A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT.

PURPOSE: To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians. METHODS: We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC. RESULTS: Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001). CONCLUSION: A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.

A prospective comparative study on the effectiveness of two different non-adherent polyurethane dressings on split-thickness skin graft donor sites.

AIM: To analyze differences in re-epithelization, exudate absorbency, ease and pain on dressing removal between ALLEVYN™ Non-Adhesive and Betaplast™ N. METHODOLOGY: Patients admitted to the general ward undergoing split skin grafting were recruited. Allevyn and Betaplast were applied on the donor site. Exudate absorption was assessed daily using an absorbency grading chart. Dressing change was done on post-operative day five. Ease of dressing removal and pain score using the Wong-Baker Pain Scale was assessed. The percentage of re-epithelization for each dressing was assessed. RESULTS: 30 patients were recruited. There was a statistically significant difference in exudate absorption on post-operative day 3 (z = -2.006, p = 0.045, T = 236) and post-operative day 4 (z = -2.026, p = 0.0143, T = 188), pain score (z = -2.861, p = 0.004, T = 180), ease of removal (z = -2.668, p = 0.008, T = 126) and re-epithelization (z = -2.566, p = 0.009, T = 336) between Betaplast and Allevyn. CONCLUSION: Betaplast may have faster re-epithelization, better exudate absorption, and is easier to remove while minimizing discomfort as compared to Allevyn.

Quantitative investigation on the critical thickness of the dielectric shell for metallic nanoparticles determined by the plasmon decay length.

Inert dielectric shells coating the surface of metallic nanoparticles (NPs) are important for enhancing the NPs' stability, biocompatibility, and realizing targeting detection, but they impair NPs' sensing ability due to the electric fields damping. The dielectric shell not only determines the distance of the analyte from the NP surface, but also affects the field decay. From a practical point of view, it is extremely important to investigate the critical thickness of the shell, beyond which the NPs are no longer able to effectively detect the analytes. The plasmon decay length of the shell-coated NPs determines the critical thickness of the coating layer. Extracting from the exponential fitting results, we quantitatively demonstrate that the critical thickness of the shell exhibits a linear dependence on the NP volume and the dielectric constants of the shell and the surrounding medium, but only with a small variation influenced by the NP shape where the dipole resonance is dominated. We show the critical thickness increases with enlarging the NP sizes, or increasing the dielectric constant differences between the shell and surrounding medium. The findings are essential for applications of shell-coated NPs in plasmonic sensing.

AB077. An evaluation of prognostic scoring systems for survival in a surgically treated cohort of 318 metastatic spine tumour surgery patients.

BACKGROUND: Survival prognostication plays a key role in the decision-making process for the surgical treatment of patients with spinal metastases. In the past traditional scoring systems such as the modified Tokuhashi and Tomita scoring systems have been used extensively, however in recent years their accuracy has been called into question. This has led to the development of machine learning algorithms to predict survival. In this study, we aim to compare the accuracy of prognostic scoring systems in a surgically treated cohort of patients. METHODS: This is a retrospective review of 318 surgically treated spinal metastases patients between 2009 and 2021. The primary outcome measured was survival from the time of diagnosis. Predicted survival at 3 months, 6 months and 1 year based on the prognostic scoring system was compared to actual survival. Predictive values of each scoring system were measured via area under receiver operating characteristic curves (AUROC). The following scoring systems were compared, Modified Tokuhashi (MT), Tomita (T), Modified Bauer (MB), Van Den Linden (VDL), Oswestry (O), New England Spinal Metastases score (NESMS), Global Spine Study Tumor Group (GSTSG) and Skeletal Oncology Research Group (SORG) scoring systems. RESULTS: For predicting 3 months survival, the GSTSG 0.980 (0.949-1.0) and NESM 0.980 (0.949-1.0) had outstanding predictive value, while the SORG 0.837 (0.751-0.923) and O 0.837 (0.775-0.900) had excellent predictive value. While for 6 months survival, only the O 0.819 (0.758-0.880) had excellent predictive value and the GSTSG 0.791(0.725-0.857) had acceptable predictive value. For 1 year survival, the NESM 0.871 (0.822-0.919) had excellent predictive value and the O 0.722 (0.657-0.786) had acceptable predictive value. The MT, T and MB scores had an area under the curve (AUC) of <0.5 for 3-month, 6-month and 1-year survival. CONCLUSIONS: Increasingly, traditional scoring systems such as the MT, T and MB scoring systems have become less predictive. While newer scoring systems such as the GSTSG, NESM and SORG have outstanding to excellent predictive value, there is no one survival scoring system that is able to accurately prognosticate survival at all 3 time points. A multidisciplinary, personalised approach to survival prognostication is needed.