CILP (Cartilage intermediate layer protein), an ECM (extracellular matrix) glycoprotein, is found to be associated with intervertebral disc degeneration, chronic heart failure, obese and cardiac ...fibrosis. However, there are few reports on the role of CILP in tumors. Thus, in this study, we mainly explored the function of CILP in the occurrence and development of tumors and whether it could be a potential pan-cancer marker. Pan-cancer data in this study were obtained from UCSC Xena. Single-cell data were obtained from GSE152938. ROC (Receiver operating characteristic) curves were used to evaluate the accuracy of CILP in predicting the occurrence of different tumor types. The Kaplan-Meier plots were used to assess the relationship between CILP expression and survival prognosis in different tumor types by COX regression analysis. Pseudotime analysis and cell communication analysis were used to further explore the function of CILP at Single cell level. The human RCC (renal cell carcinoma) cell lines ACHN and 786-O were used for further experimental verification. Bulk RNA-seq showed differences in CILP expression in several tumors. ROC curves showed that 14 tumors have AUC > 0.7. Kaplan-Meier plots indicated that CILP is a risk factor for patients in 3 kinds of tumors. ScRNA-seq (Single cell RNA sequencing) suggested that CILP might influence tumors through fibroblasts and cell-cell communication. Finally, we verified the function of CILP at the cellular level by using RCC cell lines ACHN and 786-O and found that knockdown of CILP could significantly inhibit migration and invasion. This finding supports that CILP could be a risk factor as well as a pan-cancer predictor for patients.
This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic ...(CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.
Rejuvenation of metallic glasses (MGs) brings them in higher-energy states, which improves their mechanical properties. However, how to rejuvenate MGs through a fast and simple approach is still a ...challenge. In the present work, a nonuniform structure consisting of a center-relaxation region surrounded by an edge-gradient-rejuvenation framework was created in a Zr-based bulk metallic glass (BMG) by lateral elastostatic preloading. Such a nonuniform structure greatly suppresses the rapid propagation of shear bands and has the potential to overcome the inverted relationship between strength and plasticity. More interestingly, we found a stress-induced memory effect, viz., the recovery of relaxation enthalpy during elastostatic loading, which is quite different from the thermal-induced memory effect in previous works. This indicates a certain degree of equivalence between stress and temperature in the dynamics of MGs.
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Rejuvenation of a naturally aged Zr64.13Cu15.75Ni10.12Al10 bulk metallic glass (BMG) can be achieved by laterally elastostatic pre-loading. The serration flow of the rejuvenated BMG shows a bimodal ...fashion, in which smaller serrations and larger serrations emerge alternately. Both coarse and a large number of fine shear bands, with strong interactions among themselves, coexist in the rejuvenated BMG. More pronounced β relaxation of the rejuvenated BMG indicates that the bimodal patterns of serration flows and shear bands can be attributed to elastostatically induced a more heterogeneous structure.
•Shear band affected zones induce significant rejuvenation during thermal cycles.•Semi-in-situ microhardness test confirmed the contribution source of rejuvenation.•Founding an effective method to ...improve the plasticity of damaged metallic glass.
Cryogenic thermal cycling (CTC) treatment is a simple and effective technique to rejuvenate metallic glasses (MGs). However, how the structural heterogeneities of MGs are influenced by CTC is still a problem. In this study, the effects of CTC on mechanical properties and thermal responses of as-cast and as-deformed Zr-based bulk metallic glasses (BMGs) were investigated. Due to the existence of shear bands and their affected zones, the deformed BMG presents greater sensitivity to the CTC treatment, and its softening effect first increases and then saturates with the number of cycles.
Purpose
The main function of cartilage oligomeric matrix protein (COMP) is to maintain the synthesis and stability of the extracellular matrix by interacting with collagen. At present, there are ...relatively few studies on the role of this protein in tumors. This study aimed to explore the relationship between COMP and pan-cancer, and analyzed its diagnostic and prognostic value.
Methods
The Cancer Genome Atlas database, the Genotype-Tissue Expression database and the Cancer Cell Line Encyclopedia database was used for gene expression analysis. The receiver operating characteristic curve was used to assess the diagnostic value of COMP in pan-cancer. Kaplan–Meier plots were used to assess the relationship between COMP expression and prognosis of cancers. R software v4.1.1 was used for statistical analysis, and the ggplot2 package was used for visualization.
Results
COMP was significantly overexpressed in 15 human cancers and showed significantly difference in 12 molecular subtypes and 16 immune subtypes. In addition, the expression of COMP is associated with tumor immune evasion. The ROC curve showed that the expression of COMP could predict the occurrence of 16 kinds of tumors with relative accuracy, including adrenocortical carcinoma (ACC) (AUC = 0.737), breast invasive carcinoma (BRCA) (AUC = 0.896), colon adenocarcinoma (COAD) (AUC = 0.760), colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma (COADREAD) (AUC = 0.775), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) (AUC = 0.875), kidney renal papillary cell carcinoma (KIRP) (AUC = 0.773), kidney chromophobe (KICH) (AUC = 0.809), ovarian serous cystadenocarcinoma (OV) (AUC = 0.906), prostate adenocarcinoma (PRAD) (AUC = 0.721), pancreatic adenocarcinoma (PAAD) (AUC = 0.944), rectum adenocarcinoma (READ) (AUC = 0.792), skin cutaneous melanoma (SKCM) (AUC = 0.746), stomach adenocarcinoma (STAD) (AUC = 0.711), testicular germ cell tumors (TGCT) (AUC = 0.823), thymoma (THYM) (AUC = 0.777) and uterine carcinosarcoma (UCS) (AUC = 0.769). Furthermore, COMP expression was correlated with overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI) in ACC (OS, HR = 4.95, DSS, HR = 5.55, PFI, HR = 2.79), BLCA (OS, HR = 1.59, DSS, HR = 1.72, PFI, HR = 1.36), KIRC (OS, HR = 1.36, DSS, HR = 1.94, PFI, HR = 1.57) and COADREAD (OS, HR = 1.46, DSS, HR = 1.98, PFI, HR = 1.43). We selected previously unreported bladder urothelial carcinoma (BLCA) for further study and found that COMP could be an independent risk factor for OS, DSS and PFI. Moreover, we found differentially expressed genes of COMP in BLCA and obtained top 10 hub genes, including LGR4, LGR5, RSPO2, RSPO1, RSPO3, RNF43, ZNRF3, FYN, LYN and SYK. Finally, we verified the function of COMP at the cellular level by using J82 and T24 cells and found that knockdown of COMP could significantly inhibit migration and invasion. This finding supports that COMP could be a potential biomarker for pan-cancer diagnosis and prognosis encompassing tumor microenvironment, disease stage and prognosis.
