Citations

Adaptive radiotherapy (ART) workflows are increasingly adopted to achieve dose escalation and tissue sparing under dynamic anatomical conditions. However, recontouring and time constraints hinder the implementation of real-time ART workflows. Various auto-segmentation methods, including deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS), have been developed to address these challenges. Despite the potential of DLS methods, clinical implementation remains difficult due to the need for large, high-quality datasets to ensure model generalizability. This study introduces an InterVision framework for segmentation. The InterVision framework can interpolate or create intermediate visuals between existing images to generate specific patient characteristics. The InterVision model is trained in two steps: (1) generating a general model using the dataset, and (2) tuning the general model using the dataset generated from the InterVision framework. The InterVision framework generates intermediate images between existing patient image slides using deformable vectors, effectively capturing unique patient characteristics. By creating a more comprehensive dataset that reflects these individual characteristics, the InterVision model demonstrates the ability to produce more accurate contours compared to general models. Models are evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) for 18 structures in 20 test patients. As a result, the Dice score was 0.81 ± 0.05 for the general model, 0.82 ± 0.04 for the general fine-tuning model, and 0.85 ± 0.03 for the InterVision model. The Hausdorff distance was 3.06 ± 1.13 for the general model, 2.81 ± 0.77 for the general fine-tuning model, and 2.52 ± 0.50 for the InterVision model. The InterVision model showed the best performance compared to the general model. The InterVision framework presents a versatile approach adaptable to various tasks where prior information is accessible, such as in ART settings. This capability is particularly valuable for accurately predicting complex organs and targets that pose challenges for traditional deep learning algorithms.

Hepatic stellate cells (HSC) and liver endothelial cells (LEC) migrate to sites of injury and perpetuate alcohol-induced liver injury. High-mobility group box 1 (HMGB1) is a protein released from the nucleus of injured cells that has been implicated as a proinflammatory mediator. We hypothesized that HMGB1 may be released from ethanol-stimulated liver parenchymal cells and contribute to HSC and LEC recruitment. Ethanol stimulation of rat hepatocytes and HepG2 cells resulted in translocation of HMGB1 from the nucleus as assessed by Western blot. HMGB1 protein levels were increased in the supernatant of ethanol-treated hepatocytes compared with vehicle-treated cells. Migration of both HSC and LEC was increased in response to conditioned medium for ethanol-stimulated hepatocytes (CMEtOH) compared with vehicle-stimulated hepatocytes (CMVEH) ( P < 0.05). However, the effect of CMEtOHon migration was almost entirely reversed by treatment with HMGB1-neutralizing antibody or when HepG2 cells were pretransfected with HMGB1-siRNA compared with control siRNA-transfected HepG2 cells ( P < 0.05). Recombinant HMGB1 (100 ng/ml) also stimulated migration of HSC and LEC compared with vehicle stimulation ( P < 0.05 for both HSC and LEC). HMGB1 stimulation of HSC increased the phosphorylation of Src and Erk and HMGB1-induced HSC migration was blocked by the Src inhibitor PP2 and the Erk inhibitor U0126. Hepatocytes release HMGB1 in response to ethanol with subsequent recruitment of HSC and LEC. This pathway has implications for HSC and LEC recruitment to sites of ethanol-induced liver injury.