Ultrasound-guided minimally invasive procedures are widely used to treat thyroid diseases. The objective of this study was to assess the efficacy and safety of ethanol ablation (EA) in comparison with other non-surgical options in the treatment of benign thyroid cystic nodules. We conducted a systematic search of studies on EA for thyroid cystic nodules, mainly in the Ovid-MEDLINE and Embase, Web of Science, and Cochrane databases. The standardized mean difference (SMD) of the volume reduction ratio (VRR) after EA versus other non-surgical treatments comprised the primary outcome, whereas the odds ratio (OR) of therapeutic success rates between the two groups comprised the secondary outcome. The meta-analysis included 19 studies (four randomized controlled trials and 15 non-randomized studies) with 1,514 participants. The cumulative VRR of EA was 83.908% (95% confidence interval [CI], 79.358% to 88.457%). EA had a significantly higher pooled VRR (SMD, 0.381; 95% CI, 0.028 to 0.734; P=0.030), but not a significantly higher pooled therapeutic success rate (OR, 0.867; 95% CI, 0.132 to 5.689; P=0.880), than other forms of non-surgical management including radiofrequency ablation (RFA), polidocanol sclerotherapy, and simple aspiration with or without saline flush. However, the VRR and therapeutic success rate were not significantly different between EA and RFA. Major complications were recorded only in six patients (0.53%) with self-limiting dysphonia. The role of EA as the first-line treatment for benign thyroid cysts and predominantly cystic nodules is supported by its high effectiveness and good safety profile compared to other currently available non-surgical options.

(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on computed tomography (CT) images has been proven effective in predicting hematoma expansion and poor neurologic outcome. In contrast, there is limited evidence on its predictive abilities for traumatic intraparenchymal hemorrhage (IPH). (2) Methods: A retrospective analysis of 107 traumatic IPH patients was conducted. Among them, 45 patients (42.1%) showed hemorrhagic progression of contusion (HPC) and 51 patients (47.7%) had poor neurological outcome. The IPH on the initial CT was manually segmented for radiomics analysis. After feature extraction, selection and repeatability evaluation, several machine learning algorithms were used to derive radiomics scores (R-scores) for the prediction of HPC and poor neurologic outcome. (3) Results: The AUCs for R-scores alone to predict HPC and poor neurologic outcome were 0.76 and 0.81, respectively. Clinical parameters were used to build comparison models. For HPC prediction, variables including age, multiple IPH, subdural hemorrhage, Injury Severity Score (ISS), international normalized ratio (INR) and IPH volume taken together yielded an AUC of 0.74, which was significantly (p = 0.022) increased to 0.83 after incorporation of the R-score in a combined model. For poor neurologic outcome prediction, clinical variables of age, Glasgow Coma Scale, ISS, INR and IPH volume showed high predictability with an AUC of 0.92, and further incorporation of the R-score did not improve the AUC. (4) Conclusion: The results suggest that radiomics analysis of IPH lesions on initial CT images has the potential to predict HPC and poor neurologic outcome in traumatic IPH patients. The clinical and R-score combined model further improves the performance of HPC prediction.

Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment.

Of the proposed animal interbody fusion models, rat caudal discs have gained popularity in disc research due to their strong resemblance to human discs with respect to geometry, composition and mechanical properties. The purpose of this study is to demonstrate an efficient, repeatable and easily accessible animal model of interbody fusion for future research into mechanical testing and graft materials. Twelve 12-week-old female Sprague-Dawley (SD) rats underwent caudal interbody fusion of the third and fourth coccygeal vertebrae of the tail. Serial radiological evaluation, and histological evaluation and manual palpation after sacrifice were performed to assess the fusion quality. Mechanical testing of functional units (FUs) of non-operated and operated segments was compared using a three-point bending test. At postoperative 12 weeks, callus formation was observed at the fusion sites in all rats, with the mean radiological evaluations of 2.75/3 according to the Bransford classification. Newly formed bone tissue was also observed in all rats with the mean histological score of 5.85/7, according to the Emery grading system. No palpable gaps and obvious change of bending stiffness was observed in the operated segments. The mean bending stiffness of the FUs was statistically higher than that of the control FUs (26.57 ± 6.71 N/mm vs. 12.45 ± 3.21 N/mm, p < 0.01). The rat caudal disc interbody fusion model proved to be an efficient, repeatable and easily accessible model. Future research into adjuvant treatments like growth factor injection and alternative fusion materials under conditions of osteoporosis using this model would be worthwhile.

