Periprocedural risk factors for incomplete radiofrequency ablation of liver metastases from colorectal cancer: a single-center retrospective analysis.

OBJECTIVE: To explore independent risk factors for incomplete radiofrequency ablation (iRFA) of colorectal cancer liver metastases (CRLM) and evaluate adverse outcomes following iRFA. MATERIALS AND METHODS: Magnetic resonance imaging data of CRLM patients who received percutaneous RFA were randomized into training (70%) and validation set 1 (30%) data sets. An independent validation set 2 was derived from computed tomography scans. Uni- and multivariate analyses identified independent risk factors for iRFA. Area under the curve (AUC) values were used to evaluate the predictive model performance. Risk points were assigned to independent predictors, and iRFA was predicted according to the total risk score. Kaplan-Meier curves were used to assess new intrahepatic metastases (NIHM), unablated tumor progression, and overall survival (OS). RESULTS: Multivariate regression determined as independent iRFA risk factors perivascular tumor location, subcapsular tumor location, tumor size ≥20 mm, and minimal ablative margin ≤5 mm. The AUC values of the model in the training set, validation set 1, and validation set 2 were 0.867, 0.772, and 0.820, respectively. The respective AUC values of the total risk score were 0.864, 0.768, and 0.817. During the 6-year follow-up, the cumulative OS was significantly shorter in the iRFA than in the complete RFA group, and NIHM (hazard ratio [HR] = 2.79; 95% confidence interval [CI]: 1.725, 4.513) and unablated tumor progression (HR = 3.473; 95% CI: 1.506, 8.007) were more severe. CONCLUSIONS: Perivascular tumor location, subcapsular tumor location, tumor size ≥20 mm, and minimal ablative margin ≤5 mm were independent risk factors for iRFA. iRFA may be a potential predictor of NIHM, unablated tumor progression, and OS.

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models.

Background: This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods: In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results: A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions: A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

Self-Stable Precipitation Polymerization Molecular Entanglement Effect and Molecular Weight Simulations and Experiments.

In this paper, we developed a reactive molecular dynamics (RMD) scheme to simulate the Self-Stable Precipitation (SP) polymerization of 1-pentene and cyclopentene (C5) with maleic anhydride (MAn) in an all-atom resolution. We studied the chain propagation mechanism by tracking the changes in molecular conformation and analyzing end-to-end distance and radius of gyration. The results show that the main reason of chain termination in the reaction process was due to intramolecular cyclic entanglement, which made the active center wrapped in the center of the globular chain. After conducting the experiment in the same condition with the simulation, we found that the distribution trend and peak value of the molecular-weight-distribution curve in the simulation were consistent with experimental results. The simulated number average molecular weight (Mn) and weight average molecular weight (Mw) were in good agreement with the experiment. Moreover, the simulated molecular polydispersity index (PDI) for cyclopentene reaction with maleic anhydride was accurate, differing by 0.04 from the experimental value. These show that this model is suitable for C5-maleic anhydride self-stable precipitation polymerization and is expected to be used as a molecular weight prediction tool for other maleic anhydride self-stable precipitation polymerization system.

Comparison of Percutaneous Radiofrequency Ablation for Subcapsular and Non-Subcapsular Colorectal Cancer Liver Metastases.

PURPOSE: To evaluate the efficacy and safety of percutaneous radiofrequency ablation (RFA) for subcapsular colorectal cancer liver metastases (CLMs). MATERIALS AND METHODS: With the approval of the Institutional Review Board, the clinical data of CLM patients who underwent percutaneous RFA for the first time from August 2010 to August 2020 were continuously collected. All CLMs were divided into subcapsular and non-capsular groups. Baseline characteristic data, technical effectiveness, minimal ablative margin, complications, local tumor progression (LTP), and overall survival (OS) between the two groups were analyzed using the t-test or chi-square test. A Cox regression model was used to evaluate the prognostic factors of LTP. RESULTS: One hundred and ninety-nine patients (124 males; mean age, 60.2 years) with 402 CLMs (221 subcapsular; mean size, 16.0 mm) were enrolled in the study. Technical effectiveness was achieved in 93.5% (376/402) of CLMs, with a major complication rate of 5.5%. Compared with non-subcapsular tumors, the minimal ablative margin achieved in subcapsular CLM was smaller (χ2 = -8.047, P < 0.001). With a median follow-up time of 23 months (range, 3-96 months), 37.1% of the tumors had LTP. The estimated cumulative OS at 1, 3, and 5 years was 96.1%, 66.0%, and 44.2%, respectively. There were no statistically significant differences between the two groups in terms of technical effectiveness (χ2 = 0.484, P = 0.487), major complications (χ2 = 0.082, P = 0.775), local tumor progression-free survival (LTPFS) (χ2 = 0.881, P = 0.348), and OS (χ2 = 2.874, P = 0.090). Minimal ablative margin, tumor size (≥20 mm), and technical effectiveness were predictors of LTP (all P < 0.05). CONCLUSION: RFA is a safe and effective technique for local tumor control of subcapsular CLMs.

A clinical-radiomics model incorporating T2-weighted and diffusion-weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer.

