Background: The Vesical Imaging-Reporting and Data System (VI-RADS) was created in 2018, and a 5-point VI-RADS scoring system was proposed to determine whether the muscularis of the bladder has been infiltrated by tumor tissues. Purpose: To verify the accuracy of the VI-RADS scoring system in predicting muscle-invasive bladder cancer and to explore its value in clinical application. Materials and methods: A total of 220 patients with bladder cancer who underwent multiparameter magnetic resonance imaging from January 2017 to June 2019 were selected. Then, two radiologists with equivalent qualifications gave their diagnoses of bladder tumors on T2-weighted imaging, diffusion-weighted imaging and dynamic contrast enhanced imaging. Meanwhile, the bladder tumor was also scored on the basis of the VI-RADS system; for multifocal tumors, the highest tumor load was selected for scoring. Furthermore, the final pathological results of the patients were unknown during the imaging diagnosis and scoring. Next, the VI-RADS score was compared with the pathological results after surgery, and the ability of the VI-RADS score to assess the degree of muscularis infiltration was finally analyzed. Results: A total of 220 patients were included in our study, including 194 males and 26 females. Among them, the pathological results were 113 cases of muscle-invasive bladder cancer and 107 cases of non-muscle-invasive bladder cancer. The results showed that there was a positive correlation between the pathological results and VI-RADS score (r = 0.821, P < 0.05). The area under the receiver operating characteristic curve of the VI-RADS score was 0.960 (95% CI: 0.937, 0.983). When the VI-RADS score was above 3, the sensitivity, specificity and accuracy of predicting muscle-invasive bladder cancer were 82.3, 95.3 and 88.64%, respectively. Conclusion: The VI-RADS scoring system has good diagnostic value in predicting the degree of tumor invasion and can be used to guide clinical decision-making and management.

Objective: Precise classification of mass-forming intrahepatic cholangiocarcinoma (MF-ICC) and hepatocellular carcinoma (HCC) based on magnetic resonance imaging (MRI) is crucial for personalized treatment strategy. The purpose of the present study was to differentiate MF-ICC from HCC applying a novel deep-learning-based workflow with stronger feature extraction ability and fusion capability to improve the classification performance of deep learning on small datasets. Methods: To retain more effective lesion features, we propose a preprocessing method called semi-segmented preprocessing (Semi-SP) to select the region of interest (ROI). Then, the ROIs were sent to the strided feature fusion residual network (SFFNet) for training and classification. The SFFNet model is composed of three parts: the multilayer feature fusion module (MFF) was proposed to extract discriminative features of MF-ICC/HCC and integrate features of different levels; a new stationary residual block (SRB) was proposed to solve the problem of information loss and network instability during training; the attention mechanism convolutional block attention module (CBAM) was adopted in the middle layer of the network to extract the correlation of multi-spatial feature information, so as to filter the irrelevant feature information in pixels. Results: The SFFNet model achieved an overall accuracy of 92.26% and an AUC of 0.9680, with high sensitivity (86.21%) and specificity (94.70%) for MF-ICC. Conclusion: In this paper, we proposed a specifically designed Semi-SP method and SFFNet model to differentiate MF-ICC from HCC. This workflow achieves good MF-ICC/HCC classification performance due to stronger feature extraction and fusion capabilities, which provide complementary information for personalized treatment strategy.

Background: Multi-parametric magnetic resonance imaging (MP-MRI) may provide comprehensive information for graded diagnosis of bladder cancer (BCa). Nevertheless, existing methods ignore the complex correlation between these MRI sequences, failing to provide adequate information. Therefore, the main objective of this study is to enhance feature fusion and extract comprehensive features from MP-MRI using deep learning methods to achieve an accurate diagnosis of BCa grading.Methods: In this study, a self-attention-based MP-MRI feature fusion framework (SMMF) is proposed to enhance the performance of the model by extracting and fusing features of both T2-weighted imaging (T2WI) and dynamic contrast-enhanced imaging (DCE) sequences. A new multiscale attention (MA) model is designed to embed into the neural network (CNN) end to further extract rich features from T2WI and DCE. Finally, a self-attention feature fusion strategy (SAFF) was used to effectively capture and fuse the common and complementary features of patients’ MP-MRIs.Results: In a clinically collected sample of 138 BCa patients, the SMMF network demonstrated superior performance compared to the existing deep learning-based bladder cancer grading model, with accuracy, F1 value, and AUC values of 0.9488, 0.9426, and 0.9459, respectively.Conclusion: Our proposed SMMF framework combined with MP-MRI information can accurately predict the pathological grading of BCa and can better assist physicians in diagnosing BCa.

Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion and background information via the Prior Normalization Fusion (PNF) strategy and the Feature Difference Guidance (FDG) module, to direct the model to concentrate on beneficial regions for diagnosis. Extensive experiments in the clinical dataset demonstrate that the proposed method achieves promising diagnosis performance: PDGNets based on conventional networks record an ACC (Accuracy) and AUC (Area Under the Curve) of 87.50% and 89.98%, marking improvements of 8.19% and 7.64% over the prior-free benchmark. Compared to lesion-focused benchmarks, the uplift is 6.14% and 6.02%. PDGNets based on advanced networks reach an ACC and AUC of 89.77% and 92.80%. The study underscores the potential of harnessing background information in medical image diagnosis, suggesting a more holistic view for future research.