Namkyu Kang1, Jung Wha Chung1,2, Eun Sun Jang1,3, Sook-Hyang Jeong1,3, Jin-Wook Kim4,5. 1. Department of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea. 2. Department of Internal Medicine, Wonkwang University Sanbon Hospital, Gunpo-si, Republic of Korea. 3. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. 4. Department of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea. kimjw@snubh.org. 5. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. kimjw@snubh.org.
Abstract
AIM: In this retrospective cohort study, we evaluated the significance of liver volume in the prediction of hepatocellular carcinoma (HCC) in 277 chronic hepatitis C (CHC) patients who received dynamic computed tomography (CT) during surveillance. METHODS: Liver volumes were measured on portal venous phase of CT images by using ImageJ software. Liver volume index, a ratio of the standard liver volume expected by weight and height to the measured liver volume, was calculated to adjust for normal variations. The cohort was randomly divided to derivation (n = 100) and validation sets (n = 177) for the generation of a liver volume-based Cox prediction model and validation of a liver volume-based nomogram, respectively. RESULTS: The liver volume index was independent of weight or height, and it predicted further development of HCC (hazard ratio [HR] 16.30, 95% CI 6.70-39.62; p < 0.001). Liver cirrhosis, gamma-glutamyl transferase, and liver volume index were independent predictors of HCC, and nomogram-based prediction score from these three parameters identified high-risk patients at the cutoff of 110 in both derivation (p < 0.001) and validation cohort (p < 0.001). CONCLUSION: Liver volume-based prediction model stratifies the risk of developing HCC in CHC patients whose initial dynamic CT study gave negative results.
AIM: In this retrospective cohort study, we evaluated the significance of liver volume in the prediction of hepatocellular carcinoma (HCC) in 277 chronic hepatitis C (CHC) patients who received dynamic computed tomography (CT) during surveillance. METHODS: Liver volumes were measured on portal venous phase of CT images by using ImageJ software. Liver volume index, a ratio of the standard liver volume expected by weight and height to the measured liver volume, was calculated to adjust for normal variations. The cohort was randomly divided to derivation (n = 100) and validation sets (n = 177) for the generation of a liver volume-based Cox prediction model and validation of a liver volume-based nomogram, respectively. RESULTS: The liver volume index was independent of weight or height, and it predicted further development of HCC (hazard ratio [HR] 16.30, 95% CI 6.70-39.62; p < 0.001). Liver cirrhosis, gamma-glutamyl transferase, and liver volume index were independent predictors of HCC, and nomogram-based prediction score from these three parameters identified high-risk patients at the cutoff of 110 in both derivation (p < 0.001) and validation cohort (p < 0.001). CONCLUSION: Liver volume-based prediction model stratifies the risk of developing HCC in CHC patients whose initial dynamic CT study gave negative results.
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