Radiologists Versus AI-Based Software: Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma From CT Under Various Image Display Conditions

Sato, Junya; Yanagawa, Masahiro; Nishigaki, Daiki; Hata, Akinori; Sakao, Yukinori; Sakakura, Noriaki; Yatabe, Yasushi; Shintani, Yasushi; Kido, Shoji; Tomiyama, Noriyuki

doi:https://doi.org/10.1016/j.cllc.2024.10.015

Radiologists Versus AI-Based Software: Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma From CT Under Various Image Display Conditions

Sato, Junya; Yanagawa, Masahiro; Nishigaki, Daiki et al.

Clinical Lung Cancer, 2025, 26(1), 58-71

Number of Access:1,299（2025-08-14 15:06 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/100202

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Title	Radiologists Versus AI-Based Software: Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma From CT Under Various Image Display Conditions
Creator	CreatorName Sato, Junya
	NRID 1000000546872 1000000546872 ORCID 0000-0002-0911-6769 0000-0002-0911-6769 researchmap permalink myMY3434 myMY3434 Scopus Author ID 36891136200 36891136200 CreatorName Yanagawa, Masahiro
	ORCID 0000-0002-7006-3408 0000-0002-7006-3408 CreatorName Nishigaki, Daiki
	NRID 1000010838600 1000010838600 researchmap permalink hataakinori hataakinori Scopus Author ID 57188730692 57188730692 CreatorName Hata, Akinori
	NRID 1000000274605 1000000274605 CreatorName Sakao, Yukinori
	CreatorName Sakakura, Noriaki
	NRID 1000090280809 1000090280809 CreatorName Yatabe, Yasushi
	NRID 1000090572983 1000090572983 researchmap permalink shintani6384 shintani6384 Scopus Author ID 7103142734 7103142734 CreatorName Shintani, Yasushi
	NRID 1000090314814 1000090314814 ORCID 0000-0001-5293-4110 0000-0001-5293-4110 researchmap permalink read0196068 read0196068 Scopus Author ID 7004389358 7004389358 CreatorName Kido, Shoji
	NRID 1000050294070 1000050294070 researchmap permalink 3430 3430 Scopus Author ID 7006024942 7006024942 CreatorName Tomiyama, Noriyuki
Subject	Artificial intelligence
	Lung adenocarcinoma
	Lymph node metastasis
	Prognosis
	Tumor measurement
Description	Sato J., Yanagawa M., Nishigaki D., et al. Radiologists Versus AI-Based Software: Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma From CT Under Various Image Display Conditions. Clinical Lung Cancer 26, 58 (2025); https://doi.org/10.1016/j.cllc.2024.10.015.
Abstract	Purpose: To compare the variability of quantitative values from lung adenocarcinoma CT images independently assessed by 2 radiologists and AI-based software under different display conditions, and to identify predictors of pathological lymph node metastasis (LNM), disease-free survival (DFS), and overall survival (OS). Methods: Preoperative CT images of 307 patients were displayed under 4 conditions: lung-1, lung-2, mediastinum-1, and mediastinum-2. Two radiologists (R1, R2) measured total diameter (tD) and the longest solid diameter (sD) under each condition. The AI-based software automatically detected lung nodules, providing tD, sD, total volume (tV), and solid volume (sV). Results: All measurements by R1 and R2 with AI-based software were identical. Four out of the 8 measurements showed significant variation between R1 and R2. For LNM, multivariate logistic regression identified significant indicators including sD at mediastinum-2 of R1, sD at mediastinum-1 and mediastinum-2 of R2, tV, and the proportion of sV to tV (sV/tV) of AI-based software. For DFS, multivariate Cox regression identified sD at lung-1 of R1, the proportions of sD to tD at lung-2 of R1, sD at lung-2 and mediastinum-1 of R2, tV, and sV/tV of AI-based software as significant. For OS, multivariate Cox regression identified sD at lung-1 and mediastinum-2 of R1, tD at lung-2 of R2, sD at mediastinum-1 of R2, sV, and sV/tV of AI-based software as significant. Conclusion: Radiologists’ CT measurements were significant predictors of LNM and prognosis, but variability existed among radiologists and display conditions. AI-based software can provide accurate and reproducible indicators for predicting LNM and prognosis.
