A pair-wise meta-analysis highlights circular RNAs as potential biomarkers for colorectal cancer.

BACKGROUND: Circular RNAs (circRNAs) have emerged as a special subset of endogenous RNAs that are implicated in tumorigenesis and cancer progression. Herein we aim to carry out a meta-analysis to evaluate the clinicopathologic, diagnostic and prognostic significance of circRNA expression in colorectal cancer (CRC). METHODS: A systematic search of online databases was performed for original articles published in English, which investigated the diagnostic accuracy, prognostic utility, and clinicopathologic association of circRNA(s) in CRC. Data were strictly extracted and study bias was judged using the Quality Assessment for Studies of Diagnostic Accuracy II (QUADAS II) and Newcastle-Ottawa Scale (NOS) checklists. RESULTS: A total of 13 studies, involving 1430 patients with CRC, were included in the meta-analysis. The clinicopathologic study showed that abnormally expressed circRNAs were correlated with tumor diameter (P = 0.0350), differentiation (P = 0.0038), lymphatic metastasis (P = 0.0119), distant metastasis (P < 0.0001), TNM stage (P = 0.0002), and depth of invasion (P = 0.001) in patients with CRC. The summary area under the curve (AUC) of circRNA for the discriminative efficacy between patients with and without CRC was estimated to be 0.79, corresponding to a weighted sensitivity of 0.77 [95% confidence interval (CI): 0.74-0.79], specificity of 0.67 (95%CI: 0.64-0.70), and diagnostic odds ratio (DOR) of 7.52 (95%CI: 4.66-12.12). Survival analysis showed that highly expressed circRNAs were correlated with significantly worse overall survival (OS) [hazard ratio (HR) = 2.66, 95%CI: 2.03-3.50, P = 0.000; X2 = 4.34, P = 0.740, I2 = 0.0%], whereas lower expression of circRNAs was associated with prolonged OS (weighted HR = 0.30, 95%CI: 0.17-0.53, P = 0.000; X2 = 1.34, P = 0.909, I2 = 0.0%). Stratified analysis in circRNA expression status, and test matrix also showed robust results. CONCLUSION: Abnormally expressed circRNAs may be auxiliary biomarkers facilitating CRC diagnosis, and promising prognostic biomarkers in predicting the survival of CRC patients.

[Poisson Noise Removal Using Patch-order Resampling PCA Algorithm].

The problem of Poisson denoising is common in various photon-limited imaging applications, especialy in low-light imaging, astronomy and nuclear medical applications. Due to the smal sample problem and the related insufficient self-similarity between patches of whole image, many denoising algorithms cannot obtain the favorable denoising performance. We propose patch-order resampling PCA algorithm for Poisson noise reduction. Firstly, we use the patch-ordered operations to sort the extracted image patches and exploit the bootstrap resampling method to resample the different blocks of spectral images to obtain more data matrix of image samples. Then, we select the patches with largest weights corresponding to the vectors of image samples data matrix as the most similar patches. Finaly, we use principal component analysis algorithm for processing the image to obtain the final denoised image. Experiments results show that the proposed method achieves excelent Poisson noise removal performance in the photon-limited images with smal sample problems.

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.

OBJECTIVES: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. MATERIALS AND METHODS: The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. RESULTS: Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. CONCLUSION: To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. CRITICAL RELEVANCE STATEMENT: Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. KEY POINTS: Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.

Simulation-to-real generalization for deep-learning-based refraction-corrected ultrasound tomography image reconstruction.

