Benign and malignant classification of breast tumor ultrasound images using conventional radiomics and transfer learning features: A multicenter retrospective study.

This study aims to establish an effective benign and malignant classification model for breast tumor ultrasound images by using conventional radiomics and transfer learning features. We collaborated with a local hospital and collected a base dataset (Dataset A) consisting of 1050 cases of single lesion 2D ultrasound images from patients, with a total of 593 benign and 357 malignant tumor cases. The experimental approach comprises three main parts: conventional radiomics, transfer learning, and feature fusion. Furthermore, we assessed the model's generalizability by utilizing multicenter data obtained from Datasets B and C. The results from conventional radiomics indicated that the SVM classifier achieved the highest balanced accuracy of 0.791, while XGBoost obtained the highest AUC of 0.854. For transfer learning, we extracted deep features from ResNet50, Inception-v3, DenseNet121, MNASNet, and MobileNet. Among these models, MNASNet, with 640-dimensional deep features, yielded the optimal performance, with a balanced accuracy of 0.866, AUC of 0.937, sensitivity of 0.819, and specificity of 0.913. In the feature fusion phase, we trained SVM, ExtraTrees, XGBoost, and LightGBM with early fusion features and evaluated them with weighted voting. This approach achieved the highest balanced accuracy of 0.964 and AUC of 0.981. Combining conventional radiomics and transfer learning features demonstrated clear advantages over using individual features for breast tumor ultrasound image classification. This automated diagnostic model can ease patient burden and provide additional diagnostic support to radiologists. The performance of this model encourages future prospective research in this domain.

Bibliometric and visualized analysis of reporting and data systems from 2000 to 2022: research situation, global trends, and hotspots.

Background: The reporting and data system (RADS) has been researched across the world since it was first developed. This study used bibliometrics to analyze the research trends and current status of this field over the past almost 23 years and explored possible future research hotspots. Methods: We searched the Web of Science (WOS) literature on RADSs from January 1, 2000, to November 1, 2022, and evaluated the findings visually with VOSviewer (1.6.18), CiteSpace (6.1.3), and the "bibliometrix" package in R version 4.2.1. Results: We included 6,239 publications from 88 countries and regions. The number of published has shown an overall growth trend, especially since 2016. The United States was the country with the highest number of publications and citations. The top 10 most productive institutions in RADS research were mainly from South Korea and the United States. Kim EK was the most published author, and Turkbey B had the most cited publication. European Radiology had the most publications on the subject, while Radiology was the most influential journal. Magnetic resonance imaging, carcinoma, ultrasound, RADS, mammography, breast neoplasms, and diagnosis were the most common keywords. Artificial intelligence (AI) appears to be an emerging hotspot in the research of RADS. Conclusions: This study provides an overview of the development status of research into RADSs over the past 23 years. Research into RADSs has included various systems of the body, with the most studied being the breast, prostate, liver, and thyroid. In terms of auxiliary diagnosis, there is an increasing amount of research into the application of AI in RADSs, which along with the interpretability of AI, will be a hotspot of research in the following years.

Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis.

Objectives: The purpose of this research is to summarize the structure of radiomics-based knowledge and to explore potential trends and priorities by using bibliometric analysis. Methods: Select radiomics-related publications from 2012 to October 2022 from the Science Core Collection Web site. Use VOSviewer (version 1.6.18), CiteSpace (version 6.1.3), Tableau (version 2022), Microsoft Excel and Rstudio's free online platforms (http://bibliometric.com) for co-writing, co-citing, and co-occurrence analysis of countries, institutions, authors, references, and keywords in the field. The visual analysis is also carried out on it. Results: The study included 6428 articles. Since 2012, there has been an increase in research papers based on radiomics. Judging by publications, China has made the largest contribution in this area. We identify the most productive institutions and authors as Fudan University and Tianjie. The top three magazines with the most publications are《FRONTIERS IN ONCOLOGY》, 《EUROPEAN RADIOLOGY》, and 《CANCERS》. According to the results of reference and keyword analysis, "deep learning, nomogram, ultrasound, f-18-fdg, machine learning, covid-19, radiogenomics" has been determined as the main research direction in the future. Conclusion: Radiomics is in a phase of vigorous development with broad prospects. Cross-border cooperation between countries and institutions should be strengthened in the future. It can be predicted that the development of deep learning-based models and multimodal fusion models will be the focus of future research. Advances in knowledge: This study explores the current state of research and hot spots in the field of radiomics from multiple perspectives, comprehensively, and objectively reflecting the evolving trends in imaging-related research and providing a reference for future research.

Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction.

Background: In recent years, computer-aided diagnosis (CAD) systems have played an important role in breast cancer screening and diagnosis. The image segmentation task is the key step in a CAD system for the rapid identification of lesions. Therefore, an efficient breast image segmentation network is necessary for improving the diagnostic accuracy in breast cancer screening. However, due to the characteristics of blurred boundaries, low contrast, and speckle noise in breast ultrasound images, breast lesion segmentation is challenging. In addition, many of the proposed breast tumor segmentation networks are too complex to be applied in practice. Methods: We developed the attention gate and dilation U-shaped network (GDUNet), a lightweight, breast lesion segmentation model. This model improves the inverted bottleneck, integrating it with tokenized multilayer perceptron (MLP) to construct the encoder. Additionally, we introduce the lightweight attention gate (AG) within the skip connection, which effectively filters noise in low-level semantic information across spatial and channel dimensions, thus attenuating irrelevant features. To further improve performance, we innovated the AG dilation (AGDT) block and embedded it between the encoder and decoder in order to capture critical multiscale contextual information. Results: We conducted experiments on two breast cancer datasets. The experiment's results show that compared to UNet, GDUNet could reduce the number of parameters by 10 times and the computational complexity by 58 times while providing a double of the inference speed. Moreover, the GDUNet achieved a better segmentation performance than did the state-of-the-art medical image segmentation architecture. Conclusions: Our proposed GDUNet method can achieve advanced segmentation performance on different breast ultrasound image datasets with high efficiency.

SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images.

With the advancement of deep learning technology, computer-aided diagnosis (CAD) is playing an increasing role in the field of medical diagnosis. In particular, the emergence of Transformer-based models has led to a wider application of computer vision technology in the field of medical image processing. In the diagnosis of thyroid diseases, the diagnosis of benign and malignant thyroid nodules based on the TI-RADS classification is greatly influenced by the subjective judgment of ultrasonographers, and at the same time, it also brings an extremely heavy workload to ultrasonographers. To address this, we propose Swin-Residual Transformer (SRT) in this paper, which incorporates residual blocks and triplet loss into Swin Transformer (SwinT). It improves the sensitivity to global and localized features of thyroid nodules and better distinguishes small feature differences. In our exploratory experiments, SRT model achieves an accuracy of 0.8832 with an AUC of 0.8660, outperforming state-of-the-art convolutional neural network (CNN) and Transformer models. Also, ablation experiments have demonstrated the improved performance in the thyroid nodule classification task after introducing residual blocks and triple loss. These results validate the potential of the proposed SRT model to improve the diagnosis of thyroid nodules' ultrasound images. It also provides a feasible guarantee to avoid excessive puncture sampling of thyroid nodules in future clinical diagnosis.

Treatment strategies and drug resistance mechanisms in adenocarcinoma of different organs.

Adenocarcinoma is a common type of malignant tumor, originating from glandular epithelial cells in various organs, such as pancreas, breast, lung, stomach, colon, rectus, and prostate. For patients who lose the opportunity for radical surgery, medication is available to provide potential clinical benefits. However, drug resistance is a big obstacle to obtain desired clinical prognosis. In this review, we provide a summary of treatment strategies and drug resistance mechanisms in adenocarcinoma of different organs, including pancreatic cancer, gastric adenocarcinoma, colorectal adenocarcinoma, lung adenocarcinoma, and prostate cancer. Although the underlying molecular mechanisms involved in drug resistance of adenocarcinoma vary from one organ to the other, there are several targets that are universal for drug resistance in adenocarcinoma, and targeting these molecules could potentially reverse drug resistance in the treatment of adenocarcinomas.

