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Anatomical variations of the right hepatic vein, especially large variant right hepatic veins (≥5 mm), have important clinical implications in liver transplantation and resection. This study aimed to evaluate anatomical variations of the right hepatic vein using quantitative three‐dimensional visualization analysis. Computed tomography images of 650 patients were retrospectively analyzed, and three‐dimensional visualization was applied using the derived data to analyze large variant right hepatic veins. The proportion of the large variant right hepatic vein was 16.92% (110/650). According to the location and number of the variant right hepatic veins, the configuration of the right hepatic venous system was divided into seven subtypes. The length of the retrohepatic inferior vena cava had a positive correlation with the diameter of the right hepatic vein (rs = 0.266, p = 0.001) and the variant right hepatic veins (rs = 0.211, p = 0.027). The diameter of the right hepatic vein was positively correlated with that of the middle hepatic vein (rs = 0.361, p < 0.001), while it was inversely correlated with that of the variant right hepatic veins (rs = −0.267, p = 0.005). The right hepatic vein diameter was positively correlated with the drainage volume (rs = 0.489, p < 0.001), while the correlation with the variant right hepatic veins drainage volume was negative (rs = −0.460, p < 0.001). The number of the variant right hepatic veins and their relative diameters were positively correlated (p < 0.001). The volume and percentage of the drainage area of the right hepatic vein decreased significantly as the number of the variant right hepatic vein increased (p < 0.001). The findings of this study concerning the variations of the hepatic venous system may be useful for the surgical planning of liver resection or transplantation. [ABSTRACT FROM AUTHOR]

In pancreatic cancer, the activation of quiescent pancreatic stellate cells (PSCs) is a critical event that leads to a characteristic dense desmoplastic stroma. The fibrotic network resulting from the bidirectional cross talk between pancreatic cancer cells (PCCs) and PSCs creates a complex tumor microenvironment that considerably hinders drug delivery and penetration. To improve pancreatic cancer treatment, a promising parallelization strategy with cross‐action property is urgently required that can simultaneously act on PCCs and PSCs to normalize activated PSCs (aPSCs), that is reverse them back to quiescent phenotype. Herein, a novel parallelization delivery system (CoA‐A&B‐γPGA) with dual‐pathway PSC quiescence restoration functions is designed. Metformin downregulates the secretion of transforming growth factor‐beta in PCCs, and all‐trans‐retinoic acid re‐educates aPSCs. CoA‐A&B‐γPGA thus induces aPSC quiescence and homeostatic restoration of desmoplastic stroma in vitro and in vivo. Importantly, CoA‐A&B‐γPGA exhibits deep penetration, improved accumulation and long‐term retention, and enhanced combination chemotherapy effects in multicellular spheroid and xenograft models. Furthermore, this dual‐targeting and co‐delivery system shows high specificity and sensitivity for detecting pancreatic cancer in patient samples. The innovative but simple peptide amphiphile co‐assembly strategy provides a new paradigm to design desired nanomedicines for stroma‐enriched pancreatic cancer therapy. [ABSTRACT FROM AUTHOR]

Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods--3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration--are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-theart automatic methods based on ASM. [ABSTRACT FROM AUTHOR]

Background and Aim Hepatolithiasis often requires repeated operations in East Asia. This study aims to evaluate the clinical application of three-dimensional reconstruction and visible simulation techniques for repeated operation in patients with intrahepatic calculi. Methods A medical image processing system was used for modeling, segmentation, and three-dimensional reconstruction of intrahepatic stones in 20 patients, consisting of 7 males and 13 females who were subjected to repeated surgical treatment from May 2010 to November 2011. The three-dimensional models of the liver and bile ducts in a standard template library format were then processed by the Free Form Modeling System. Accurate digital information about the bile duct system, lesions, calculi distribution, and surrounding organs obtained from all directions, multiple angles, and multistrata were used to decide the rational surgical modality. Virtual operations were then performed on the models with virtual surgical instruments in the Free Form Modeling System. The results were used to guide and were compared with the real surgical procedures performed. Results The surgical outcomes of all patients in this study were satisfactory. Three-dimensionally reconstructed models provided clear and strong relief perception and a user-friendly interface. Visible simulation surgery performed based on three-dimensionally reconstructed models led to an optimal operation plan that had great resemblance to the actual surgeries for cases with intrahepatic stones. Conclusions Three-dimensional reconstruction and visible simulation techniques had unique value in optimizing repeated operation plans and in guiding actual surgical procedures for patients with recurrent intrahepatic calculi. [ABSTRACT FROM AUTHOR]