for breast biopsy and perform sensitivity analysis. The base case MDP model reveals that 2% is the optimal threshold for breast biopsy for patients between 42 and 75 however the thresholds below age 42 is lower (1%) and above age 75 is higher (range of 3–5%). Our sensitivity analysis reveals that the optimal biopsy threshold varies most notably with changes in age and disutility of biopsy.
Our MDP model validates the 2% threshold currently used for biopsy but shows this optimal threshold varies substantially with patient age and biopsy disutility.
PLoS ONE 11/2012; 7(11):e48820. DOI:10.1371/journal.pone.0048820 · 3.23 Impact Factor
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ABSTRACT: Soft constraints are introduced in an iterative projection approach, in order to make the set of constraints compatible for the case of noisy measurements. The degrees of freedom in the design are then used to arrive at a computationally simple form of the soft constraint algorithm. Simulation shows that the true solution is still feasible under noisy conditions, a property lost with the use of hard constraint algorithms.
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '84.; 04/1984
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ABSTRACT: OBJECTIVE: This commentary provides a brief overview of cost-effectiveness analysis, which is increasingly applied in radiologic research. The purpose is to familiarize readers with the basic concepts in this topic and to provide help in appraising original articles in this area of research, as featured in this issue of the AJR. CONCLUSION:Despite some limitations, decision-analytic modeling provides a useful tool for cost-effectiveness analysis in emerging technologies and helps to direct future research and the practice of radiology.
American Journal of Roentgenology 12/2008; 191(5):1320-2. DOI:10.2214/AJR.08.1514 · 2.73 Impact Factor
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