Publisher	Elsevier Inc.
Source Title	Clinical Lung Cancer
Volume (Issue)	26(1)
Page	58-71
Date of Issued	2025-01-01
Language	English
Handle URL	https://hdl.handle.net/11094/100202
PISSN	15257304
eISSN	19380690
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1016/j.cllc.2024.10.015
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCTERMS.bibliographicCitation	Clinical Lung Cancer. 2025, 26(1), p. 58-71
DC.title	Radiologists Versus AI-Based Software: Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma From CT Under Various Image Display Conditions
DC.creator	Sato, Junya
	Yanagawa, Masahiro
	Nishigaki, Daiki
	Hata, Akinori
	Sakao, Yukinori
	Sakakura, Noriaki
	Yatabe, Yasushi
	Shintani, Yasushi
	Kido, Shoji
	Tomiyama, Noriyuki
DC.publisher	Elsevier Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-01-01
DC.identifier	https://doi.org/10.1016/j.cllc.2024.10.015
DC.subject	Artificial intelligence
	Lung adenocarcinoma
	Lymph node metastasis
	Prognosis
	Tumor measurement
DC.description	Sato J., Yanagawa M., Nishigaki D., et al. Radiologists Versus AI-Based Software: Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma From CT Under Various Image Display Conditions. Clinical Lung Cancer 26, 58 (2025); https://doi.org/10.1016/j.cllc.2024.10.015.
DCTERMS.abstract	Purpose: To compare the variability of quantitative values from lung adenocarcinoma CT images independently assessed by 2 radiologists and AI-based software under different display conditions, and to identify predictors of pathological lymph node metastasis (LNM), disease-free survival (DFS), and overall survival (OS). Methods: Preoperative CT images of 307 patients were displayed under 4 conditions: lung-1, lung-2, mediastinum-1, and mediastinum-2. Two radiologists (R1, R2) measured total diameter (tD) and the longest solid diameter (sD) under each condition. The AI-based software automatically detected lung nodules, providing tD, sD, total volume (tV), and solid volume (sV). Results: All measurements by R1 and R2 with AI-based software were identical. Four out of the 8 measurements showed significant variation between R1 and R2. For LNM, multivariate logistic regression identified significant indicators including sD at mediastinum-2 of R1, sD at mediastinum-1 and mediastinum-2 of R2, tV, and the proportion of sV to tV (sV/tV) of AI-based software. For DFS, multivariate Cox regression identified sD at lung-1 of R1, the proportions of sD to tD at lung-2 of R1, sD at lung-2 and mediastinum-1 of R2, tV, and sV/tV of AI-based software as significant. For OS, multivariate Cox regression identified sD at lung-1 and mediastinum-2 of R1, tD at lung-2 of R2, sD at mediastinum-1 of R2, sV, and sV/tV of AI-based software as significant. Conclusion: Radiologists’ CT measurements were significant predictors of LNM and prognosis, but variability existed among radiologists and display conditions. AI-based software can provide accurate and reproducible indicators for predicting LNM and prognosis.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	100202
citation_title	Radiologists Versus AI-Based Software: Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma From CT Under Various Image Display Conditions
citation_author	Sato, Junya
	Yanagawa, Masahiro
	Nishigaki, Daiki
	Hata, Akinori
	Sakao, Yukinori
	Sakakura, Noriaki
	Yatabe, Yasushi
	Shintani, Yasushi
	Kido, Shoji
	Tomiyama, Noriyuki
citation_publisher	Elsevier Inc.
citation_language	English
citation_date	2025-01-01
citation_journal_title	Clinical Lung Cancer
citation_volume	26
citation_issue	1
citation_firstpage	58
citation_lastpage	71
citation_doi	https://doi.org/10.1016/j.cllc.2024.10.015
citation_public_url	https://hdl.handle.net/11094/100202

Radiologists Versus AI-Based Software: Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma From CT Under Various Image Display Conditions

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