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Background Methods Results Conclusions Recent advancements in cervical interlaminar epidural steroid injections have given rise to the modified paramedian interlaminar (mPIL) approach. The objective of this study was to perform an analysis of the contrast spread pattern within the cervical epidural space, taking into account different needle tip positions in the mPIL approach.A total of 48 patients were included in the study and randomly assigned to either the medial or lateral group based on the needle tip's position in the anterior–posterior view. The primary outcome measured was the contrast flow under fluoroscopic visualization. As a secondary outcome, we analyzed the location of the needle tip position in both lateral and contralateral oblique views. Clinical effectiveness was assessed by measuring pain intensity and functional disability post‐procedure.Significant disparities were noted in the ventral distribution of contrast between the medial and lateral groups. In the lateral images, needle tips in the lateral group were positioned more ventrally compared to those in the medial group. Both groups exhibited statistically significant improvements in neck and radicular pain, as well as functional status, 4 weeks after treatment, with no significant differences between them.Our results suggest that the ventral dispersion of contrast material during cervical interlaminar epidural steroid injections using the mPIL approach may vary depending on the needle tip location. [ABSTRACT FROM AUTHOR]

Background and Objectives: Local infiltration analgesia (LIA) represents a potential approach to reducing pain in patients undergoing total hip arthroplasty (THA). The pericapsular nerve group (PENG) block also provides adequate analgesia for fractures and THA. As most hip surgeries use a lateral incision, affecting the cutaneous supply by branches of the lateral femoral cutaneous nerve (LFCN), the LFCN block can contribute to postoperative analgesia. However, no studies have investigated the effectiveness of supplemental PENG block combined with LFCN block in patients undergoing LIA after hip fracture surgery. Our study aimed to assess the effectiveness of PENG combined with LFCN block following hip fracture surgery in patients who underwent LIA. Materials and Methods: Forty-six patients were randomly assigned to LIA or PENG + LFCN + LIA groups. The primary outcome was the pain score at rest and during movement at 2, 6, 12, 24, and 48 h postoperatively. The total opioid dose for postoperative analgesia was also measured at the same time points. Secondary outcomes included postoperative cognitive function assessment. Results: The median pain scores at rest and during movement were lower in the PENG + LFCN + LIA group throughout the study periods compared to the LIA group, except at 2 h (at rest) and 48 h (during movement) after surgery. The total fentanyl dose was lower in the PENG + LFCN + LIA group at all time points after surgery when compared to the LIA group. Postoperative delirium incidence and the median abbreviated mental test scores were not significantly different between the two groups. Conclusions: The combination of PENG and LFCN blocks may contribute to enhanced recovery for patients undergoing LIA after hip fracture surgery. However, further well-controlled research is necessary to determine the effectiveness of supplemental PENG combined with LFCN block in addressing cognitive deficits in these patients. [ABSTRACT FROM AUTHOR]

BACKGROUND: It is often difficult to automatically segment lung tumors due to the large tumor size variation ranging from less than 1 cm to greater than 7 cm depending on the T-stage. OBJECTIVE: This study aims to accurately segment lung tumors of various sizes using a consistency learning-based multi-scale dual-attention network (CL-MSDA-Net). METHODS: To avoid under- and over-segmentation caused by different ratios of lung tumors and surrounding structures in the input patch according to the size of the lung tumor, a size-invariant patch is generated by normalizing the ratio to the average size of the lung tumors used for the training. Two input patches, a size-invariant patch and size-variant patch are trained on a consistency learning-based network consisting of dual branches that share weights to generate a similar output for each branch with consistency loss. The network of each branch has a multi-scale dual-attention module that learns image features of different scales and uses channel and spatial attention to enhance the scale-attention ability to segment lung tumors of different sizes. RESULTS: In experiments with hospital datasets, CL-MSDA-Net showed an F1-score of 80.49%, recall of 79.06%, and precision of 86.78%. This resulted in 3.91%, 3.38%, and 2.95% higher F1-scores than the results of U-Net, U-Net with a multi-scale module, and U-Net with a multi-scale dual-attention module, respectively. In experiments with the NSCLC-Radiomics datasets, CL-MSDA-Net showed an F1-score of 71.7%, recall of 68.24%, and precision of 79.33%. This resulted in 3.66%, 3.38%, and 3.13% higher F1-scores than the results of U-Net, U-Net with a multi-scale module, and U-Net with a multi-scale dual-attention module, respectively. CONCLUSIONS: CL-MSDA-Net improves the segmentation performance on average for tumors of all sizes with significant improvements especially for small sized tumors. [ABSTRACT FROM AUTHOR]