Conclusion
This finding supports that COMP could be a potential biomarker for pan-cancer diagnosis and prognosis encompassing tumor microenvironment, disease stage and prognosis.
Objectives
While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains ...challenging. We developed and validated an AI system for COVID-19 detection on presenting CXR.
Methods
A deep learning model (RadGenX), trained on 168,850 CXRs, was validated on a large international test set of presenting CXRs of symptomatic patients from 9 study sites (US, Italy, and Hong Kong SAR) and 2 public datasets from the US and Europe. Performance was measured by area under the receiver operator characteristic curve (AUC). Bootstrapped simulations were performed to assess performance across a range of potential COVID-19 disease prevalence values (3.33 to 33.3%). Comparison against international radiologists was performed on an independent test set of 852 cases.
Results
RadGenX achieved an AUC of 0.89 on 4-fold cross-validation and an AUC of 0.79 (95%CI 0.78–0.80) on an independent test cohort of 5,894 patients. Delong’s test showed statistical differences in model performance across patients from different regions (
p
< 0.01), disease severity (
p
< 0.001), gender (
p
< 0.001), and age (
p
= 0.03). Prevalence simulations showed the negative predictive value increases from 86.1% at 33.3% prevalence, to greater than 98.5% at any prevalence below 4.5%. Compared with radiologists, McNemar’s test showed the model has higher sensitivity (
p
< 0.001) but lower specificity (
p
< 0.001).
Conclusion
An AI model that predicts COVID-19 infection on CXR in symptomatic patients was validated on a large international cohort providing valuable context on testing and performance expectations for AI systems that perform COVID-19 prediction on CXR.
Key Points
• An AI model developed using CXRs to detect COVID-19 was validated in a large multi-center cohort of 5,894 patients from 9 prospectively recruited sites and 2 public datasets.
• Differences in AI model performance were seen across region, disease severity, gender, and age.
• Prevalence simulations on the international test set demonstrate the model’s NPV is greater than 98.5% at any prevalence below 4.5%.
Background
Single‐shot diffusion‐weighted imaging (ssDWI) has been shown useful for detecting active bowel inflammation in Crohn's disease (CD) without MRI contrast. However, ssDWI suffers from ...geometric distortion and low spatial resolution.
Purpose
To compare conventional ssDWI with higher‐resolution ssDWI (HR‐ssDWI) and multi‐shot DWI based on multiplexed sensitivity encoding (MUSE‐DWI) for evaluating bowel inflammation in CD, using contrast‐enhanced MR imaging (CE‐MRI) as the reference standard.
Study Type
Prospective.
Subjects
Eighty nine patients with histological diagnosis of CD from previous endoscopy (55 male/34 female, age: 17–69 years).
Field Strength/Sequences
ssDWI (2.7 mm × 2.7 mm), HR‐ssDWI (1.8 mm × 1.8 mm), MUSE‐DWI (1.8 mm × 1.8 mm) based on echo‐planar imaging, T2‐weighted imaging, and CE‐MRI sequences, all at 1.5 T.
Assessment
Five raters independently evaluated the tissue texture conspicuity, geometry accuracy, minimization of artifacts, diagnostic confidence, and overall image quality using 5‐point Likert scales. The diagnostic performance (sensitivity, specificity and accuracy) of each DWI sequences was assessed on per‐bowel‐segment basis.
Statistical Tests
Inter‐rater agreement for qualitative evaluation of each parameter was measured by the intra‐class correlation coefficient (ICC). Paired Wilcoxon signed‐rank tests were performed to evaluate the statistical significance of differences in qualitative scoring between DWI sequences. A P value <0.05 was considered to be statistically significant.
Results
Tissue texture conspicuity, geometric distortions, and overall image quality were significantly better for MUSE‐DWI than for ssDWI and HR‐ssDWI with good agreement among five raters (ICC: 0.70–0.89). HR‐ssDWI showed significantly poorer performance to ssDWI and MUSE‐DWI for all qualitative scores and had the worst diagnostic performance (sensitivity of 57.0% and accuracy of 87.3%, with 36 undiagnosable cases due to severe artifacts). MUSE‐DWI showed significantly higher sensitivity (97.5% vs. 86.1%) and accuracy (98.9% vs. 95.1%) than ssDWI for detecting bowel inflammation.
Data Conclusion
MUSE‐DWI was advantageous in assessing bowel inflammation in CD, resulting in improved spatial resolution and image quality.
Level of Evidence
2
Technical Efficacy Stage
2
PURPOSE:To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR).
MATERIALS AND METHODS:In this retrospective study, a DL model was ...trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists’ performance and was assessed by area under the receiver operating characteristics (AUC).
RESULTS:The median age of the subjects in the test set was 61 (interquartile range39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test).
CONCLUSIONS:A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.
