Cost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules

Sep 22, 2026

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Artificial intelligence–based radiomic approaches can accurately evaluate indeterminate pulmonary nodules, which are increasingly detected through lung cancer screening and computed tomography. Despite potential to reduce time to diagnosis and avoid invasive procedures for benign disease, the cost-effectiveness of AI-based risk stratification is not established. This study evaluates whether AI-assisted clinician evaluation is cost-effective versus clinician evaluation alone using a payer decision model with lifetime horizon and sensitivity analyses.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Background

Methods

Decision model and outcomes

Sensitivity analyses

Funding and competing interests

Results

OPEN ACCESS Citation: Godfrey CM, Leech AA, McGann KC, Zhu J, Marmor HN, Pena S, et al. (2026) Costeffectiveness analysis of artificial intelligenceassisted risk stratification of indeterminate pulmonary nodules. PLoS One 21(3): e0343492. [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0343492 Editor: Jun Hyeok Lim, Inha University Hospital, KOREA, REPUBLIC OF Received: July 31, 2025 Accepted: February 7, 2026 Published: March 5, 2026 Copyright: © 2026 Godfrey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data availability statement: All relevant data are within the manuscript and its Supporting Information files. We have now made all of raw data available in our submission in our tablesand figures, and included the full variable list file. Collectively, this provides all data required RESEARCH ARTICLE Cost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules Caroline M. Godfrey1, Ashley A. Leech2, Kevin C. McGann1, Jinyi Zhu2, Hannah N. Marmor3, Sophia Pena3, Lyndsey C. Pickup4, Fabien Maldonado5, Evan C. Osmundson6, Stacie B. Dusetzina2, Eric L. Grogan3,7*, Stephen A. Deppen3,7 1 Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America, 2 Department of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America, 3 Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America, 4 Optellum Ltd, Oxford, United Kingdom, 5 Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America, 6 Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America, 7 Tennessee Valley Healthcare System, Nashville, Tennessee, United States of America * [eric.grogan@vumc.org](eric.grogan@vumc.org) Abstract Background Artificial intelligence-based radiomic approaches have been shown to accurately evaluate indeterminate pulmonary nodules. With the expansion of lung cancer screening and utilization of computed tomography imaging, indeterminate pulmonary nodules requiring diagnostic evaluation are increasingly common. Accurate noninvasive characterization may reduce time to cancer diagnosis and decrease invasive procedures for benign disease, but the cost-effectiveness of AI-based methods has not been quantified. We sought to evaluate the cost-effectiveness of AI-assisted clinician evaluation compared to clinician evaluation alone for the cancer risk stratification of patients with indeterminate pulmonary nodules. Methods We constructed a decision model assuming guideline-based care from a payer perspective with a lifetime horizon. The base case is a 1.1 cm incidentally discovered IPN in a 60-year-old operative candidate in a clinical population with a 65% malignancy prevalence. Cost per life-year gained (LYG) was the primary outcome. We conducted deterministic sensitivity analyses on all parameters and performed a probabilistic sensitivity analysis. Given clinical variability of malignancy prevalence, we assessed the malignancy prevalence threshold at which utilization of AI would be cost-effective. PLOS One | [https://doi.org/10.1371/journal.pone.0343492](https://doi.org/10.1371/journal.pone.0343492) March 5, 2026 1 / 14 to duplicate our work. We have additionally included our actual TreeAge software, which if future users wanted/had access to the TreeAge software could be used to run our full model immediately. This is not necessary, however, as the complete raw data and variables list is also provided--meaning the user could run the model on any

FAQs

What clinical problem does the study address?

The study addresses diagnostic decision-making for indeterminate pulmonary nodules that require evaluation to determine cancer risk.

How is AI-assisted evaluation compared in the analysis?

AI-assisted clinician evaluation is compared against clinician evaluation alone for cancer risk stratification.

What method and outcomes are used to assess cost-effectiveness?

A payer perspective decision model with a lifetime horizon is used, with cost per life-year gained (LYG) as the primary outcome, supported by deterministic and probabilistic sensitivity analyses.

Sep 22, 2026

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