In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the X-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of low-dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio, leading to strong noise and artifacts that down-grade the CT image quality. In this paper, we propose a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the LDCT image quality. Specifically, we incorporate 3-D volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve the structural and textural information in reference to the normal-dose CT images and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information and outperforms competing methods.

Background Tumor-associated macrophages are mainly polarized into the M2 phenotype, remodeling the tumor microenvironment and promoting tumor progression by secreting various cytokines. Methods Tissue microarray consisting of prostate cancer (PCa), normal prostate, and lymph node metastatic samples from patients with PCa were stained with Yin Yang 1 (YY1) and CD163. Transgenic mice overexpressing YY1 were constructed to observe PCa tumorigenesis. Furthermore, in vivo and in vitro experiments, including CRISPR-Cas9 knock-out, RNA sequencing, chromatin immunoprecipitation (ChIP) sequencing, and liquid–liquid phase separation (LLPS) assays, were performed to investigate the role and mechanism of YY1 in M2 macrophages and PCa tumor microenvironment. Results YY1 was highly expressed in M2 macrophages in PCa and was associated with poorer clinical outcomes. The proportion of tumor-infiltrated M2 macrophages increased in transgenic mice overexpressing YY1. In contrast, the proliferation and activity of anti-tumoral T lymphocytes were suppressed. Treatment targeting YY1 on M2 macrophages using an M2-targeting peptide-modified liposome carrier suppressed PCa cell lung metastasis and generated synergistic anti-tumoral effects with PD-1 blockade. IL-4/STAT6 pathway regulated YY1, and YY1 increased the macrophage-induced PCa progression by upregulating IL-6. Furthermore, by conducting H3K27ac-ChIP-seq in M2 macrophages and THP-1, we found that thousands of enhancers were gained during M2 macrophage polarization, and these M2-specific enhancers were enriched in YY1 ChIP-seq signals. In addition, an M2-specific IL-6 enhancer upregulated IL-6 expression through long-range chromatin interaction with IL-6 promoter in M2 macrophages. During M2 macrophage polarization, YY1 formed an LLPS, in which p300, p65, and CEBPB acted as transcriptional cofactors. Conclusions Phase separation of the YY1 complex in M2 macrophages upregulated IL-6 by promoting IL-6 enhancer–promoter interactions, thereby increasing PCa progression.

Polymeric nanoparticles have been widely used as carriers of drugs and bioimaging agents due to their excellent biocompatibility, biodegradability, and structural versatility. The principal application of polymeric nanoparticles in medicine is for cancer therapy, with increased tumor accumulation, precision delivery of anticancer drugs to target sites, higher solubility of pharmaceutical properties and lower systemic toxicity. Recently, the stimuli-responsive polymeric nanoplatforms attracted more and more attention because they can change their physicochemical properties responding to the stimuli conditions, such as low pH, enzyme, redox agents, hypoxia, light, temperature, magnetic field, ultrasound, and so on. Moreover, the unique properties of stimuli-responsive polymeric nanocarriers in target tissues may significantly improve the bioactivity of delivered agents for cancer treatment. This review introduces stimuli-responsive polymeric nanoparticles and their applications in tumor theranostics with the loading of chemical drugs, nucleic drugs and imaging molecules. In addition, we discuss the strategy for designing multifunctional polymeric nanocarriers and provide the perspective for the clinical applications of these stimuli-responsive polymeric nanoplatforms.