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Purpose: The objective of this study was to conduct a meta-analysis comparing the diagnostic efficacy of models based on diffusion-weighted imaging (DWI)-MRI, dynamic contrast enhancement (DCE)-MRI, and combination models (DCE and DWI) in distinguishing benign from malignant non-mass enhancement (NME) breast lesions. Materials and methods: PubMed, Embase, and Cochrane Library were searched, from inception to January 30, 2023, for studies that used DCE or DWI-MRI for the prediction of NME breast cancer patients. A bivariate random-effects model was used to calculate the meta-analytic sensitivity, specificity, and area under the curve (AUC) of the DCE, DWI, and combination models. Subgroup analysis and meta-regression analysis were performed to find the source of heterogeneity. Results: Of the 838 articles screened, 18 were eligible for analysis (13 on DCE, five on DWI, and four studies reporting the diagnostic accuracy of both DCE and DWI). The funnel plot showed no publication bias (p > 0.5). The pooled sensitivity and specificity and the AUC of the DCE, DWI, and combination models were 0.58, 0.72, and 0.70, respectively; 0.84, 0.69, and 0.84, respectively; and 0.88, 0.79, 0.90, respectively. The meta-analysis found no evidence of a threshold effect and significant heterogeneity among trials in terms of DCE sensitivity and specificity, as well as DWI specificity alone (I² > 75%). The meta-regression revealed that different diagnostic criteria contributed to the DCE study's heterogeneity (p < 0.05). Different reference criteria significantly influenced the heterogeneity of the DWI model (p < 0.05). Subgroup analysis revealed that clustered ring enhancement (CRE) had the highest pooled specificity (0.92) among other DCE features. The apparent diffusion coefficient (ADC) with a mean threshold <1.3 x 10-3 mm²/s had a slightly higher sensitivity of 0.86 compared to 0.82 with an ADC of >1.3 x 10-3 mm²/s. Conclusion: The combination model (DCE and DWI) outperformed DCE or DWI alone in identifying benign and malignant NME lesions. The DCE-CRE feature was the most specific test for ruling in NME cancers. [ABSTRACT FROM AUTHOR]

Background: Non-muscle-invasive bladder cancer (NMIBC) is categorized into high and low grades with different clinical treatments and prognoses. Thus, accurate preoperative evaluation of the histologic NMIBC grade through imaging techniques is essential. Objectives: To develop and validate an MRI-based radiomics nomogram for individualized prediction of NMIBC grading. Methods: The study included 169 consecutive patients with NMIBC (training cohort: n = 118, validation cohort: n = 51). A total of 3148 radiomic features were extracted, and one-way analysis of variance and least absolute shrinkage and selection operator were used to select features for building the radiomics score (Rad-score). Three models to predict NMIBC grading were developed using logistic regression analysis: a clinical model, a radiomics model and a radiomics-clinical combined nomogram model. The discrimination and calibration power and clinical applicability of the models were evaluated. The diagnostic performance of each model was compared by determining the area under the curve (AUC) in receiver operating characteristic (ROC) curve analysis. Results: A total of 24 features were used to build the Rad-score. A clinical model, a radiomics model, and a radiomics-clinical nomogram model that incorporated the Rad-score, age, and number of tumors were constructed. The radiomics model and nomogram showed AUCs of 0.910 and 0.931 in the validation set, which outperformed the clinical model (0.745). The decision curve analysis also showed that the radiomics model and combined nomogram model yielded higher net benefits than the clinical model. Conclusion: A radiomics-clinical combined nomogram model has the potential to be used as a non-invasive tool for the differentiating low-from high-grade NMIBCs. [ABSTRACT FROM AUTHOR]

Purpose. This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods.,is retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results. In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases (P <0.05) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions.,eresults of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better. [ABSTRACT FROM AUTHOR]

Recently, lncRNA has been verified to regulate the development and progression of tumor. LncRNA ABHD11-AS1 has been proven to serve as an oncogene in several cancers. However, the role of ABHD11-AS1 in colorectal cancer remains totally unknown. In the present study, qRT-PCR assay revealed that ABHD11-AS1 expression was markedly higher in colorectal cancer tissues and cell lines. In addition, patients who displayed overexpression of ABHD11-AS1 showed a significantly poorer progression free survival (PFS) and overall survival (OS) by Kaplan–Meier analysis. Loss-of-function experiments suggested that silencing of ABHD11-AS1 expression could significantly reduce the proliferation, colony formation, migration and invasion of colorectal cancer cells, and increase cell apoptosis. Moreover, bioinformatics analysis, biotin pull-down assay, luciferase reporter assay, and RIP assay disclosed that ABHD11-AS1 straightly interacted with miR-133a. We also found that SOX4 was a downstream target of miR-133a and ABHD11-AS1 subsequently exerted its biological effects via modulating the expression of SOX4 in colorectal cancer cells. Collectively, these findings manifested that the ABHD11-AS1/miR-133a/SOX4 axis may be a cogitable and promising therapeutic target for colorectal cancer. [ABSTRACT FROM AUTHOR]

Fabrication of functional nanofibrous biomimetic scaffolds for tissue regeneration remains a challenge. This work demonstrates that functional nanofibrous scaffolds were fabricated from blends of polylactide with other functional polymers by a thermally induced phase separation (TIPS) technique, here exemplified by the fabrication of electroactive nanofibrous scaffolds from the blends of polylactide and an electroactive degradable tetraaniline-polylactide-tetraaniline (TPT) block copolymer by TIPS. The TPT copolymer was synthesized by coupling reaction between the carboxyl-capped tetraaniline and polylactide. The chemical structure, electroactivity, thermal properties and mechanical properties of TPT and polylactide/ TPT blend films were characterized. The copolymer blends were fabricated into electroactive nanofibrous scaffolds by TIPS. The effect of aniline tetramer content, polymer concentration and phase separation temperature on the diameter of nanofibers was investigated. The adhesion and proliferation of C2C12 myoblast cells and protein adsorption on the electroactive biodegradable substrates were evaluated, and the results show that the electroactive materials are nontoxic and could enhance the C2C12 cell proliferation without electrical stimulation, and adsorbed more proteins compared to polylactide. The electrical stimulation on the electroactive substrates significantly increased the cell proliferation of C2C12 myoblasts. This work opens the way to fabricate functional nanofibrous scaffolds from the blends of polylactide and other functional polymers by TIPS. [ABSTRACT FROM AUTHOR]