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Dual-energy CT for differentiation of hypodense liver lesions in pancreatic adenocarcinoma.

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Title: Dual-energy CT for differentiation of hypodense liver lesions in pancreatic adenocarcinoma.
Authors: Jensen, Corey T.1 (AUTHOR) cjensen@mdanderson.org, Wong, Vincenzo K.1 (AUTHOR), Likhari, Gauruv S.1 (AUTHOR), Daoud, Taher E.1 (AUTHOR), Ahmad, Moiz2 (AUTHOR), Bassett, Roland3 (AUTHOR), Pasyar, Sarah3 (AUTHOR), Virarkar, Mayur K.4 (AUTHOR), Roman-Colon, Alicia M.5 (AUTHOR), Liu, Xinming2 (AUTHOR)
Source: European Radiology. Jun2025, Vol. 35 Issue 6, p3538-3546. 9p.
Subjects: Dual energy CT (Tomography), Liver tumors, Computed tomography, Pancreatic tumors, Computer-assisted image analysis (Medicine), Receiver operating characteristic curves, Contrast media
Abstract: Objective: To assess the accuracy of CT spectral HU curve assessment of hypodense liver lesions. Methods: In this retrospective HIPAA–compliant study (January 2016 through May 2023), patients with biopsy-proven pancreatic adenocarcinoma and a biopsied indeterminate liver lesion underwent a DECT abdominal CT scan. Spectral HU curves were provided for each hypodense liver lesion, and slopes were calculated. Lesion Hounsfield units, iodine concentration and virtual enhancement were recorded. The Wilcoxon rank sum test was used to compare malignant and benign lesions. Optimal cutoff points were estimated using ROC curves and Youden's Index. Results: Thirty-six patients (19 men, 17 women) with a mean age of 63 years ± 9 (standard deviation), a mean height of 170.9 cm ± 9.5, a mean weight of 69.8 kg ± 14.5, and a body mass index of 23.9 kg/m2 ± 3.5. Reference standard assessment identified 92 liver lesions (50 metastases, 24 cysts, 13 abscesses, 3 regions of inflammation, 2 hemangiomas) with a mean size of 1.1 cm ± 0.5. The mean interval between the CT and liver lesion biopsy was 24 days. A diagnosis of benign versus malignant was determined based on optimal cutoffs: spectral curve slope of 1.36, iodine concentration of 6.47 (100 µg/cm3), and enhancement of 10.25. The receiver operating curves (ROC) for diagnosis using spectral curve slope, iodine concentration, and virtual enhancement resulted in an area under the curve (AUC) of 0.948, 0.946, and 0.937, respectively. Conclusion: Spectral HU curves and iodine concentration of well-defined hypodense liver lesions are highly accurate in the diagnosis of benign versus malignant lesions. Key Points: QuestionLimited evidence exists for spectral imaging diagnosis of liver lesions—can DECT accurately differentiate between benign and metastatic hypodense liver lesions? FindingsNinety-two hypodense liver lesions evaluated using HU keV curve slope, iodine concentration, and virtual enhancement resulted in accurate benign versus metastatic differentiation. Clinical relevanceHypodense liver lesions are a challenging issue at staging, often requiring further imaging, follow-up, and/or biopsy. The additional information from multi-energy CT can be useful to differentiate between benign and malignant lesions, thereby reducing the need for costly additional evaluation. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Dual-energy CT for differentiation of hypodense liver lesions in pancreatic adenocarcinoma.
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  Data: <searchLink fieldCode="AR" term="%22Jensen%2C+Corey+T%2E%22">Jensen, Corey T.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cjensen@mdanderson.org</i><br /><searchLink fieldCode="AR" term="%22Wong%2C+Vincenzo+K%2E%22">Wong, Vincenzo K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Likhari%2C+Gauruv+S%2E%22">Likhari, Gauruv S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Daoud%2C+Taher+E%2E%22">Daoud, Taher E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ahmad%2C+Moiz%22">Ahmad, Moiz</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bassett%2C+Roland%22">Bassett, Roland</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pasyar%2C+Sarah%22">Pasyar, Sarah</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Virarkar%2C+Mayur+K%2E%22">Virarkar, Mayur K.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Roman-Colon%2C+Alicia+M%2E%22">Roman-Colon, Alicia M.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xinming%22">Liu, Xinming</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Jun2025, Vol. 35 Issue 6, p3538-3546. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Dual+energy+CT+%28Tomography%29%22">Dual energy CT (Tomography)</searchLink><br /><searchLink fieldCode="DE" term="%22Liver+tumors%22">Liver tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Pancreatic+tumors%22">Pancreatic tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Contrast+media%22">Contrast media</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Objective: To assess the accuracy of CT spectral HU curve assessment of hypodense liver lesions. Methods: In this retrospective HIPAA–compliant study (January 2016 through May 2023), patients with biopsy-proven pancreatic adenocarcinoma and a biopsied indeterminate liver lesion underwent a DECT abdominal CT scan. Spectral HU curves were provided for each hypodense liver lesion, and slopes were calculated. Lesion Hounsfield units, iodine concentration and virtual enhancement were recorded. The Wilcoxon rank sum test was used to compare malignant and benign lesions. Optimal cutoff points were estimated using ROC curves and Youden's Index. Results: Thirty-six patients (19 men, 17 women) with a mean age of 63 years ± 9 (standard deviation), a mean height of 170.9 cm ± 9.5, a mean weight of 69.8 kg ± 14.5, and a body mass index of 23.9 kg/m2 ± 3.5. Reference standard assessment identified 92 liver lesions (50 metastases, 24 cysts, 13 abscesses, 3 regions of inflammation, 2 hemangiomas) with a mean size of 1.1 cm ± 0.5. The mean interval between the CT and liver lesion biopsy was 24 days. A diagnosis of benign versus malignant was determined based on optimal cutoffs: spectral curve slope of 1.36, iodine concentration of 6.47 (100 µg/cm3), and enhancement of 10.25. The receiver operating curves (ROC) for diagnosis using spectral curve slope, iodine concentration, and virtual enhancement resulted in an area under the curve (AUC) of 0.948, 0.946, and 0.937, respectively. Conclusion: Spectral HU curves and iodine concentration of well-defined hypodense liver lesions are highly accurate in the diagnosis of benign versus malignant lesions. Key Points: QuestionLimited evidence exists for spectral imaging diagnosis of liver lesions—can DECT accurately differentiate between benign and metastatic hypodense liver lesions? FindingsNinety-two hypodense liver lesions evaluated using HU keV curve slope, iodine concentration, and virtual enhancement resulted in accurate benign versus metastatic differentiation. Clinical relevanceHypodense liver lesions are a challenging issue at staging, often requiring further imaging, follow-up, and/or biopsy. The additional information from multi-energy CT can be useful to differentiate between benign and malignant lesions, thereby reducing the need for costly additional evaluation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00330-024-11291-5
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        Text: English
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      – SubjectFull: Liver tumors
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      – SubjectFull: Computed tomography
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              Text: Jun2025
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