Thymic epithelial tumor with superior vena cava syndrome diagnosed by percutaneous endovascular tumor biopsy: A report of two cases

Thymic epithelial tumor is a rare, potentially progressive disease that commonly infiltrates mediastinal structures. In rare cases, it may cause superior vena cava syndrome. Pretreatment histopathological diagnosis is essential to determine the most effective treatment strategy. Percutaneous endovascular biopsy is a rarely reported non-surgical diagnostic option for large vessel tumoral involvement. We report two cases of thymic epithelial tumor with superior vena cava syndrome diagnosed by percutaneous endovascular biopsy. No procedural complications occurred, and subsequent systemic treatment was promptly administered. This procedure may have potential as a useful diagnostic method for patients with mediastinal tumors involving large vessels.     

Murine models of posterolateral spinal fusion: A systematic review

BackgroundRodent models are commonly used experimentally to assess treatment effectiveness in spinal fusion. Certain factors are associated with better fusion rates. The objectives of the present study were to report the protocols most frequently used, to evaluate factors known to positively influence fusion rate, and to identify new factors.MethodA systematic literature search of PubMed and Web of Science found 139 experimental studies of posterolateral lumbar spinal fusion in rodent models. Data for level and location of fusion, animal strain, sex, weight and age, graft, decortication, fusion assessment and fusion and mortality rates were collected and analyzed.ResultsThe standard murine model for spinal fusion was male Sprague Dawley rats of 295 g weight and 13 weeks’ age, using decortication, with L4-L5 as fusion level. The last two criteria were associated with significantly better fusion rates. On manual palpation, the overall mean fusion rate in rats was 58% and the autograft mean fusion rate was 61%. Most studies evaluated fusion as a binary on manual palpation, and only a few used CT and histology. Average mortality was 3.03% in rats and 1.56% in mice.ConclusionsThese results suggest using a rat model, younger than 10 weeks and weighing more than 300 grams on the day of surgery, to optimize fusion rates, with decortication before grafting and fusing the L4-L5 level.     

Recent advances in lipid nanoparticles for delivery of nucleic acid,mRNA, and gene editing-based therapeutics

Lipid nanoparticles (LNPs) are becoming popular as a means of delivering therapeutics, including those based on nucleic acids and mRNA. The mRNA-based coronavirus disease 2019 vaccines are perfect examples to highlight the role played by drug delivery systems in advancing human health. The fundamentals of LNPs for the delivery of nucleic acid- and mRNA-based therapeutics, are well established. Thus, future research on LNPs will focus on addressing the following: expanding the scope of drug delivery to different constituents of the human body, expanding the number of diseases that can be targeted, and studying the change in the pharmacokinetics of LNPs under physiological and pathological conditions. This review article provides an overview of recent advances aimed at expanding the application of LNPs, focusing on the pharmacokinetics and advantages of LNPs. In addition, analytical techniques, library construction and screening, rational design, active targeting, and applicability to gene editing therapy have also been discussed.     

Prediction of the response to photodynamic therapy in patients with chronic central serous chorioretinopathy based on optical coherence tomography using deep learning

PurposeTo assess the prediction of the response to photodynamic therapy (PDT) in chronic central serous chorioretinopathy (CSCR) based on spectral-domain optical coherence tomography (SD-OCT) images using deep learning (DL).MethodsRetrospective study including 216 eyes of 175 patients with CSCR and persistent subretinal fluid (SRF) who underwent half-fluence PDT. SD-OCT macular examination was performed before (baseline) and 3 months after treatment. Patients were classified into groups by experts based on the response to PDT: Group 1, complete SRF resorption (n = 100); Group 2, partial SRF resorption (n = 66); and Group 3, absence of any SRF resorption (n = 50). This work proposes different computational approaches: 1st approach compares all groups; 2nd compares groups 1 vs. 2 and 3 together; 3rd compares groups 2 vs. 3.ResultsThe mean age was 55.6 ± 10.9 years and 70.3% were males. In the first approach, the algorithm showed a precision of up to 57% to detect the response to treatment in group 1 based on the initial scan, with a mean average accuracy of 0.529 ± 0.035. In the second model, the mean accuracy was higher (0.670 ± 0.046). In the third approach, the algorithm showed a precision of 0.74 ± 0.12 to detect the response to treatment in group 2 (partial SRF resolution) and 0.69 ± 0.15 in group 3 (absence of SRF resolution).ConclusionDespite the high clinical variability in the response of chronic CSCR to PDT, this DL algorithm offers an objective and promising tool to predict the response to PDT treatment in clinical practice.     

