Junghoan Park1, Jung Hoon Kim2,3,4, Ji-Eun Kim1, Sang Joon Park1, Nam-Joon Yi5, Joon Koo Han1,6,7. 1. Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. 2. Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. jhkim2008@gmail.com. 3. Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. jhkim2008@gmail.com. 4. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea. jhkim2008@gmail.com. 5. Department of Surgery, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. 6. Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. 7. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
Abstract
PURPOSE: The aim of the study is to predict the rate of liver regeneration in recipients after living-donor liver transplantation using preoperative CT texture and shape analysis of the future graft. METHODS: 102 donor-recipient pairs who underwent living-donor liver transplantation using right lobe grafts were retrospectively included. We semi-automatically segmented the future graft from preoperative CT. The volume of the future graft (LVpre) was measured, and texture and shape analyses were performed. The graft liver was segmented from postoperative follow-up CT and the volume of the graft (LVpost) was measured. The regeneration index was defined by the following equation: [(LVpost-LVpre)/LVpre] × 100(%). We performed a stepwise, multivariate linear regression analysis to investigate the association between clinical, texture and shape parameters and the RI and to make the best-fit predictive model. RESULTS: The mean regeneration index was 47.5 ± 38.6%. In univariate analysis, the volume of the future graft, energy, effective diameter, surface area, sphericity, roundnessm, compactness1, and grey-level co-occurrence matrix contrast as well as several clinical parameters were significantly associated with the regeneration index (p < 0.05). The best-fit predictive model for the regeneration index made by multivariate analysis was as follows: Regeneration index (%) = 127.020-0.367 × effective diameter - 1.827 × roundnessm + 47.371 × recipient body surface area (m2) + 12.041 × log(recipient white blood cell count) (× 103/μL)+ 18.034 (if the donor was female). CONCLUSION: The effective diameter and roundnessm of the future graft were associated with liver regeneration. Preoperative CT texture analysis of future grafts can be useful for predicting liver regeneration in recipients after living-donor liver transplantation.
PURPOSE: The aim of the study is to predict the rate of liver regeneration in recipients after living-donor liver transplantation using preoperative CT texture and shape analysis of the future graft. METHODS: 102 donor-recipient pairs who underwent living-donor liver transplantation using right lobe grafts were retrospectively included. We semi-automatically segmented the future graft from preoperative CT. The volume of the future graft (LVpre) was measured, and texture and shape analyses were performed. The graft liver was segmented from postoperative follow-up CT and the volume of the graft (LVpost) was measured. The regeneration index was defined by the following equation: [(LVpost-LVpre)/LVpre] × 100(%). We performed a stepwise, multivariate linear regression analysis to investigate the association between clinical, texture and shape parameters and the RI and to make the best-fit predictive model. RESULTS: The mean regeneration index was 47.5 ± 38.6%. In univariate analysis, the volume of the future graft, energy, effective diameter, surface area, sphericity, roundnessm, compactness1, and grey-level co-occurrence matrix contrast as well as several clinical parameters were significantly associated with the regeneration index (p < 0.05). The best-fit predictive model for the regeneration index made by multivariate analysis was as follows: Regeneration index (%) = 127.020-0.367 × effective diameter - 1.827 × roundnessm + 47.371 × recipient body surface area (m2) + 12.041 × log(recipient white blood cell count) (× 103/μL)+ 18.034 (if the donor was female). CONCLUSION: The effective diameter and roundnessm of the future graft were associated with liver regeneration. Preoperative CT texture analysis of future grafts can be useful for predicting liver regeneration in recipients after living-donor liver transplantation.