PURPOSE: Lymphovascular invasion (LVI) and perineural invasion (PNI) are independent prognostic factors in patients with colorectal cancer (CRC). In this study, we aimed to develop and validate a preoperative predictive model based on high-throughput radiomic features and clinical factors for accurate prediction of LVI/PNI in these patients. METHODS: Two hundred and sixty-three patients who underwent colorectal resection for histologically confirmed CRC between 1 February 2011 and 30 June 2020 were retrospectively enrolled. Between 1 February 2011 and 30 September 2018, 213 patients were randomly divided into a training cohort (n = 149) and a validation cohort (n = 64) by a ratio of 7:3. We used a 10000-iteration bootstrap analysis to estimate the prediction error and confidence interval for two cohorts. The independent test cohort consisted of 50 patients between 1 October 2018 and 30 June 2020. Regions of interest (ROIs) were manually delineated in high-resolution T2-weighted and diffusion-weighted images using ITK-SNAP software on each CRC tumor slice. In total, 3356 radiomic features were extracted from each ROI. Next, we used the maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms to select the strongest of these features to establish a clinical-radiomics model for predicting LVI/PNI. Receiver-operating characteristic and calibration curves were then plotted to evaluate the predictive performance of the model in the training, validation, and independent test cohorts. RESULTS: A multiparametric clinical-radiomics model combining MRI-reported extramural vascular invasion (EMVI) status and a Radiomics score for the LVI/PNI estimation was established. This model had significant predictive power in the training cohort (area under the curve [AUC] 0.91; 95% confidence interval [CI]: 0.85-0.97), validation cohort (AUC: 0.88; 95% CI: 0.79-89), and independent test cohorts (AUC 0.83, 95% CI 0.72-0.95). The model performed well in the independent test cohort with sensitivity of 0.818, specificity of 0.714, and accuracy of 0.760. Calibration curve and decision curve analysis demonstrated clinical benefits. CONCLUSION: Multiparametric clinical-radiomics models can accurately predict LVI/PNI in patients with CRC. Our model has predictive ability that should improve preoperative diagnostic performance and allow more individualized treatment decisions.

Metformin ameliorates the progression of atherosclerosis via suppressing macrophage infiltration and inflammatory responses in rabbits.

AIMS: The present study aimed to investigate the possible effects of metformin on the progression of atherosclerosis in a rabbit model. MAIN METHODS: Rabbits were randomly divided into three groups (n = 10): the control (Ctrl) group (fed with a chow diet), and two experimental groups, the AS group and the Met group (both received an atherogenic diet). After 2 weeks of acclimatization, the rabbits in the AS and Met groups were given a placebo and metformin, respectively, daily by gavage for 10 weeks. Plasma lipids and inflammatory cytokines were measured. The aorta was isolated for histological and immunohistochemical analysis. In vitro, human umbilical vein endothelial cells (HUVECs) were treated with metformin, and monocyte adhesion and adhesion molecule expression were measured. KEY FINDINGS: Metformin reduced plasma inflammatory cytokine levels but did not alter lipid content. Compared with that in the AS group, the atherosclerosis burden in the Met group was significantly decreased. The lesional macrophage content was reduced, but the lesional collagen content was not affected in the metformin-treated rabbits, compared with the corresponding levels in the non-treated controls. Furthermore, the aortic mRNA expression levels of adhesion molecules and inflammatory cytokines in the Met group were also significantly reduced compared with those in the AS group. Metformin treatment reduced monocyte adhesion to endothelial cells (ECs) and adhesion molecule expression, and inhibited rabbit monocyte differentiation into macrophages and the macrophage inflammatory response. SIGNIFICANCE: Our results suggest that metformin impeded the progression of atherosclerosis, possibly by suppressing macrophage infiltration and inflammatory responses.

Resveratrol suppresses lipoprotein-associated phospholipase A2 expression by reducing oxidative stress in macrophages and animal models.

SCOPE: Resveratrol is a naturally occurring polyphenolic compound with known cardioprotective, anti-inflammatory, and antioxidant properties. Lipoprotein-associated phospholipase A2 (Lp-PLA2 ) is associated with the risk of cardiovascular disease. Here, we investigated the effects of resveratrol on Lp-PLA2 expression in vitro and in vivo and explored the underlying mechanisms. METHODS AND RESULTS: Human monocytic cells (THP-1) were induced to differentiate into macrophages for an in vitro experimental model. Resveratrol suppressed Lp-PLA2 expression and reduced inflammation; lipopolysaccharide (LPS, 1 µg/mL), tumor necrosis factor-α (TNF-α, 10 ng/mL) and reactive oxygen species (ROS) were employed to stimulate an increase in Lp-PLA2 expression and ROS levels, and the stimulation was inhibited by resveratrol (50 µM) and other antioxidants. The inhibition of resveratrol was inversed partially by sirtuin 1 (SIRT1) inhibitors (Nicotinamide, 1-10 mM) (p<0.05). Next, a chronic inflammation mouse model induced by a HFD (high fat diet) supplemented with resveratrol 100 mg/kg/day orally for 12 weeks, resulted in resveratrol-induced decreases in the Lp-PLA2 levels in the plasma and liver and increases in the superoxide dismutase 2 (SOD2) expression in the liver (p<0.05). CONCLUSION: Based on our results, the protective effects of resveratrol on cardiovascular events may be related to its ability to suppress Lp-PLA2 expression.

Evaluate the efficacy of minimum attenuation value in differentiation of adrenal adenomas from nonadenomas on unenhanced CT.

Most adrenal masses are adenomas which contain a large amount of intracytoplasmic lipid and have lower attenuation values on unenhanced computed tomography (CT) examinations. Mean atrioventricular and CT histogram analysis is commonly used for the diagnosis of adenomas; however, the former disregards tissue heterogeneities of the mass and diagnostic efficiency is decreased, and the latter demands specific post-processing workstation for analyzing the data. The purpose of our study is to develop a simple and sensitive method for the diagnosis of adenomas on unenhanced CT.