• Hayeon Kim, John A Vargo, Kenneth J Smith, and Sushil Beriwal.
• Department of Radiation Oncology, UPMC Hillman Cancer Center and University of Pittsburgh School of Medicine, Pittsburgh, PA. Electronic address: kimh2@upmc.edu.
• Clin. Breast Cancer. 2021 Jun 1; 21 (3): e271-e278.

BackgroundCurrently it remains difficult to identify patients most likely to benefit from radiotherapy (RT) for ductal carcinoma-in-situ (DCIS), thus leading to wide variation in practice patterns. The genomic risk assessment tool DCISionRT (PreludeDX) has been validated to prognosticate recurrence risk and predict RT benefit. We aimed to study the cost-effectiveness analysis comparing DCIS treatments based on DCISionRT testing to traditional clinicopathologic risk factors.Patients And MethodsA Markov state transition model was constructed to perform a cost-effectiveness analysis comparing breast-conserving surgery with or without RT using DCISionRT testing vs. traditional clinicopathologic risk factors. Clinical parameters were obtained from clinical trial data and cross-validation studies. Cost data were based on 2019 Medicare reimbursement. Incremental cost-effectiveness ratio (ICER) was calculated as incremental cost per quality-adjusted life-year (QALY) gained comparing DCIS treatments using DCISionRT testing to traditional clinicopathologic risk factors and evaluated with a willingness-to-pay threshold of US$100,000 per QALY gained. To account for uncertainty, 1-way and probabilistic sensitivity analyses were performed.ResultsBase case analysis showed that DCIS management using DCISionRT testing was a cost-effective strategy, resulting in an ICER of $74,331 per QALY gained compared to clinicopathology-based treatment. Model results were sensitive to a variation of the proportion of genomic-high, low-risk patients receiving RT in DCISionRT testing strategy, and changes in DCISionRT testing cost.ConclusionDCISionRT testing could potentially be a cost-effective strategy compared to traditional decision making for DCIS treatments, optimizing RT benefit based on an accurate recurrence risk assessment.Copyright © 2020 Elsevier Inc. All rights reserved.

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