| Literature DB >> 33686457 |
Felix G Gassert1, Johannes Rübenthaler2, Clemens C Cyran2, Johann S Rink3, Vincent Schwarze2, Johanna Luitjens2, Florian T Gassert1, Marcus R Makowski1, Stefan O Schoenberg3, Marius E Mayerhoefer4, Dietmar Tamandl5, Matthias F Froelich6.
Abstract
PURPOSE: Rectal cancer is one of the most frequent causes of cancer-related morbidity and mortality in the world. Correct identification of the TNM state in primary staging of rectal cancer has critical implications on patient management. Initial evaluations revealed a high sensitivity and specificity for whole-body PET/MRI in the detection of metastases allowing for metastasis-directed therapy regimens. Nevertheless, its cost-effectiveness compared with that of standard-of-care imaging (SCI) using pelvic MRI + chest and abdominopelvic CT is yet to be investigated. Therefore, the aim of this study was to analyze the cost-effectiveness of whole-body 18F FDG PET/MRI as an alternative imaging method to standard diagnostic workup for initial staging of rectal cancer.Entities:
Keywords: Cost-effectiveness; PET/MRI; Rectal cancer; Staging
Mesh:
Substances:
Year: 2021 PMID: 33686457 PMCID: PMC8426298 DOI: 10.1007/s00259-021-05193-7
Source DB: PubMed Journal: Eur J Nucl Med Mol Imaging ISSN: 1619-7070 Impact factor: 9.236
Fig. 1Decision model for strategies CT + pelvic MRI and wb 18F FDG PET/MRI. For each outcome, a Markov model analysis was performed (a). Markov model with potential states of disease. The first state was determined depending on the outcomes in the decision model (b). Ca carcinoma, CT computed tomography, MRI magnetic resonance imaging, PET positron emission tomography, wb whole body, M1 with metastases, M0 without metastases, M Markov model, N negative, P positive
Model input parameters
| Variable | Estimate | Source |
|---|---|---|
| Pre-test probability of initial M1 tumor | 22% | Noone et al. 2018 |
| Expected age at diagnostic procedure | 67 years | Noone et al. 2018 |
| Assumed willingness to pay per QALY | $100,000.00 | Assumption |
| Discount rate | 3% | Assumption |
| Markov model time horizon | 5 years | Assumption |
| Diagnostic test performances | ||
| MRI sensitivity for M1 | 94 [69.8; 99.8] | Yoon et al. 2019 |
| MRI specificity for M1 | 98 [90.3; 99.9] | Yoon et al. 2019 |
| CT + pelvic MRI sensitivity for M1 | 94 [69.8; 99.8] | Yoon et al. 2019 |
| CT + pelvic MRI specificity for M1 | 73 [59.0; 83.9] | Yoon et al. 2019 |
| Costs (acute) | ||
| PET/MRI | $1443.00 | Medicare (Ref. No. 78813) |
| CT whole body | $586.00 | Medicare (Ref. No. 71260 + 74177) |
| Pelvic MRI | $385.00 | Medicare (Ref. No. 72197) |
| Biopsy | $1375.00 | Medicare (Ref. No. 47000) |
| Probability of biopsy | 20% | Expert opinion |
| Ablation | $4595.00 | Medicare (Ref. No. 47382) |
| Costs (long term) | ||
| M0 yearly | $22,571.80 (first year) $1172.00 (following years) | Joranger et al. 2018 |
| Therapy for patients with M1 | $51,706.8 | Joranger et al. 2018 |
| Death | 0 | |
| Utilities | ||
| M0 yearly | 0.79 (first year), 0.87 (following years) | Calderon et al. 2019, Ratjen et al. 2018 |
| M1 without ablation yearly | 0.66 | Joranger et al. 2018, Fiori et al. 2019 |
| M1 with ablation yearly | 0.715 (first year), 0.87 (following years) | Calderon et al. 2019, Helou et al. 2019 |
| QOL after biopsy | 0.995 | Adapted from Feldmann et al. 2018 |
| Death | 0 | Assumption |
| Transition probabilities | ||
| Probability of secondary occurrence of M1 after resection of primarius | 3.30% | Augestad et al. 2015 |
| Probability of occurrence of M1 after ablation | 29.00% | Lintoiu-Ursut et al. 2015 |
| Probability of death with M0 | 6.60% | Arias et al. 2019 |
| Probability of death with ablated M1 | 6.60% | Arias et al. 2019 |
| Probability of death with unablated M1 | 32.00% | Arias et al. 2019 |
| Percentage of ablatable M1 lesions | 17.00% | Brouwer et al. 2018 |
| Percentage of new ablatable lesions in M0 | 0.56% | Brouwer et al. 2018, Augestad et al. 2015 |
| Percentage of new unablatable lesions in M0 | 2.74% | Brouwer et al. 2018, Augestad et al. 2016 |
QALY quality-adjusted life years, QOL quality of life, MRI magnetic resonance imaging, PET positron emission tomography, CT computed tomography, M0 no metastases, M1 with metastases
Fig. 2Markov simulation for 5 years. Outcomes for patients with metastatic disease receiving a timely treatment (true positive) (A). Outcomes for patients with metastatic disease receiving a delayed treatment (false negative) (B). Outcomes for patients without metastatic disease (true negative and false positive) (C)
Fig. 3Tornado diagram showing the impact of input parameters on the incremental cost-effectiveness ratio (ICER) starting from the expected value in the base-case scenario. Assuming a willingness-to-pay threshold of $100,000 per QALY, PET/MRI loses its dominance at costs of $1592. For all other parameters investigated, the ICER of 18F FDG PET/MRI remained below the willingness-to-pay threshold, indicating the cost-effectiveness of 18F FDG PET/MRI in this setting. MRI magnetic resonance imaging, PET positron emission tomography, M0 no metastases, M1 with metastases, QALY quality-adjusted life years
Fig. 4Probabilistic sensitivity analysis utilizing Monte Carlo simulations with 30,000 iterations. Incremental cost-effectiveness scatterplot PET/MRI versus CT + pelvic MRI (a). Cost-effectiveness acceptability curve dependent on willingness to pay (WTP) (b). PET/MRI is cost-effective in the majority of iterations above a WTP threshold of $70,291