Objective. The image reconstruction of ultrasound computed tomography is computationally expensive with conventional iterative methods. The fully learned direct deep learning reconstruction is promising to speed up image reconstruction significantly. However, for direct reconstruction from measurement data, due to the lack of real labeled data, the neural network is usually trained on a simulation dataset and shows poor performance on real data because of the simulation-to-real gap.Approach. To improve the simulation-to-real generalization of neural networks, a series of strategies are developed including a Fourier-transform-integrated neural network, measurement-domain data augmentation methods, and a self-supervised-learning-based patch-wise preprocessing neural network. Our strategies are evaluated on both the simulation dataset and real measurement datasets from two different prototype machines.Main results. The experimental results show that our deep learning methods help to improve the neural networks' robustness against noise and the generalizability to real measurement data.Significance. Our methods prove that it is possible for neural networks to achieve superior performance to traditional iterative reconstruction algorithms in imaging quality and allow for real-time 2D-image reconstruction. This study helps pave the path for the application of deep learning methods to practical ultrasound tomography image reconstruction based on simulation datasets.

Are We There Yet? The Value of Deep Learning in a Multicenter Setting for Response Prediction of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy.

This retrospective study aims to evaluate the generalizability of a promising state-of-the-art multitask deep learning (DL) model for predicting the response of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (nCRT) using a multicenter dataset. To this end, we retrained and validated a Siamese network with two U-Nets joined at multiple layers using pre- and post-therapeutic T2-weighted (T2w), diffusion-weighted (DW) images and apparent diffusion coefficient (ADC) maps of 83 LARC patients acquired under study conditions at four different medical centers. To assess the predictive performance of the model, the trained network was then applied to an external clinical routine dataset of 46 LARC patients imaged without study conditions. The training and test datasets differed significantly in terms of their composition, e.g., T-/N-staging, the time interval between initial staging/nCRT/re-staging and surgery, as well as with respect to acquisition parameters, such as resolution, echo/repetition time, flip angle and field strength. We found that even after dedicated data pre-processing, the predictive performance dropped significantly in this multicenter setting compared to a previously published single- or two-center setting. Testing the network on the external clinical routine dataset yielded an area under the receiver operating characteristic curve of 0.54 (95% confidence interval [CI]: 0.41, 0.65), when using only pre- and post-therapeutic T2w images as input, and 0.60 (95% CI: 0.48, 0.71), when using the combination of pre- and post-therapeutic T2w, DW images, and ADC maps as input. Our study highlights the importance of data quality and harmonization in clinical trials using machine learning. Only in a joint, cross-center effort, involving a multidisciplinary team can we generate large enough curated and annotated datasets and develop the necessary pre-processing pipelines for data harmonization to successfully apply DL models clinically.

Ultrasound transmission tomography image reconstruction with a fully convolutional neural network.

Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally demanding. To address this problem, we develop an efficient fully learned image reconstruction method based on a convolutional neural network. The image is reconstructed via one forward propagation of the network given input sensor data, which is much faster than the reconstruction using conventional iterative optimization methods. To transform the ultrasound measured data in the sensor domain into the reconstructed image in the image domain, we apply multiple down-scaling and up-scaling convolutional units to efficiently increase the number of hidden layers with a large receptive and projective field that can cover all elements in inputs and outputs, respectively. For dataset generation, a paraxial approximation forward model is used to simulate ultrasound measurement data. The neural network is trained with a dataset derived from natural images in ImageNet and tested with a dataset derived from medical images in OA-Breast Phantom dataset. Test results show the superior efficiency of the proposed neural network to other reconstruction algorithms including popular neural networks. When compared with conventional iterative optimization algorithms, our neural network can reconstruct a 110 × 86 image more than 20 times faster on a CPU and 1000 times faster on a GPU with comparable image quality and is also more robust to noise.

Texture Variation Adaptive Image Denoising With Nonlocal PCA.

Image textures, as a kind of local variations, provide important information for the human visual system. Many image textures, especially the small-scale or stochastic textures, are rich in high-frequency variations, and are difficult to be preserved. Current state-of-the-art denoising algorithms typically adopt a nonlocal approach consisting of image patch grouping and group-wise denoising filtering. To achieve a better image denoising while preserving the variations in texture, we first adaptively group high correlated image patches with the same kinds of texture elements (texels) via an adaptive clustering method. This adaptive clustering method is applied in an over-clustering-and-iterative-merging approach, where its noise robustness is improved with a custom merging threshold relating to the noise level and cluster size. For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures. Experiments on natural images show the superiority of the proposed transform-domain variation adaptive filtering to traditional PCA-based hard or soft threshold filtering. As a whole, the proposed denoising method achieves a favorable texture-preserving performance both quantitatively and visually, especially for irregular textures, which is further verified in camera raw image denoising.

