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Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT

High-attenuation pulmonary abnormalities are commonly seen on CT. These findings are increasingly encountered with the growing number of CT examinations and the wide availability of thin-slice images. The abnormalities include benign lesions, such as infectious granulomatous diseases and metabolic d...

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Autores principales: Fukuda, Taiki, Egashira, Ryoko, Ueno, Midori, Hashisako, Mikiko, Sumikawa, Hiromitsu, Tominaga, Junya, Yamada, Daisuke, Fukuoka, Junya, Misumi, Shigeki, Ojiri, Hiroya, Hatabu, Hiroto, Johkoh, Takeshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587054/
https://www.ncbi.nlm.nih.gov/pubmed/37857741
http://dx.doi.org/10.1186/s13244-023-01501-x
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author Fukuda, Taiki
Egashira, Ryoko
Ueno, Midori
Hashisako, Mikiko
Sumikawa, Hiromitsu
Tominaga, Junya
Yamada, Daisuke
Fukuoka, Junya
Misumi, Shigeki
Ojiri, Hiroya
Hatabu, Hiroto
Johkoh, Takeshi
author_facet Fukuda, Taiki
Egashira, Ryoko
Ueno, Midori
Hashisako, Mikiko
Sumikawa, Hiromitsu
Tominaga, Junya
Yamada, Daisuke
Fukuoka, Junya
Misumi, Shigeki
Ojiri, Hiroya
Hatabu, Hiroto
Johkoh, Takeshi
author_sort Fukuda, Taiki
collection PubMed
description High-attenuation pulmonary abnormalities are commonly seen on CT. These findings are increasingly encountered with the growing number of CT examinations and the wide availability of thin-slice images. The abnormalities include benign lesions, such as infectious granulomatous diseases and metabolic diseases, and malignant tumors, such as lung cancers and metastatic tumors. Due to the wide spectrum of diseases, the proper diagnosis of high-attenuation abnormalities can be challenging. The assessment of these abnormal findings requires scrutiny, and the treatment is imperative. Our proposed stepwise diagnostic algorithm consists of five steps. Step 1: Establish the presence or absence of metallic artifacts. Step 2: Identify associated nodular or mass-like soft tissue components. Step 3: Establish the presence of solitary or multiple lesions if identified in Step 2. Step 4: Ascertain the predominant distribution in the upper or lower lungs if not identified in Step 2. Step 5: Identify the morphological pattern, such as linear, consolidation, nodular, or micronodular if not identified in Step 4. These five steps to diagnosing high-attenuation abnormalities subdivide the lesions into nine categories. This stepwise radiologic diagnostic approach could help to narrow the differential diagnosis for various pulmonary high-attenuation abnormalities and to achieve a precise diagnosis. Critical relevance statement Our proposed stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities may help to recognize a variety of those high-attenuation findings, to determine whether the associated diseases require further investigation, and to guide appropriate patient management. Key points • To provide a stepwise diagnostic approach to high-attenuation pulmonary abnormalities. • To familiarize radiologists with the varying cause of high-attenuation pulmonary abnormalities. • To recognize which high-attenuation abnormalities require scrutiny and prompt treatment. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01501-x.
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spelling pubmed-105870542023-10-21 Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT Fukuda, Taiki Egashira, Ryoko Ueno, Midori Hashisako, Mikiko Sumikawa, Hiromitsu Tominaga, Junya Yamada, Daisuke Fukuoka, Junya Misumi, Shigeki Ojiri, Hiroya Hatabu, Hiroto Johkoh, Takeshi Insights Imaging Educational Review High-attenuation pulmonary abnormalities are commonly seen on CT. These findings are increasingly encountered with the growing number of CT examinations and the wide availability of thin-slice images. The abnormalities include benign lesions, such as infectious granulomatous diseases and metabolic diseases, and malignant tumors, such as lung cancers and metastatic tumors. Due to the wide spectrum of diseases, the proper diagnosis of high-attenuation abnormalities can be challenging. The assessment of these abnormal findings requires scrutiny, and the treatment is imperative. Our proposed stepwise diagnostic algorithm consists of five steps. Step 1: Establish the presence or absence of metallic artifacts. Step 2: Identify associated nodular or mass-like soft tissue components. Step 3: Establish the presence of solitary or multiple lesions if identified in Step 2. Step 4: Ascertain the predominant distribution in the upper or lower lungs if not identified in Step 2. Step 5: Identify the morphological pattern, such as linear, consolidation, nodular, or micronodular if not identified in Step 4. These five steps to diagnosing high-attenuation abnormalities subdivide the lesions into nine categories. This stepwise radiologic diagnostic approach could help to narrow the differential diagnosis for various pulmonary high-attenuation abnormalities and to achieve a precise diagnosis. Critical relevance statement Our proposed stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities may help to recognize a variety of those high-attenuation findings, to determine whether the associated diseases require further investigation, and to guide appropriate patient management. Key points • To provide a stepwise diagnostic approach to high-attenuation pulmonary abnormalities. • To familiarize radiologists with the varying cause of high-attenuation pulmonary abnormalities. • To recognize which high-attenuation abnormalities require scrutiny and prompt treatment. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01501-x. Springer Vienna 2023-10-20 /pmc/articles/PMC10587054/ /pubmed/37857741 http://dx.doi.org/10.1186/s13244-023-01501-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Educational Review
Fukuda, Taiki
Egashira, Ryoko
Ueno, Midori
Hashisako, Mikiko
Sumikawa, Hiromitsu
Tominaga, Junya
Yamada, Daisuke
Fukuoka, Junya
Misumi, Shigeki
Ojiri, Hiroya
Hatabu, Hiroto
Johkoh, Takeshi
Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT
title Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT
title_full Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT
title_fullStr Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT
title_full_unstemmed Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT
title_short Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT
title_sort stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on ct
topic Educational Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587054/
https://www.ncbi.nlm.nih.gov/pubmed/37857741
http://dx.doi.org/10.1186/s13244-023-01501-x
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