Venous complications after pediatric liver transplantation seriously affect the survival rate of patients and grafts. At present, the diagnostic indicators have not been unified. Venous complications may cause portal hypertension, which may lead to splenomegaly and splenic vein dilatation. Therefore, the changes in spleen may be closely related to the venous complications. The purpose of this study was to explore the relationship between ultrasonic splenic parameters and venous complications and to study whether these splenic parameters can be used for the diagnosis of venous complications.

We retrospectively included pediatric patients who underwent liver transplantation and collected ultrasonic spleen parameters before, and then 1-3 days, 1-3 weeks, 1-3 months, and 4-12 months after liver transplantation. We observed whether there were portal vein or hepatic vein complications within 1 year after liver transplantation.

Among 109 pediatric patients after liver transplantation included in our study, 11 of them suffered from portal vein complications and nine hepatic vein complications. Spleen transverse diameter, spleen longitudinal diameter, spleen portal vein diameter, spleen index, spleen transverse diameter ratio, spleen longitudinal diameter ratio, and spleen index ratio were independent risk factors of venous complications. The accuracy of spleen transverse diameter (AUROC: 0.73), spleen index (AUROC: 0.70), spleen transverse diameter ratio (AUROC: 0.71), and spleen index ratio (AUROC: 0.72) in predicting venous complications were higher than other ones.

Ultrasonic examination is a common follow-up method for pediatric patients after liver transplantation and the application of ultrasonic spleen parameters may be helpful to monitor venous complications.

Living donor liver transplantation (LDLT) is a definitive procedure for splenomegaly caused by liver cirrhosis and portal hypertension, but splenomegaly persists in some patients. The aim of this study was to clarify the long-term changes in the spleen volume after LDLT.

The 13 pediatric patients who survived for >8 years after LDLT were retrospectively analyzed. We calculated the spleen volume/standard spleen volume (SV/SSV) ratio by automated computed tomography (CT) volumetry. We assessed the spleen volumes before LDLT, at roughly postoperative week (POW) 4, at postoperative year (POY) 1, at POY 5, and at POY 10.

With regard to SV as evaluated by CT volumetry, there were no consistent trends, with median values as follows: before LDLT, 282.5 (71-641) cm³; POW 4, 252 (109-798) cm³; POY 1, 222.5 (97-948) cm³; POY 5, 263.5 (123-564) cm³; and POY 10, 377 (201-1080) cm³. In contrast, the SV/SSV ratio decreased chronologically as follows: before LDLT, 5.0 (0.7-6.0); POW 4, 3.7 (2.3-4.3); POY 1, 2.2 (1.7-6.3); POY 5, 1.7 (1.1-5.4); and POY 10, 1.4 (1.1-6.9). In the remote phase after LDLT, many cases showed a trend toward an improved SV/SSV ratio, but splenomegaly was prolonged without improvement in 3 cases (23.1%) with portal vein complications and advanced fibrosis. Furthermore, all 3 cases showed a decreased platelet count due to hypersplenism.

Splenomegaly requires a long time to demonstrate an improvement. In cases without an improvement of splenomegaly, we should suspect abnormalities in the graft liver and portal hemodynamics.

To develop the first fully automated 3D spleen segmentation framework derived from T1-weighted magnetic resonance (MR) imaging data and to verify its performance for spleen delineation and volumetry. This approach considers the issue of low contrast between spleen and adjacent tissue in non-contrast-enhanced MR images. Native T1-weighted MR volume data was performed on a 1.5 T MR system in an epidemiological study. We analyzed random subsamples of MR examinations without pathologies to develop and verify the spleen segmentation framework. The framework is modularized to include different kinds of prior knowledge into the segmentation pipeline. Classification by support vector machines differentiates between five different shape types in computed foreground probability maps and recognizes characteristic spleen regions in axial slices of MR volume data. A spleen-shape space generated by training produces subject-specific prior shape knowledge that is then incorporated into a final 3D level set segmentation method. Individually adapted shape-driven forces as well as image-driven forces resulting from refined foreground probability maps steer the level set successfully to the segment the spleen. The framework achieves promising segmentation results with mean Dice coefficients of nearly 0.91 and low volumetric mean errors of 6.3%. The presented spleen segmentation approach can delineate spleen tissue in native MR volume data. Several kinds of prior shape knowledge including subject-specific 3D prior shape knowledge can be used to guide segmentation processes achieving promising results.