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The purpose of this study is to investigate the image quality and clinical feasibility of double low-dose liver computed tomography using a deep-learning-based iodine contrast boosting algorithm in participants at high risk for hepatocellular carcinoma.
The purpose of this study is to investigate the image quality (CNR, subjective inage quality, vessel and lesion conspicuity) and clinical feasibility (rate of lesion detection) of double low-dose liver computed tomography using a deep-learning-based iodine contrast boosting algorithm in participants at high risk for hepatocellular carcinoma.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Conventional liver CT | Active Comparator | Underwent conventional liver CT on HCC high-risk patient |
|
| Double low dose CT | Experimental | Underwent double low dose liver CT with 30% lower radiation dose and 20% contrast media on HCC high-risk patient |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Low radiation dose CT | Radiation | Underwent liver CT with 30% lower radiation dose |
| |
| Measure | Description | Time Frame |
|---|---|---|
| Detection rate of solid focal lesion in liver | diagnostic performance | 12 months after last patient's image work up |
| Quantitative image quality analysis | Contrast-to-Noise | Immediate after study enrollement |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Jeong Min Lee, MD | Seoul National University Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Seoul National University Hospital | Seoul | South Korea |
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Participants, investigators, and outcome assessors were all masked to the allocation
| Conventional CT (radiation) |
| Radiation |
Underwent liver CT without change of radiation. |
|
| Low contrast dose CT | Drug | Underwent liver CT 20% lower dose contrast media. |
|
|
| Conventional CT (contrast agent) | Drug | Underwent liver CT without change of contrast media doses. |
|
|
| Deep-learning based contrast boosting algorithms | Other | Applied deep-learning based contrast boosting algorithms on acquired CT images |
|
| ID | Term |
|---|---|
| D006528 | Carcinoma, Hepatocellular |
| ID | Term |
|---|---|
| D000230 | Adenocarcinoma |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D008113 | Liver Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D004066 | Digestive System Diseases |
| D008107 | Liver Diseases |
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| ID | Term |
|---|---|
| D003226 | Congresses as Topic |
| D011827 | Radiation |
| D003287 | Contrast Media |
| ID | Term |
|---|---|
| D009938 | Organizations |
| D004472 | Health Care Economics and Organizations |
| D055585 | Physical Phenomena |
| D064907 | Diagnostic Uses of Chemicals |
| D020228 | Pharmacologic Actions |
| D020164 | Chemical Actions and Uses |
| D020313 | Specialty Uses of Chemicals |
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