Cost-Effectiveness of Artificial Intelligence-Based Opportunistic Compression Fracture Screening of Existing Radiographs.

PURPOSE: Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information available on existing chest and abdominal radiographs, the authors' study group has developed software to access these radiographs for OVCFs with high sensitivity and specificity using an established artificial intelligence deep learning algorithm. The aim of this analysis was to assess the potential cost-effectiveness of implementing this software. METHODS: A deterministic expected-value cost-utility model was created, combining a tree model and a Markov model, to compare the strategies of opportunistic screening for OVCFs against usual care. Total costs and total quality-adjusted life-years were calculated for each strategy. Screening and treatment costs were considered from a limited societal perspective, at 2022 prices. RESULTS: In the base case, assuming a cost of software implantation of $10 per patient screened, the screening strategy dominated the nonscreening strategy: it resulted in lower cost and increased quality-adjusted life-years. The lower cost was due primarily to the decreased costs associated with fracture treatment and decreased probability of requiring long-term care in patients who received preventive treatment. The screening strategy was dominant up to a cost of $46 per patient screened. CONCLUSIONS: Artificial intelligence-based opportunistic screening for OVCFs on existing radiographs can be cost effective from a societal perspective.

Cost-effectiveness analysis of magnetic resonance-guided focused ultrasound ablation for palliation of refractory painful bone metastases.

OBJECTIVE: The aim of this study was to determine if magnetic resonance-guided focused ultrasound (MRgFUS) is cost-effective compared with medication, for refractory pain from bone metastases in the United States. METHODS: We constructed a Markov state transition model using TreeAge Pro software (TreeAge Software, Inc., Williamstown, MA, USA) to model costs, outcomes, and the cost-effectiveness of a treatment strategy using MRgFUS for palliative treatment of painful bone metastases compared with a Medication Only strategy (Figure 1). Model transition state probabilities, costs (in 2018 US$), and effectiveness data (quality-adjusted life-years [QALYs]) were derived from available literature, local expert opinion, and reimbursement patterns at two U.S. tertiary academic medical centers actively performing MRgFUS. Costs and QALYs, discounted at three percent per year, were accumulated each month over a 24-month time horizon. One-way and probabilistic sensitivity analyses were performed. RESULTS: In the base-case analysis, the MRgFUS treatment strategy costs an additional $11,863 over the 2-year time horizon to accumulate additional 0.22 QALYs, equal to a $54,160/QALY ICER, thus making MRgFUS the preferred strategy. One-way sensitivity analyses demonstrate that for the base-case analysis, the crossover point at which Medication Only would instead become the preferred strategy is $23,341 per treatment. Probabilistic sensitivity analyses demonstrate that 67 percent of model iterations supported the conclusion of the base case. CONCLUSIONS: Our model demonstrates that MRgFUS is cost-effective compared with Medication Only for palliation of painful bone metastases for patients with medically refractory metastatic bone pain across a range of sensitivity analyses.

Understanding Cost-Effectiveness Analyses: An Explanation Using Three Different Analyses of Lung Cancer Screening.

OBJECTIVE: Cost-effectiveness analyses (CEAs) contribute to informed decision making, at both the practitioner and societal levels; therefore, understanding CEAs is valuable for radiologists. In light of the recently published National Lung Cancer Screening Trial (NLST) CEA, we aim to explain the terminology, methods, and heterogeneity of CEAs. CONCLUSION: We compared the NLST results to two example lung cancer screening CEAs (which do not rely on NLST data). Both examples assessed screening but reached substantially different conclusions.