Real-time contrast-enhanced ultrasound-guided percutaneous biopsy in the diagnosis of ovarian metastasis of gallbladder carcinoma: a case report.

BACKGROUND: Multiple-organ primary tumors can invade the ovary through lymphatic and hematogenous routes, presenting as ovarian Krukenberg tumors, but these rarely originate from the gallbladder. Krukenberg tumors can present similar to primary ovarian tumors; however, their treatments are completely different. PATIENT CONCERNS: A 62-year-old Chinese woman presented with abdominal distension for six months and weight loss of five kilograms for two months. DIAGNOSES: Based on multiple imaging examinations, the patient was preliminarily diagnosed with a malignant tumor of unknown origin with multiple metastases (omentum). To identify the origin of the malignancy, the patient underwent real-time contrast-enhanced ultrasound-guided percutaneous biopsy. The results revealed a perihepatic hypoechoic lesion and right adnexal mass that were both metastatic adenocarcinomas from the gallbladder. INTERVENTIONS: The patient initially received chemotherapy with gemcitabine and cisplatin instead of surgery. However, the tumor increased in size on re-examination after two cycles, so the treatment was shifted to a combination regimen with durvalumab for six cycles. OUTCOMES: The treatment proceeded smoothly, with no recurrence or obvious progression of the cancer during follow-up. CONCLUSIONS: Differentiating between primary and metastatic ovarian tumors is important. Early diagnosis and effective treatment options are essential for patient survival. CEUS-guided percutaneous biopsy is a valuable procedure for patients with multiple metastases who cannot tolerate surgery.

DPAM-PSPNet: ultrasonic image segmentation of thyroid nodule based on dual-path attention mechanism.

Objective.Deep learning has demonstrated its versatility in the medical field, particularly in medical image segmentation, image classification, and other forms of automated diagnostics. The clinical diagnosis of thyroid nodules requires radiologists to locate nodules, diagnose conditions based on nodule boundaries, textures and their experience. This task is labor-intensive and tiring; therefore, an automated system for accurate thyroid nodule segmentation is essential. In this study, a model named DPAM-PSPNet was proposed, which automatically segments nodules in thyroid ultrasound images and enables to segment malignant nodules precisely.Approach.In this paper, accurate segmentation of nodule edges is achieved by introducing the dual path attention mechanism (DPAM) in PSPNet. In one channel, it captures global information with a lightweight cross-channel interaction mechanism. In other channel, it focus on nodal margins and surrounding information through the residual bridge network. We also updated the integrated loss function to accommodate the DPAM-PSPNet.Main results.The DPAM-PSPNet was tested against the classical segmentation model. Ablation experiments were designed for the two-path attention mechanism and the new loss function, and generalization experiments were designed on the public dataset. Our experimental results demonstrate that DPAM-PSPNet outperforms other existing methods in various evaluation metrics. In the model comparison experiments, it achieved performance with an mIOU of 0.8675, mPA of 0.9357, mPrecision of 0.9202, and Dice coefficient of 0.9213.Significance.The DPAM-PSPNet model can segment thyroid nodules in ultrasound images with little training data and generate accurate boundary regions for these nodules.

Gold nanoparticles and gold nanorods in the landscape of cancer therapy.

