Biomechanical analysis of posteromedial tibial plateau fracture fixation in fresh cadaveric bone.

This study aims to compare the mechanical strength of three different posterior-based internal fixation methods for posteromedial tibial plateau fractures. The study utilized 12 tibial plateaus harvested from fresh-frozen cadavers, and the posteromedial fracture fragments were created. The bones were then randomly assigned to one of three fixation methods: two posteroanterior lag screws (LS) size 4.0 mm, posterior buttress plate using a 3.5 mm small dynamic compression plate (DCP), or posterior buttress plate using a 3.5 mm T-shaped plate (TP). Biomechanical testing was performed by applying vertical compression force to the center of the posteromedial fracture fragment until the load to failure (displacement ≥ 3 mm) was reached, and displacement of the fragment was measured using a motion sensor. The data exhibited normal distribution, and one-way analysis of variance (ANOVA) was used to determine the load to failure, followed by Fisher post hoc Least-Significant Difference (LSD) to correct for multiple comparisons. The statistical analysis demonstrated significantly higher mean load to failure values in the T-shaped plate group compared to both the small dynamic compression plate group and the lag screw group (p < 0.05). However, after conducting further post hoc analysis, the observed significant differences were solely between the LS and TP groups (p = 0.021). These findings suggest that the T-shaped plate represents the most effective method for internally fixing posteromedial tibial plateau fractures.

Preoperative controlling nutritional status (CONUT) score is an independent prognostic factor in cholangiocarcinoma patients treated with hepatectomy.

Background: Nutritional status is one of the important factors determining the short- and long-term outcomes of surgery in cancer. This study aimed to assess the prognostic role of preoperative controlling nutritional status (CONUT) score in intrahepatic cholangiocarcinoma (iCCA) patients. Methods: A total of 101 iCCA patients who underwent hepatectomy between 2015 and 2018 at the Srinagarind Hospital, Khon Kaen University, were included in this retrospective study. Patients were classified according to the CONUT score. Univariate and multivariate analyses were performed to determine the correlation between clinicopathological features and overall survival. Results: Patients were categorized into normal nutrition (n = 40 or 39.5%), mild (n = 54 or 53.5%), and moderate-severe malnutrition (n = 7). Patients with high CONUT scores had significantly shorter survival (HR 2.55, 95% CI 1.04-6.25, p = 0.04). In multivariable analysis, tumor size (HR = 2.58, p < 0.01), the growth pattern of mass forming combined with periductal (HR = 4, p < 0.01), lymph node metastasis (HR = 7.20, p < 0.01) and high CONUT score (HR = 4.71, p = 0.01) were independent factors for poor survival of iCCA patients. Conclusion: The preoperative CONUT score is a simple prognostic factor to predict the outcomes of iCCA patients undergoing hepatectomy.

Programmed death­ligand 1 expression in tumor cells and tumor­infiltrating lymphocytes are associated with depth of tumor invasion in penile cancer.

The present study aimed to demonstrate the proportion of the programmed death-ligand 1 (PD-L1) expression in penile cancer patients and the association with clinicopathological parameters. Formalin-fixed paraffin-embedded specimens were obtained from 43 patients with primary penile squamous cell carcinoma treated at Srinagarind Hospital, Faculty of Medicine, Khon Kaen University, between 2008 and 2018. PD-L1 expression was evaluated by the immunohistochemistry using an SP263 monoclonal antibody. PD-L1 positivity was defined as >25% tumor cell staining or >25% tumor-associated immune cell staining. The correlation between PD-L1 expression and clinicopathological parameters was analyzed. A total of eight of 43 patients (18.6%) were identified as positive for PD-L1 expression in tumor cells and tumor-infiltrating lymphocytes. In the PD-L1 positive group, there was a significant association with pathological T stage (P=0.014) with a higher percentage of PD-L1 positive tumors in T1 stage compared with T2-T4 stage. In this cohort, there was a trend towards longer survival in patients with positive PD-L1 expression (5-year OS: 75% vs. 61.2%, P=0.19). Lymph node involvement and the location of tumor at the shaft of penis were two independent prognostic factors for survival. In conclusion, the PD-L1 expression was detected in 18% of penile cancer patients and high expression of PD-L1 was associated with the early T stage.

Artificial intelligence (AI) vs. human in hip fracture detection.

Objective: This study aimed to assess the diagnostic accuracy and sensitivity of a YOLOv4-tiny AI model for detecting and classifying hip fractures types. Materials and methods: In this retrospective study, a dataset of 1000 hip and pelvic radiographs was divided into a training set consisting of 450 fracture and 450 normal images (900 images total) and a testing set consisting of 50 fracture and 50 normal images (100 images total). The training set images were each manually augmented with a bounding box drawn around each hip, and each bounding box was manually labeled either (1) normal, (2) femoral neck fracture, (3) intertrochanteric fracture, or (4) subtrochanteric fracture. Next, a deep convolutional neural network YOLOv4-tiny AI model was trained using the augmented training set images, and then model performance was evaluated with the testing set images. Human doctors then evaluated the same testing set images, and the performances of the model and doctors were compared. The testing set contained no crossover data. Results: The resulting output images revealed that the AI model produced bounding boxes around each hip region and classified the fracture and normal hip regions with a sensitivity of 96.2%, specificity of 94.6%, and an accuracy of 95%. The human doctors performed with a sensitivity ranging from 69.2 to 96.2%. Compared with human doctors, the detection rate sensitivity of the model was significantly better than a general practitioner and first-year residents and equivalent to specialist doctors. Conclusions: This model showed hip fracture detection sensitivity comparable to well-trained radiologists and orthopedists and classified hip fractures highly accurately.

Diagnostic accuracy of deep learning for evaluation of C-spine injury from lateral neck radiographs.

Background: Traumatic spinal cord injury (TSI) is a leading cause of morbidity and mortality worldwide, with the cervical spine being the most affected. Delayed diagnosis carries a risk of morbidity and mortality. However, cervical spine CT scans are time-consuming, costly, and not always available in general care. In this study, deep learning was used to assess and improve the detection of cervical spine injuries on lateral radiographs, the most widely used screening method to help physicians triage patients quickly and avoid unnecessary CT scans. Materials and methods: Lateral neck or lateral cervical spine radiographs were obtained for patients who underwent CT scan of cervical spine. Ground truth was determined based on CT reports. CiRA CORE, a codeless deep learning program, was used as a training and testing platform. YOLO network models, including V2, V3, and V4, were trained to detect cervical spine injury. The diagnostic accuracy, sensitivity, and specificity of the model were calculated. Results: A total of 229 radiographs (129 negative and 100 positive) were selected for inclusion in our study from a list of 625 patients with cervical spine CT scans, 181 (28.9%) of whom had cervical spine injury. The YOLO V4 model performed better than the V2 or V3 (AUC = 0.743), with sensitivity, specificity, and accuracy of 80%, 72% and 75% respectively. Conclusion: Deep learning can improve the accuracy of lateral c-spine or neck radiographs. We anticipate that this will assist clinicians in quickly triaging patients and help to minimize the number of unnecessary CT scans.

Crizotinib-Associated Renal Cysts in Anaplastic Lymphoma Kinase-Positive Lung Cancer Patients: A Single-Center Experience.

Crizotinib is an anaplastic lymphoma kinase (ALK) inhibitor that was approved for ALK-harboring lung cancer. There have been reports about the development and progression of renal cysts from crizotinib. We report a series of 3 cases of crizotinib-associated renal cysts in patients admitted to our institution, with different kinds of presentation. A monitor for complex renal cysts is warranted in patients receiving crizotinib.