Construction of 3D and 2D contrast-enhanced CT radiomics for prediction of CGB3 expression level and clinical prognosis in bladder cancer.

Objective: The purpose of this study was to construct a 3D and 2D contrast-enhanced computed tomography (CECT) radiomics model to predict CGB3 levels and assess its prognostic abilities in bladder cancer (Bca) patients. Methods: Transcriptome data and CECT images of Bca patients were downloaded from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database. Clinical data of 43 cases from TCGA and TCIA were used for radiomics model evaluation. The Volume of interest (VOI) (3D) and region of interest (ROI) (2D) radiomics features were extracted. For the construction of predicting radiomics models, least absolute shrinkage and selection operator regression were used, and the filtered radiomics features were fitted using the logistic regression algorithm (LR). The model's effectiveness was measured using 10-fold cross-validation and the area under the receiver operating characteristic curve (AUC of ROC). Result: CGB3 was a differential expressed prognosis-related gene and involved in the immune response process of plasma cells and T cell gamma delta. The high levels of CGB3 are a risk element for overall survival (OS). The AUCs of VOI and ROI radiomics models in the training set were 0.841 and 0.776, while in the validation set were 0.815 and 0.754, respectively. The Delong test revealed that the AUCs of the two models were not statistically different, and both models had good predictive performance. Conclusion: The CGB3 expression level is an important prognosis factor for Bca patients. Both 3D and 2D CECT radiomics are effective in predicting CGB3 expression levels.

Differential diagnosis of parotid gland tumours: Application of SWI combined with DWI and DCE-MRI.

BACKGROUND: Parotid tumours (PTs) have a variety of pathological types, and the surgical procedures differ depending on the tumour type. However, accurate diagnosis of PTs from the current preoperative examinations is unsatisfactory. METHODS: This retrospective study was approved by the Ethics Committee of our hospital, and the requirement for informed consent was waived. A total of 73 patients with PTs, including 55 benign and 18 malignant tumours confirmed by surgical pathology, were enrolled. All patients underwent diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), susceptibility-weighted imaging (SWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI). The signal uniformity and capsule on T2WI, apparent diffusion coefficient (ADC) derived from DWI, semi-quantitative parameter time-intensity curve (TIC) pattern, and quantitative parameters including transfer constant (Ktrans), extravascular extracellular volume fraction (Ve), wash-out constant (Kep) calculated from DCE-MRI, and intratumoural susceptibility signal (ITSS) obtained from SWI were assessed and compared between benign and malignant PTs. Logistic regression analysis was used to select the predictive parameters for the classification of benign and malignant parotid gland tumours, and receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic performance. RESULTS: Malignant PTs tended to exhibit a type C TIC pattern, whereas benign tumours tended to be type A and B (p < 0.001). Benign PTs had less ITSS than malignant tumours (p < 0.001). Multivariate analyses showed that ADC, Ve, and ITSS were predictors of tumour classification. ROC analysis showed that the area under the curve (AUC) of ADC, Ve, ITSS, and ADC combined with Ve were 0.623, 0.615, 0.826, and 0.782, respectively, in differentiating between malignant and benign PTs. When ITSS was added, the AUCs of ADC, Ve, and ADC combined with Ve increased to 0.882, 0.848, and 0.930, respectively. CONCLUSION: SWI offers incremental diagnostic value to DWI and DCE-MRI in the characterisation of parotid gland tumours.

Serum B-cell activating factor and lung ultrasound B-lines in connective tissue disease related interstitial lung disease.

Objective: To investigate the role of serum B-cell activating factor (BAFF) and lung ultrasound (LUS) B-lines in connective tissue disease related interstitial lung disease (CTD-ILD), and their association with different ILD patterns on high resolution computed tomography (HRCT) of chest. Methods: We measured the levels of BAFF and KL-6 by ELISA in the sera of 63 CTD-ILD patients [26 with fibrotic ILD (F-ILD), 37 with non-fibrotic ILD (NF-ILD)], 30 CTD patients without ILD, and 26 healthy controls. All patients underwent chest HRCT and LUS examination. Results: Serum BAFF levels were significantly higher in CTD patients compared to healthy subjects (617.6 ± 288.1 pg/ml vs. 269.0 ± 60.4 pg/ml, p < 0.01). BAFF concentrations were significantly different between ILD group and non-ILD group (698.3 ± 627.4 pg/ml vs. 448.3 ± 188.6 pg/ml, p < 0.01). In patients with ILD, BAFF concentrations were significantly correlated with B-lines number (r = 0.37, 95% CI 0.13-0.56, p < 0.01), KL-6 level (r = 0.26, 95% CI 0.01-0.48, p < 0.05), and Warrick score (r = 0.33, 95% CI 0.09-0.53, p < 0.01), although all correlations were only low to moderate. B-lines number correlated with Warrick score (r = 0.65, 95% CI 0.48-0.78, p < 0.01), and KL-6 levels (r = 0.43, 95% CI 0.21-0.61, p < 0.01). Patients with F-ILD had higher serum BAFF concentrations (957.5 ± 811.0 pg/ml vs. 516.1 ± 357.5 pg/ml, p < 0.05), KL-6 levels (750.7 ± 759.0 U/ml vs. 432.5 ± 277.5 U/ml, p < 0.05), B-lines numbers (174.1 ± 82 vs. 52.3 ± 57.5, p < 0.01), and Warrick score (19.9 ± 4.6 vs. 13.6 ± 3.4, p < 0.01) vs. NF-ILD patients. The best cut-off values to separate F-ILD from NF-ILD using ROC curves were 408 pg/ml for BAFF (AUC = 0.73, p < 0.01), 367 U/ml for KL-6 (AUC = 0.72, p < 0.05), 122 for B-lines number (AUC = 0.89, p < 0.01), and 14 for Warrick score (AUC = 0.87, p < 0.01) respectively. Conclusion: Serum BAFF levels and LUS B-lines number could be useful supportive biomarkers for detecting and evaluating the severity and/or subsets of CTD-ILD. If corroborated, combining imaging, serological, and sonographic biomarkers might be beneficial and comprehensive in management of CTD-ILD.