Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification

Harriet L Lancaster; Sunyi Zheng; Olga O Aleshina; Donghoon Yu; Valeria Yu Chernina; Marjolein A Heuvelmans; Geertruida H de Bock; Monique D Dorrius; Jan Willem Gratama; Sergey P Morozov; Victor A Gombolevskiy; Mario Silva; Jaeyoun Yi; Matthijs Oudkerk

doi:10.1016/j.lungcan.2022.01.002

Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification

Lung Cancer. 2022 Mar:165:133-140. doi: 10.1016/j.lungcan.2022.01.002. Epub 2022 Jan 6.

Affiliations

¹ Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Institute for Diagnostic Accuracy, Groningen, Netherlands.
² Department of Radiotherapy, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Institute for Diagnostic Accuracy, Groningen, Netherlands.
³ State Budget-Funded Health Care Institution of the City of Moscow «Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russian Federation.
⁴ Coreline Soft, Seoul, Republic of Korea.
⁵ Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
⁶ Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
⁷ Department of Radiology, Gelre Hospital, Apeldoorn, the Netherlands.
⁸ State Budget-Funded Health Care Institution of the City of Moscow «Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russian Federation; AIRI, Moscow, Russian Federation.
⁹ Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
¹⁰ Institute for Diagnostic Accuracy, Groningen, Netherlands; Faculty of Medical Sciences, University of Groningen, Groningen, Netherlands. Electronic address: m.oudkerk@rug.nl.

PMID: 35123156
DOI: 10.1016/j.lungcan.2022.01.002

Abstract

Objective: To evaluate performance of AI as a standalone reader in ultra-low-dose CT lung cancer baseline screening, and compare it to that of experienced radiologists.

Methods: 283 participants who underwent a baseline ultra-LDCT scan in Moscow Lung Cancer Screening, between February 2017-2018, and had at least one solid lung nodule, were included. Volumetric nodule measurements were performed by five experienced blinded radiologists, and independently assessed using an AI lung cancer screening prototype (AVIEW LCS, v1.0.34, Coreline Soft, Co. ltd, Seoul, Korea) to automatically detect, measure, and classify solid nodules. Discrepancies were stratified into two groups: positive-misclassification (PM); nodule classified by the reader as a NELSON-plus /EUPS-indeterminate/positive nodule, which at the reference consensus read was < 100 mm³, and negative-misclassification (NM); nodule classified as a NELSON-plus /EUPS-negative nodule, which at consensus read was ≥ 100 mm³.

Results: 1149 nodules with a solid-component were detected, of which 878 were classified as solid nodules. For the largest solid nodule per participant (n = 283); 61 [21.6 %; 53 PM, 8 NM] discrepancies were reported for AI as a standalone reader, compared to 43 [15.1 %; 22 PM, 21 NM], 36 [12.7 %; 25 PM, 11 NM], 29 [10.2 %; 25 PM, 4 NM], 28 [9.9 %; 6 PM, 22 NM], and 50 [17.7 %; 15 PM, 35 NM] discrepancies for readers 1, 2, 3, 4, and 5 respectively.

Conclusion: Our results suggest that through the use of AI as an impartial reader in baseline lung cancer screening, negative-misclassification results could exceed that of four out of five experienced radiologists, and radiologists' workload could be drastically diminished by up to 86.7%.

Keywords: Artificial intelligence; Computer-aided detection; Lung cancer; Pulmonary nodule; Screening; Ultra LDCT.