A new medical imaging postprocessing and interpretation concept to investigate the clinical relevance of incidentalomas: can we keep Pandora's box closed?. (June 2023)
- Record Type:
- Journal Article
- Title:
- A new medical imaging postprocessing and interpretation concept to investigate the clinical relevance of incidentalomas: can we keep Pandora's box closed?. (June 2023)
- Main Title:
- A new medical imaging postprocessing and interpretation concept to investigate the clinical relevance of incidentalomas: can we keep Pandora's box closed?
- Authors:
- Kwee, Thomas C
Roest, Christian
Kasalak, Ömer
Pennings, Jan P
de Jong, Igle Jan
Yakar, Derya - Abstract:
- Background: Incidental imaging findings (incidentalomas) are common, but there is currently no effective means to investigate their clinical relevance. Purpose: To introduce a new concept to postprocess a medical imaging examination in a way that incidentalomas are concealed while its diagnostic potential is maintained to answer the referring physician's clinical questions. Material and Methods: A deep learning algorithm was developed to automatically eliminate liver, gallbladder, pancreas, spleen, adrenal glands, lungs, and bone from unenhanced computed tomography (CT). This deep learning algorithm was applied to a separately held set of unenhanced CT scans of 27 patients who underwent CT to evaluate for urolithiasis, and who had a total of 32 incidentalomas in one of the aforementioned organs. Results: Median visual scores for organ elimination on modified CT were 100% for the liver, gallbladder, spleen, and right adrenal gland, 90%–99% for the pancreas, lungs, and bones, and 80%–89% for the left adrenal gland. In 26 out of 27 cases (96.3%), the renal calyces and pelves, ureters, and urinary bladder were completely visible on modified CT. In one case, a short (<1 cm) trajectory of the left ureter was not clearly visible due to adjacent atherosclerosis that was mistaken for bone by the algorithm. Of 32 incidentalomas, 28 (87.5%) were completely concealed on modified CT. Conclusion: This preliminary technical report demonstrated the feasibility of a new approach toBackground: Incidental imaging findings (incidentalomas) are common, but there is currently no effective means to investigate their clinical relevance. Purpose: To introduce a new concept to postprocess a medical imaging examination in a way that incidentalomas are concealed while its diagnostic potential is maintained to answer the referring physician's clinical questions. Material and Methods: A deep learning algorithm was developed to automatically eliminate liver, gallbladder, pancreas, spleen, adrenal glands, lungs, and bone from unenhanced computed tomography (CT). This deep learning algorithm was applied to a separately held set of unenhanced CT scans of 27 patients who underwent CT to evaluate for urolithiasis, and who had a total of 32 incidentalomas in one of the aforementioned organs. Results: Median visual scores for organ elimination on modified CT were 100% for the liver, gallbladder, spleen, and right adrenal gland, 90%–99% for the pancreas, lungs, and bones, and 80%–89% for the left adrenal gland. In 26 out of 27 cases (96.3%), the renal calyces and pelves, ureters, and urinary bladder were completely visible on modified CT. In one case, a short (<1 cm) trajectory of the left ureter was not clearly visible due to adjacent atherosclerosis that was mistaken for bone by the algorithm. Of 32 incidentalomas, 28 (87.5%) were completely concealed on modified CT. Conclusion: This preliminary technical report demonstrated the feasibility of a new approach to postprocess and evaluate medical imaging examinations that can be used by future prospective research studies with long-term follow-up to investigate the clinical relevance of incidentalomas. … (more)
- Is Part Of:
- Acta radiologica. Volume 64:Number 6(2023)
- Journal:
- Acta radiologica
- Issue:
- Volume 64:Number 6(2023)
- Issue Display:
- Volume 64, Issue 6 (2023)
- Year:
- 2023
- Volume:
- 64
- Issue:
- 6
- Issue Sort Value:
- 2023-0064-0006-0000
- Page Start:
- 2170
- Page End:
- 2179
- Publication Date:
- 2023-06
- Subjects:
- Computed tomography -- diagnostic imaging -- incidental findings -- medical overuse
Radiology, Medical -- Periodicals
Radiography, Medical -- Periodicals
Radiotherapy -- Periodicals
616.0757 - Journal URLs:
- http://acr.sagepub.com ↗
http://ar.rsmjournals.com ↗
http://www.uk.sagepub.com/home.nav ↗
http://informahealthcare.com/loi/ard ↗
http://www.tandf.co.uk/journals/titles/02841851.asp ↗ - DOI:
- 10.1177/02841851231158769 ↗
- Languages:
- English
- ISSNs:
- 0284-1851
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0662.000000
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