Prognostic value of the Glasgow prognostic score in colorectal cancer: a meta-analysis of 9,839 patients.

PURPOSE: The aim of this study was to perform a systematic review and meta-analysis to evaluate the value of the Glasgow prognostic score (GPS) or modified Glasgow prognostic score (mGPS) in patients with colorectal cancer (CRC). METHODS: A comprehensive medical literature search was performed using the online databases PubMed, Embase, Web of Science, and the Cochrane Library. After extracting basic characteristics and prognostic data from the included studies, overall survival (OS) and cancer-specific survival (CSS) were pooled as primary outcomes. Subgroup analyses were performed according to therapeutic strategies, models, cutoff values, regions, tumor, node, metastasis stages, sample size, and ages. RESULTS: Forty-three independent cohorts from 41 studies with 9,839 CRC patients were included in the present study. Correlation between GPS or mGPS and OS was analyzed in 32 cohorts of 7,714 patients, and 23 independent cohorts of 5,375 patients focused on the correlation between GPS or mGPS and CSS. The overall outcomes showed that patients with elevated GPS or mGPS were associated with poor OS (HR: 2.20, 95% CI: 1.88-2.57, P<0.001). Elevated GPS or mGPS also resulted in worse CSS (HR: 1.86, 95% CI: 1.59-2.17, P<0.001). The results of the subgroup analyses confirmed the overall outcomes. CONCLUSION: GPS or mGPS is an accurate prognostic predictor in patients with CRC. Patients with elevated pretreatment GPS or mGPS have a poor prognosis. Subgroup analyses confirmed the overall outcomes. Pretreatment GPS is a useful biomarker in the management of CRC.

Serine protease HtrA1 as an inhibitor on proliferation invasion and migration of gastric cancer.

HtrA1, as serine protease lower expressed in various human solid tumors, can down-regulate cell growth and proliferation. In this study, we focus on whether overexpressed HtrA1 can inhibit the growth of gastric cancer in vitro. This study found the HtrA1 is lower expressed in gastric cancer tissue than in normal gastric tissue. When HtrA1 is highly expressing with recombinant plasmid in gastric cancer cell lines SGC-7901 and AGS, it weakened cell proliferation, invasion, and migration in vitro. These data suggested that HtrA1 as an inhibitor in gastric cancer cells resulted in anti-proliferation, reduced invasion, decreased migration, and suppressed growth and may be an effective molecular targets on gastric cancer treatment.

Targeted silencing of CXCL1 by siRNA inhibits tumor growth and apoptosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is an aggressive malignancy and a major cause of cancer-related mortality worldwide. Our previous study shows that chemokine (C-X-C motif) ligand 1 (CXCL1) was upregulated and CXCR1 was downregulated in tumor tissues as compared to peritumor tissues by chemotaxis assay. As the status of CXCL subgroups and their receptors affect progression of HCC, we evaluated potential mechanisms of CXCL1 associated with anticancer effects in HCC based on our previous study. The effects of targeting CXCL1 by RNA interference (RNAi) on the proliferation and apoptosis of CBRH-7919 cells were observed in vitro and in vivo. Additionally, whether CXCL1 knockdown significantly reduce the activity of STAT3, NF-κB and HIF-1 or not were also estimated. RNAi of CXCL1 in the CBRH-7919 cells decreased the growth of tumors in nude mice by inhibited cells proliferation and induced apoptosis. In conclusion, these findings suggest that CXCL1 plays critical roles in the growth and apoptosis of HCC. RNAi of CXCL1 inhibits the growth and apoptosis of tumor cells, which indicates that CXCL1 may be a potential molecular target for use in HCC therapy.