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Insights into Imaging

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Open Access 01-12-2023 | Computed Tomography | Original Article

Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening

Authors: Yihui Du, Marcel J. W. Greuter, Mathias W. Prokop, Geertruida H. de Bock

Published in: Insights into Imaging | Issue 1/2023

Abstract

Objective

An increasing number of commercial deep learning computer-aided detection (DL-CAD) systems are available but their cost-saving potential is largely unknown. This study aimed to gain insight into appropriate pricing for DL-CAD in different reading modes to be cost-saving and to determine the potentially most cost-effective reading mode for lung cancer screening.

Methods

In three representative settings, DL-CAD was evaluated as a concurrent, pre-screening, and second reader. Scoping review was performed to estimate radiologist reading time with and without DL-CAD. Hourly cost of radiologist time was collected for the USA (€196), UK (€127), and Poland (€45), and monetary equivalence of saved time was calculated. The minimum number of screening CTs to reach break-even was calculated for one-time investment of €51,616 for DL-CAD.

Results

Mean reading time was 162 (95% CI: 111–212) seconds per case without DL-CAD, which decreased by 77 (95% CI: 47–107) and 104 (95% CI: 71–136) seconds for DL-CAD as concurrent and pre-screening reader, respectively, and increased by 33–41 s for DL-CAD as second reader. This translates into €1.0–4.3 per-case cost for concurrent reading and €0.8–5.7 for pre-screening reading in the USA, UK, and Poland. To achieve break-even with a one-time investment, the minimum number of CT scans was 12,300–53,600 for concurrent reader, and 9400–65,000 for pre-screening reader in the three countries.

Conclusions

Given current pricing, DL-CAD must be priced substantially below €6 in a pay-per-case setting or used in a high-workload environment to reach break-even in lung cancer screening. DL-CAD as pre-screening reader shows the largest potential to be cost-saving.

Critical relevance statement

Deep-learning computer-aided lung nodule detection (DL-CAD) software must be priced substantially below 6 euro in a pay-per-case setting or must be used in high-workload environments with one-time investment in order to achieve break-even. DL-CAD as a pre-screening reader has the greatest cost savings potential.

Key points

• DL-CAD must be substantially below €6 in a pay-per-case setting to reach break-even.

• DL-CAD must be used in a high-workload screening environment to achieve break-even.

• DL-CAD as a pre-screening reader shows the largest potential to be cost-saving.

Graphical Abstract

Available only for authorised users

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Title: Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening
Authors: Yihui Du
Marcel J. W. Greuter
Mathias W. Prokop
Geertruida H. de Bock
Publication date: 01-12-2023
Publisher: Springer Vienna
Keywords: Computed Tomography
Computed Tomography
Lung Cancer
Lung Cancer
Lung Cancer
Published in: Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-023-01561-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening

Abstract

Objective

Methods

Results

Conclusions

Critical relevance statement

Key points

Graphical Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusions

Critical relevance statement

Key points

Graphical Abstract

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