Background: Although planned cesarean delivery (PCD) is the mainstay of management for abnormal placentation, some patients still require emergency cesarean delivery (ECD). We aimed to systematically analyze the impact of various modes of delivery on neonatal outcomes. Methods: This study was complied with the PRISMA guidelines and was registered in the PROSPERO (code: CRD42022379487). A systematic search was conducted on Ovid MEDLINE and Embase, Web of Science, PubMed, and the Cochrane databases. Data extracted included gestational age at delivery, birth weight, the Apgar scores at 1 and 5 minutes, numbers of newborns with low Apgar score (<7) at 5 minutes, the rates of neonatal intensive care unit admission, and the rates of neonatal mortality. Results: Fifteen cohort studies met the inclusion criteria, comprising a total of 2565 women (2567 neonates) who underwent PCD (n = 1483) or ECD (n = 1082) for prenatally diagnosed placenta accreta spectrum (PAS) and/or placenta previa (PP). Compared with the ECD group, neonates in the PCD group had significantly higher gestational ages (standardized mean difference [SMD]: 2.20; 95% confidence interval [CI]: 1.25–3.15; P < .001), birth weights (SMD: 1.64; 95% CI: 1.00–2.27; P < .001), and Apgar scores at 1 minute (SMD: 0.51; 95% CI: 0.29–0.73; P < .001) and 5 minutes (SMD: 0.47; 95% CI: 0.25–0.70; P < .001). Additionally, the PCD group had significantly lower rates of neonatal intensive care unit admission (odds ratio [OR]: 0.21; 95% CI: 0.14–0.29; P < .001), low Apgar score at 5 minutes (OR: 0.27; 95% CI: 0.11–0.69; P = .01), and neonatal mortality (OR: 0.13; 95% CI: 0.05–0.33; P < .001). Conclusion: When pregnancies are complicated by abnormal placentation, PCD is linked to noticeably better neonatal outcomes than emergent delivery.

A portion of individuals diagnosed with primary central nervous system lymphomas (PCNSL) may experience early relapse or refractory (R/R) disease following treatment. This research explored the potential of MRI-based radiomics in forecasting R/R cases in PCNSL. Forty-six patients with pathologically confirmed PCNSL diagnosed between January 2008 and December 2020 were included in this study. Only patients who underwent pretreatment brain MRIs and complete postoperative follow-up MRIs were included. Pretreatment contrast-enhanced T1WI, T2WI, and T2 FLAIR imaging were analyzed. A total of 107 radiomic features, including 14 shape-based, 18 first-order statistical, and 75 texture features, were extracted from each sequence. Predictive models were then built using five different machine learning algorithms to predict R/R in PCNSL. Of the included 46 PCNSL patients, 20 (20/46, 43.5%) patients were found to have R/R. In the R/R group, the median scores in predictive models such as support vector machine, k-nearest neighbors, linear discriminant analysis, naïve Bayes, and decision trees were significantly higher, while the apparent diffusion coefficient values were notably lower compared to those without R/R (p < 0.05). The support vector machine model exhibited the highest performance, achieving an overall prediction accuracy of 83%, a precision rate of 80%, and an AUC of 0.78. Additionally, when analyzing tumor progression, patients with elevated support vector machine and naïve Bayes scores demonstrated a significantly reduced progression-free survival (p < 0.05). These findings suggest that preoperative MRI-based radiomics may provide critical insights for treatment strategies in PCNSL.