Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT

Ota, Takashi; Onishi, Hiromitsu; Fukui, Hideyuki; Tsuboyama, Takahiro; Nakamoto, Atsushi; Honda, Toru; Matsumoto, Shohei; Tatsumi, Mitsuaki; Tomiyama, Noriyuki

doi:https://doi.org/10.1007/s00330-024-11024-8

Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT

Ota, Takashi; Onishi, Hiromitsu; Fukui, Hideyuki et al.

European Radiology, 2024, 35(3), 1361-1377

アクセス数:1,564件（2025-08-17 03:02 集計）

固定URL: https://hdl.handle.net/11094/98272

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タイトル	Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT
著者	NRID 1000020868088 1000020868088 researchmap permalink takashi_0123 takashi_0123 Scopus Author ID 57194721064 57194721064 著者名 Ota, Takashi
	NRID 1000020452435 1000020452435 researchmap permalink edv edv Scopus Author ID 9240645100 9240645100 著者名 Onishi, Hiromitsu
	NRID 1000000721101 1000000721101 researchmap permalink h-fukui h-fukui Scopus Author ID 57192195026 57192195026 著者名 Fukui, Hideyuki
	NRID 1000000423187 1000000423187 researchmap permalink ttsuboyama ttsuboyama Scopus Author ID 56370777700 56370777700 著者名 Tsuboyama, Takahiro
	NRID 1000020625199 1000020625199 researchmap permalink nakamo10 nakamo10 Scopus Author ID 36778686900 36778686900 著者名 Nakamoto, Atsushi
	著者名 Honda, Toru
	著者名 Matsumoto, Shohei
	NRID 1000060397700 1000060397700 ORCID 0000-0003-1778-0421 0000-0003-1778-0421 researchmap permalink mtatsumi mtatsumi Scopus Author ID 7102266657 7102266657 著者名 Tatsumi, Mitsuaki
	NRID 1000050294070 1000050294070 researchmap permalink 3430 3430 Scopus Author ID 7006024942 7006024942 著者名 Tomiyama, Noriyuki
キーワード等	Differential diagnosis
	Liver neoplasms
	Logistic models
	Logistic regression
	X-ray computed tomography
内容	The version of record of this article, first published in European Radiology, is available online at Publisher’s website: https://doi.org/10.1007/s00330-024-11024-8.
抄録	Objectives: To create prediction models (PMs) for distinguishing between benign and malignant liver lesions using quantitative data from dual-energy CT (DECT) without contrast agents. Materials and methods: This retrospective study included patients with liver lesions who underwent DECT, including non-contrast-enhanced scans. Benign lesions included hepatic hemangioma, whereas malignant lesions included hepatocellular carcinoma, metastatic liver cancer, and intrahepatic cholangiocellular carcinoma. Patients were divided into derivation and validation groups. In the derivation group, two radiologists calculated ten multiparametric data using univariate and multivariate logistic regression to generate PMs. In the validation group, two additional radiologists measured the parameters to assess the diagnostic performance of PMs. Results: The study included 121 consecutive patients (mean age 67.4 ± 13.8 years, 80 males), with 97 in the derivation group (25 benign and 72 malignant) and 24 in the validation group (7 benign and 17 malignant). Oversampling increased the benign lesion sample to 75, equalizing the malignant group for building PMs. All parameters were statistically significant in univariate analysis (all p < 0.05), leading to the creation of five PMs in multivariate analysis. The area under the curve for the five PMs of two observers was as follows: PM1 (slope K, blood) = 0.76, 0.74; PM2 (slope K, fat) = 0.55, 0.51; PM3 (effective-Z difference, blood) = 0.75, 0.72; PM4 (slope K, blood, fat) = 0.82, 0.78; and PM5 (slope K, effective-Z difference, blood) = 0.90, 0.87. PM5 yielded the best diagnostic performance. Conclusion: Multiparametric non-contrast-enhanced DECT is a highly effective method for distinguishing between liver lesions. Clinical relevance statement: The utilization of non-contrast-enhanced DECT is extremely useful for distinguishing between benign and malignant liver lesions. This approach enables physicians to plan better treatment strategies, alleviating concerns associated with contrast allergy, contrast-induced nephropathy, radiation exposure, and excessive medical expenses.
