Pancreatic cancer ranks in the 10th–11th position among cancers affecting men in Taiwan, besides being a rather difficult-to-treat disease. The overall 5-year survival rate of pancreatic cancer is only 5–10%, while that of resectable pancreatic cancer is still approximately 15–20%. Cancer stem cells possess intrinsic detoxifying mechanisms that allow them to survive against conventional therapy by developing multidrug resistance. This study was conducted to investigate how to overcome chemoresistance and its mechanisms in pancreatic cancer stem cells (CSCs) using gemcitabine-resistant pancreatic cancer cell lines. Pancreatic CSCs were identified from human pancreatic cancer lines. To determine whether CSCs possess a chemoresistant phenotype, the sensitivity of unselected tumor cells, sorted CSCs, and tumor spheroid cells to fluorouracil (5-FU), gemcitabine (GEM), and cisplatin was analyzed under stem cell conditions or differentiating conditions. Although the mechanisms underlying multidrug resistance in CSCs are poorly understood, ABC transporters such as ABCG2, ABCB1, and ABCC1 are believed to be responsible. Therefore, we measured the mRNA expression levels of ABCG2, ABCB1, and ABCC1 by real-time RT-PCR. Our results showed that no significant differences were found in the effects of different concentrations of gemcitabine on CSCs CD44+/EpCAM+ of various PDAC cell line cultures (BxPC-3, Capan-1, and PANC-1). There was also no difference between CSCs and non-CSCs. Gemcitabine-resistant cells exhibited distinct morphological changes, including a spindle-shaped morphology, the appearance of pseudopodia, and reduced adhesion characteristics of transformed fibroblasts. These cells were found to be more invasive and migratory, and showed increased vimentin expression and decreased E-cadherin expression. Immunofluorescence and immunoblotting experiments demonstrated increased nuclear localization of total β-catenin. These alterations are hallmarks of epithelial-to-mesenchymal transition (EMT). Resistant cells showed activation of the receptor protein tyrosine kinase c-Met and increased expression of the stem cell marker cluster of differentiation (CD) 24, CD44, and epithelial specific antigen (ESA). We concluded that the expression of the ABCG2 transporter protein was significantly higher in CD44+ and EpCAM+ CSCs of PDAC cell lines. Cancer stem-like cells exhibited chemoresistance. Gemcitabine-resistant pancreatic tumor cells were associated with EMT, a more aggressive and invasive phenotype of numerous solid tumors. Increased phosphorylation of c-Met may also be related to chemoresistance, and EMT and could be used as an attractive adjunctive chemotherapeutic target in pancreatic cancer.

Background: Splenic injury is the most common solid visceral injury in blunt abdominal trauma, and high-resolution abdominal computed tomography (CT) can adequately detect the injury. However, these lethal injuries sometimes have been overlooked in current practice. Deep learning (DL) algorithms have proven their capabilities in detecting abnormal findings in medical images. The aim of this study is to develop a three-dimensional, weakly supervised DL algorithm for detecting splenic injury on abdominal CT using a sequential localization and classification approach. Material and methods: The dataset was collected in a tertiary trauma center on 600 patients who underwent abdominal CT between 2008 and 2018, half of whom had splenic injuries. The images were split into development and test datasets at a 4 : 1 ratio. A two-step DL algorithm, including localization and classification models, was constructed to identify the splenic injury. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Grad-CAM (Gradient-weighted Class Activation Mapping) heatmaps from the test set were visually assessed. To validate the algorithm, we also collected images from another hospital to serve as external validation data. Results: A total of 480 patients, 50% of whom had spleen injuries, were included in the development dataset, and the rest were included in the test dataset. All patients underwent contrast-enhanced abdominal CT in the emergency room. The automatic two-step EfficientNet model detected splenic injury with an AUROC of 0.901 (95% CI: 0.836–0.953). At the maximum Youden index, the accuracy, sensitivity, specificity, PPV, and NPV were 0.88, 0.81, 0.92, 0.91, and 0.83, respectively. The heatmap identified 96.3% of splenic injury sites in true positive cases. The algorithm achieved a sensitivity of 0.92 for detecting trauma in the external validation cohort, with an acceptable accuracy of 0.80. Conclusions: The DL model can identify splenic injury on CT, and further application in trauma scenarios is possible.

