3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D PDAC, where the contrast is especially poor, owing to the high heterogeneity in both tumor and background tissues. In this study, we developed a new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images of PDAC tumors and pancreatic tissue, which can generate the inter-slice connection data that the existing 2D CT image synthesis models lack. The transition to 3D models allowed the preservation of contextual information from adjacent slices, improving efficiency and accuracy, especially for the poor-contrast challenging case of PDAC. PDAC's challenging characteristics, such as an iso-attenuating or hypodense appearance and lack of well-defined margins, make tumor shape and texture learning challenging. To overcome these challenges and improve the performance of 3D GAN models, our innovation was to develop a 3D U-Net architecture for the generator, to improve shape and texture learning for PDAC tumors and pancreatic tissue. Thorough examination and validation across many datasets were conducted on the developed 3D GAN model, to ascertain the efficacy and applicability of the model in clinical contexts. Our approach offers a promising path for tackling the urgent requirement for creative and synergistic methods to combat PDAC. The development of this GAN-based model has the potential to alleviate data scarcity issues, elevate the quality of synthesized data, and thereby facilitate the progression of deep learning models, to enhance the accuracy and early detection of PDAC tumors, which could profoundly impact patient outcomes. Furthermore, the model has the potential to be adapted to other types of solid tumors, hence making significant contributions to the field of medical imaging in terms of image processing models.

Port first vs. Tip first: does difference in portacath insertion techniques reduce complication rates.

BACKGROUND: Accurate placement of central venous access devices is important to avoid complications such as infection, thrombosis and migration. This audit aims to determine if there is a difference in complication rates and accuracy of tip position between two different intravenous jugular (IVJ) port device insertion techniques: fixation of port first (PF) versus tip first (TF). METHODS: Patients who underwent port device insertions from 2019 to 2021 at the Cairns Hospital were identified from the Australia Vascular Audit database. The primary outcome of accurate catheter tip placement (based on radiological criteria), secondary outcomes of line infection, thrombosis and other outcomes such as removal rates were gathered and compared between the 2 groups of port first (PF) versus tip first (TF) insertion. RESULTS: Two-hundred and twenty-seven patients underwent port device insertions during the period of interest. 98 (43.2%) patients had a PF insertion technique and 129 (56.8%) had a TF insertion technique. In the PF group, 81.6% (P < 0.05) of lines were accurately placed compared to 69.8% (P < 0.05) in the TF group. The line related thrombosis rate was 1% (P < 0.05) in the PF group compared to 6.2% (P < 0.05) in the TF group. Rate of line infections in the PF group was 5.1% (P = 0.92) compared to 6.2% (P = 0.92) in the TF group. CONCLUSION: The port first technique for IVJ port device placement was associated with higher accuracy and lower thrombosis rates and this was statistically significant. Further studies should involve larger multicentre populations to compare results between practitioners.

Does Working-Memory Training Given to Reception-Class Children Improve the Speech of Children at Risk of Fluency Difficulty?

Procedures were designed to test for the effects of working-memory training on children at risk of fluency difficulty that apply to English and to many of the languages spoken by children with English as an Additional Language (EAL) in UK schools. Working-memory training should: (1) improve speech fluency in high-risk children; (2) enhance non-word repetition (NWR) (phonological) skills for all children; (3) not affect word-finding abilities. Children starting general education (N = 232) were screened to identify those at risk of fluency difficulty. Children were selected who were at high-risk (12), or low-risk (27) of fluency difficulty. For the low-risk children 10 received, and 17 did not receive, the working-memory training. All children in the treatment groups received working-memory training over a 2-week period. For the high-risk group, fluency improved and lasted for at least a week after the end of the study. Phonological skills improved in this group and in the low-risk group who received the training and the improvements continued for at least a week. The low-risk group who did not receive working-memory training showed no improvements, and no group improved word-finding ability.