出版者	Springer Science and Business Media Deutschland GmbH
収録物名	European Radiology
巻(号)	35(3)
ページ	1361-1377
刊行年月	2024-08-26
言語	英語
ハンドルURL	https://hdl.handle.net/11094/98272
PISSN	09387994
eISSN	14321084
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1007/s00330-024-11024-8
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	European Radiology. 2024, 35(3), p. 1361-1377
DC.title	Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT
DC.creator	Ota, Takashi
	Onishi, Hiromitsu
	Fukui, Hideyuki
	Tsuboyama, Takahiro
	Nakamoto, Atsushi
	Honda, Toru
	Matsumoto, Shohei
	Tatsumi, Mitsuaki
	Tomiyama, Noriyuki
DC.publisher	Springer Science and Business Media Deutschland GmbH
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-08-26
DC.identifier	https://doi.org/10.1007/s00330-024-11024-8
DC.subject	Differential diagnosis
	Liver neoplasms
	Logistic models
	Logistic regression
	X-ray computed tomography
DC.description	The version of record of this article, first published in European Radiology, is available online at Publisher’s website: https://doi.org/10.1007/s00330-024-11024-8.
DCTERMS.abstract	Objectives: To create prediction models (PMs) for distinguishing between benign and malignant liver lesions using quantitative data from dual-energy CT (DECT) without contrast agents. Materials and methods: This retrospective study included patients with liver lesions who underwent DECT, including non-contrast-enhanced scans. Benign lesions included hepatic hemangioma, whereas malignant lesions included hepatocellular carcinoma, metastatic liver cancer, and intrahepatic cholangiocellular carcinoma. Patients were divided into derivation and validation groups. In the derivation group, two radiologists calculated ten multiparametric data using univariate and multivariate logistic regression to generate PMs. In the validation group, two additional radiologists measured the parameters to assess the diagnostic performance of PMs. Results: The study included 121 consecutive patients (mean age 67.4 ± 13.8 years, 80 males), with 97 in the derivation group (25 benign and 72 malignant) and 24 in the validation group (7 benign and 17 malignant). Oversampling increased the benign lesion sample to 75, equalizing the malignant group for building PMs. All parameters were statistically significant in univariate analysis (all p < 0.05), leading to the creation of five PMs in multivariate analysis. The area under the curve for the five PMs of two observers was as follows: PM1 (slope K, blood) = 0.76, 0.74; PM2 (slope K, fat) = 0.55, 0.51; PM3 (effective-Z difference, blood) = 0.75, 0.72; PM4 (slope K, blood, fat) = 0.82, 0.78; and PM5 (slope K, effective-Z difference, blood) = 0.90, 0.87. PM5 yielded the best diagnostic performance. Conclusion: Multiparametric non-contrast-enhanced DECT is a highly effective method for distinguishing between liver lesions. Clinical relevance statement: The utilization of non-contrast-enhanced DECT is extremely useful for distinguishing between benign and malignant liver lesions. This approach enables physicians to plan better treatment strategies, alleviating concerns associated with contrast allergy, contrast-induced nephropathy, radiation exposure, and excessive medical expenses.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	98272
citation_title	Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT
citation_author	Ota, Takashi
	Onishi, Hiromitsu
	Fukui, Hideyuki
	Tsuboyama, Takahiro
	Nakamoto, Atsushi
	Honda, Toru
	Matsumoto, Shohei
	Tatsumi, Mitsuaki
	Tomiyama, Noriyuki
citation_publisher	Springer Science and Business Media Deutschland GmbH
citation_language	英語
citation_date	2024-08-26
citation_journal_title	European Radiology
citation_volume	35
citation_issue	3
citation_firstpage	1361
citation_lastpage	1377
citation_doi	https://doi.org/10.1007/s00330-024-11024-8
citation_public_url	https://hdl.handle.net/11094/98272

Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT

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