Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT
Abstract 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 me...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-10-01
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| Series: | Insights into Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13244-023-01501-x |
| _version_ | 1797452382392549376 |
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| author | Taiki Fukuda Ryoko Egashira Midori Ueno Mikiko Hashisako Hiromitsu Sumikawa Junya Tominaga Daisuke Yamada Junya Fukuoka Shigeki Misumi Hiroya Ojiri Hiroto Hatabu Takeshi Johkoh |
| author_facet | Taiki Fukuda Ryoko Egashira Midori Ueno Mikiko Hashisako Hiromitsu Sumikawa Junya Tominaga Daisuke Yamada Junya Fukuoka Shigeki Misumi Hiroya Ojiri Hiroto Hatabu Takeshi Johkoh |
| author_sort | Taiki Fukuda |
| collection | DOAJ |
| description | Abstract 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 |
| first_indexed | 2024-03-09T15:07:31Z |
| format | Article |
| id | doaj.art-0e801731034b45e8852a9610bb30264f |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-03-09T15:07:31Z |
| publishDate | 2023-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-0e801731034b45e8852a9610bb30264f2023-11-26T13:32:59ZengSpringerOpenInsights into Imaging1869-41012023-10-0114111910.1186/s13244-023-01501-xStepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CTTaiki Fukuda0Ryoko Egashira1Midori Ueno2Mikiko Hashisako3Hiromitsu Sumikawa4Junya Tominaga5Daisuke Yamada6Junya Fukuoka7Shigeki Misumi8Hiroya Ojiri9Hiroto Hatabu10Takeshi Johkoh11Department of Radiology, The Jikei University School of MedicineDepartment of Radiology, Faculty of Medicine, Saga UniversityDepartment of Radiology, University of Occupational and Environmental HealthDepartment of Pathology, Kyushu UniversityDepartment of Radiology, National Hospital Organization Kinki-Chuo Chest Medical CenterDepartment of Diagnostic Radiology, Tohoku University Graduate School of MedicineDepartment of Radiology, St. Luke’s International HospitalDepartment of Pathology, Nagasaki University Graduate School of Biomedical SciencesDepartment of Radiology, The Jikei University School of MedicineDepartment of Radiology, The Jikei University School of MedicineDepartment of Radiology, Brigham and Women’s Hospital, Harvard Medical SchoolDepartment of Radiology, Kansai Rosai HospitalAbstract 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 Abstracthttps://doi.org/10.1186/s13244-023-01501-xLung diseasesTomography (X-ray computed)AlgorithmsHigh attenuationCalcification |
| spellingShingle | Taiki Fukuda Ryoko Egashira Midori Ueno Mikiko Hashisako Hiromitsu Sumikawa Junya Tominaga Daisuke Yamada Junya Fukuoka Shigeki Misumi Hiroya Ojiri Hiroto Hatabu Takeshi Johkoh Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT Insights into Imaging Lung diseases Tomography (X-ray computed) Algorithms High attenuation Calcification |
| 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 | Lung diseases Tomography (X-ray computed) Algorithms High attenuation Calcification |
| url | https://doi.org/10.1186/s13244-023-01501-x |
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