Objective: To identify the feasibility and efficiency of deep convolutional neural networks (DCNNs) in the detection of ankle fractures and to explore ensemble strategies that applied multiple projections of radiographs. Ankle radiographs (AXRs) are the primary tool used to diagnose ankle fractures. Applying DCNN algorithms on AXRs can potentially improve the diagnostic accuracy and efficiency of detecting ankle fractures. Methods: A DCNN was trained using a trauma image registry, including 3102 AXRs. We separately trained the DCNN on anteroposterior (AP) and lateral (Lat) AXRs. Different ensemble methods, such as “sum-up,” “severance-OR,” and “severance-Both,” were evaluated to incorporate the results of the model using different projections of view. Results: The AP/Lat model’s individual sensitivity, specificity, positive-predictive value, accuracy, and F1 score were 79%/84%, 90%/86%, 88%/86%, 83%/85%, and 0.816/0.850, respectively. Furthermore, the area under the receiver operating characteristic curve (AUROC) of the AP/Lat model was 0.890/0.894 (95% CI: 0.826–0.954/0.831–0.953). The sum-up method generated balanced results by applying both models and obtained an AUROC of 0.917 (95% CI: 0.863–0.972) with 87% accuracy. The severance-OR method resulted in a better sensitivity of 90%, and the severance-Both method obtained a high specificity of 94%. Conclusion: Ankle fracture in the AXR could be identified by the trained DCNN algorithm. The selection of ensemble methods can depend on the clinical situation which might help clinicians detect ankle fractures efficiently without interrupting the current clinical pathway. Advances in knowledge: This study demonstrated different ensemble strategies of AI algorithms on multiple view AXRs to optimize the performance in various clinical needs.

Automated detection of the region of interest (ROI) is a critical step in the two-step classification system in several medical image applications. However, key information such as model parameter selection, image annotation rules, and ROI confidence score are essential but usually not reported. In this study, we proposed a practical framework of ROI detection by analyzing hip joints seen on 7399 anteroposterior pelvic radiographs (PXR) from three diverse sources. We presented a deep learning-based ROI detection framework utilizing a single-shot multi-box detector with a customized head structure based on the characteristics of the obtained datasets. Our method achieved average intersection over union (IoU) = 0.8115, average confidence = 0.9812, and average precision with threshold IoU = 0.5 (AP50) = 0.9901 in the independent testing set, suggesting that the detected hip regions appropriately covered the main features of the hip joints. The proposed approach featured flexible loose-fitting labeling, customized model design, and heterogeneous data testing. We demonstrated the feasibility of training a robust hip region detector for PXRs. This practical framework has a promising potential for a wide range of medical image applications.

(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use design thinking steps to develop a DL algorithm that accelerates deployment in clinical practice and improves its performance to meet clinical requirements. (2) Methods: We applied the design thinking process to interview clinical doctors and gain insights to develop and modify the DL algorithm to meet clinical scenarios. We also compared the DL performance of the algorithm before and after the integration of design thinking. (3) Results: After empathizing with clinical doctors and defining their needs, we identified the unmet need of five trauma surgeons as “how to reduce the misdiagnosis of femoral fracture by pelvic plain film (PXR) at initial emergency visiting”. We collected 4235 PXRs from our hospital, of which 2146 had a hip fracture (51%) from 2008 to 2016. We developed hip fracture DL detection models based on the Xception convolutional neural network by using these images. By incorporating design thinking, we improved the diagnostic accuracy from 0.91 (0.84–0.96) to 0.95 (0.93–0.97), the sensitivity from 0.97 (0.89–1.00) to 0.97 (0.94–0.99), and the specificity from 0.84 (0.71–0.93) to 0.93(0.990–0.97). (4) Conclusions: In summary, this study demonstrates that design thinking can ensure that DL solutions developed for trauma care are user-centered and meet the needs of patients and healthcare providers.

This study aimed to assess current evidence regarding the effect of selenium (Se) supplementation on the prognosis in patients sustaining trauma. MEDLINE, Embase, and Web of Science databases were searched with the following terms: “trace element”, “selenium”, “copper”, “zinc”, “injury”, and “trauma”. Seven studies were included in the meta-analysis. The pooled results showed that Se supplementation was associated with a lower mortality rate (OR 0.733, 95% CI: 0.586, 0.918, p = 0.007; heterogeneity, I² = 0%). Regarding the incidence of infectious complications, there was no statistically significant benefit after analyzing the four studies (OR 0.942, 95% CI: 0.695, 1.277, p = 0.702; heterogeneity, I² = 14.343%). The patients with Se supplementation had a reduced ICU length of stay (standard difference in means (SMD): −0.324, 95% CI: −0.382, −0.265, p < 0.001; heterogeneity, I² = 0%) and lesser hospital length of stay (SMD: −0.243, 95% CI: −0.474, −0.012, p < 0.001; heterogeneity, I² = 45.496%). Se supplementation after trauma confers positive effects in decreasing the mortality and length of ICU and hospital stay.