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. 2022 Mar 29;14(7):1729.
doi: 10.3390/cancers14071729.

Cost-Effectiveness of Artificial Intelligence Support in Computed Tomography-Based Lung Cancer Screening

Sebastian Ziegelmayer  1 , Markus Graf  1 , Marcus Makowski  1 , Joshua Gawlitza  1 , Felix Gassert  1
Affiliations

Cost-Effectiveness of Artificial Intelligence Support in Computed Tomography-Based Lung Cancer Screening

Sebastian Ziegelmayer et al. Cancers (Basel). .

Abstract

Background: Lung cancer screening is already implemented in the USA and strongly recommended by European Radiological and Thoracic societies as well. Upon implementation, the total number of thoracic computed tomographies (CT) is likely to rise significantly. As shown in previous studies, modern artificial intelligence-based algorithms are on-par or even exceed radiologist's performance in lung nodule detection and classification. Therefore, the aim of this study was to evaluate the cost-effectiveness of an AI-based system in the context of baseline lung cancer screening.

Methods: In this retrospective study, a decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Literature research was performed to determine model input parameters. Model uncertainty and possible costs of the AI-system were assessed using deterministic and probabilistic sensitivity analysis.

Results: In the base case scenario CT + AI resulted in a negative incremental cost-effectiveness ratio (ICER) as compared to CT only, showing lower costs and higher effectiveness. Threshold analysis showed that the ICER remained negative up to a threshold of USD 68 for the AI support. The willingness-to-pay of USD 100,000 was crossed at a value of USD 1240. Deterministic and probabilistic sensitivity analysis showed model robustness for varying input parameters.

Conclusion: Based on our results, the use of an AI-based system in the initial low-dose CT scan of lung cancer screening is a feasible diagnostic strategy from a cost-effectiveness perspective.

Keywords: AI-support system; cost-effectiveness analysis; deep learning; lung cancer screening.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Markov model with possible states of disease and transition probabilities between states. BC = bronchial cancer; LT = life tables.
Figure 2
Figure 2
Roll-back of the economic model showing costs and effectiveness of the different outcomes. Distributions leading to overall costs and effectiveness are different for CT and CT + AI depending on sensitivity and specificity of the two methods and indicated as probabilities. BC = bronchial cancer; CT = computed tomography; TP = true positive; TN = true negative; FP = false positive; FN = false negative; Prob = probability.
Figure 3
Figure 3
Probabilistic sensitivity analysis utilizing Monte-Carlo simulations (30,000 iterations). Incremental cost-effectiveness scatter plot for CT + AI vs. CT. iterations with an ICER-value below the willingness-to-pay of USD 100,000 per QALY are shown as green crosses. WTP = willingness-to-pay.
Figure 4
Figure 4
(A) Tornado diagram showing the impact of input parameters on incremental cost-effectiveness ratio (ICER) in the base case scenario. Assuming a willingness-to-pay threshold of USD 100,000 per QALY, CT + AI remained cost-effective in all cases. (B) Tornado diagram showing the impact of input parameters on incremental cost-effectiveness ratio (ICER) when costs of AI were set to USD 1240 with an expected value of USD 100,000 per QALY. Blue bars show changes when decreasing the value of an input parameter as compared to the base case scenario and red bars when increasing the respective value. Sens = sensitivity; Spec = specificity; CT = computed tomography; AI = artificial intelligence; P = probability.
Figure 5
Figure 5
One-way sensitivity analysis for costs of AI (USD) and the corresponding incremental cost effectiveness ratio (ICER in USD/QALY). Thresholds indicate values at an ICER of USD 0/QALY and USD 100,000/QALY. ICER = incremental cost-effectiveness ratio; AI = artificial intelligence; QALY = quality adjusted life year.
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