Loss of skeletal muscle density during neoadjuvant chemotherapy in older women with advanced stage ovarian cancer is associated with postoperative complications

ObjectiveTo assess the association between loss of lumbar skeletal muscle mass and density during neoadjuvant chemotherapy (NACT) and postoperative complications after interval cytoreductive surgery (CRS) in older patients with ovarian cancer.Materials and methodsThis multicenter, retrospective cohort study included patients aged 70 years and older with primary advanced stage ovarian cancer (International Federation of Gynecology and Obstetrics stage III-IV), treated with NACT and interval CRS. Skeletal muscle mass and density were retrospectively assessed using Skeletal Muscle Index (SMI) and Muscle Attenuation (MA) on routinely made Computed Tomography scans before and after NACT. Loss of skeletal muscle mass or density was defined as >2% decrease per 100 days in SMI or MA during NACT.ResultsIn total, 111 patients were included. Loss of skeletal muscle density during NACT was associated with developing any postoperative complication ≤30 days after interval CRS both in univariable (Odds Ratio (OR) 3.69; 95% Confidence Interval (CI) 1.57–8.68) and in multivariable analysis adjusted for functional impairment and WHO performance status (OR 3.62; 95%CI 1.27–10.25). Loss of skeletal muscle density was also associated with infectious complications (OR 3.67; 95%CI 1.42–9.52) and unintended discontinuation of adjuvant chemotherapy (OR 5.07; 95%CI 1.41–18.19). Unlike loss of skeletal muscle density, loss of skeletal muscle mass showed no association with postoperative outcomes.ConclusionIn older patients with ovarian cancer, loss of skeletal muscle density during NACT is associated with worse postoperative outcomes. These results could add to perioperative risk assessment, guiding the decision to undergo surgery or the need for perioperative interventions.     

Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma

ObjectiveTo evaluate the performance of a deep learning (DL)-based radiomics strategy on contrast-enhanced computed tomography (CT) to predict microvascular invasion (MVI) status and clinical outcomes, recurrence-free survival (RFS) and overall survival (OS) in patients with early stage hepatocellular carcinoma (HCC) receiving surgical resection.MethodsAll 283 eligible patients were included retrospectively between January 2008 and December 2015, and assigned into the training cohort (n = 198) and the testing cohort (n = 85). We extracted radiomics features via handcrafted radiomics analysis manually and DL analysis of pretrained convolutional neural networks via transfer learning automatically. Support vector machine was adopted as the classifier. A clinical-radiological model for MVI status integrated significant clinical features and the radiological signature generated from the radiological model with the optimal area under the receiver operating characteristics curve (AUC) in the testing cohort. Otherwise, DL-based prognostic models were constructed in prediction of recurrence and mortality via Cox proportional hazard analysis.ResultsThe clinical-radiological model for MVI represented an AUC of 0.909, accuracy of 96.47%, sensitivity of 90.91%, specificity of 97.30%, positive predictive value of 83.33%, and negative predictive value of 98.63% in the testing cohort. The clinical-radiological models for identification of RFS and OS outperformed prediction performance of the clinical model or the DL signature alone. The DL-based integrated model for prognostication showed great predictive value with significant classification and discrimination abilities after validation.ConclusionsThe integrated DL-based radiomics models achieved accurate preoperative prediction of MVI status, and might facilitate predicting tumor recurrence and mortality in order to optimize clinical decisions for patients with early stage HCC.