Cancer is a grievous disease whose treatment requires a more efficient, non-invasive therapy, associated with minimal side effects. Gold nanoparticles possessing greatly impressive optical properties have been a forerunner in bioengineered cancer therapy. This theranostic system has gained immense popularity and finds its application in the field of molecular detection, biological imaging, cancer cell targeting, etc. The photothermal property of nanoparticles, especially of gold nanorods, causes absorption of the light incident by the light source, and transforms it into heat, resulting in tumor cell destruction. This review describes the different optical features of gold nanoparticles and summarizes the advance research done for the application of gold nanoparticles and precisely gold nanorods for combating various cancers including breast, lung, colon, oral, prostate, and pancreatic cancer.

Artifacts in two-dimensional shear wave elastography of liver.

BACKGROUND: Artifacts are common when using two-dimensional shear wave elastography (2-D SWE) to measure liver stiffness (LS), but they are poorly recognized. AIM: To investigate the presence and influence of artifacts in 2-D SWE of liver. METHODS: We included 158 patients with chronic liver disease, who underwent 2-D SWE examination by a novice and an expert. A cross line at the center of the elastogram was drawn and was divided it into four locations: top-left, top-right, bottom-left, and bottom-right. The occurrence frequency of artifacts in different locations was compared. The influence of artifacts on the LS measurements was evaluated by comparing the elastogram with the most artifacts (EMA) and the elastogram with the least artifacts (ELA). RESULTS: The percentage of elastograms with artifacts in the novice (51.7%) was significantly higher than that of the expert (19.6%) (P < 0.001). It was found that both operators had the highest frequency of artifacts at bottom-left, followed by top-left and bottom-right, and top-right had the lowest frequency. The LS values (LSVs) and standard deviation values of EMAs were significantly higher than those of ELAs for both operators. An intraclass correlation coefficient value of 0.96 was found in the LSVs of EMAs of the two operators, and it increased to 0.98 when the LSVs of the ELAs were used. Both operators had lower stability index values for EMAs than ELAs, but the difference was only statistically significant for the novice. CONCLUSION: Artifacts are common when using 2-D SWE to measure LS, especially for the novice. Artifacts may lead to the overestimation of LS and reduce the repeatability and reliability of LS measurements.

An effective convolutional neural network for classification of benign and malignant breast and thyroid tumors from ultrasound images.

Breast and thyroid cancers are the two most common cancers among women worldwide. The early clinical diagnosis of breast and thyroid cancers often utilizes ultrasonography. Most of the ultrasound images of breast and thyroid cancer lack specificity, which reduces the accuracy of ultrasound clinical diagnosis. This study attempts to develop an effective convolutional neural network (E-CNN) for the classification of benign and malignant breast and thyroid tumors from ultrasound images. The 2-Dimension (2D) ultrasound images of 1052 breast tumors were collected, and 8245 2D tumor images were obtained from 76 thyroid cases. We performed tenfold cross-validation on breast and thyroid data, with a mean classification accuracy of 0.932 and 0.902, respectively. In addition, the proposed E-CNN was applied to classify and evaluate 9297 mixed images (breast and thyroid images). The mean classification accuracy was 0.875, and the mean area under the curve (AUC) was 0.955. Based on data in the same modality, we transferred the breast model to classify typical tumor images of 76 patients. The finetuning model achieved a mean classification accuracy of 0.945, and a mean AUC of 0.958. Meanwhile, the transfer thyroid model realized a mean classification accuracy of 0.932, and a mean AUC of 0.959, on 1052 breast tumor images. The experimental results demonstrate the ability of the E-CNN to learn the features and classify breast and thyroid tumors. Besides, it is promising to classify benign and malignant tumors from ultrasound images with the transfer model under the same modality.

Efficacy and Safety of Thermal Ablation for Treating Lymph Node Metastasis From Papillary Thyroid Carcinoma: A Systematic Review and Meta-Analysis.

Objective: To evaluate the efficacy and safety of thermal ablation, including radiofrequency ablation (RFA), microwave ablation (MVA), and laser ablation (LA), for treating lymph node metastasis (LNM) from papillary thyroid carcinoma (PTC). Design and Methods: PubMed and EMBASE were searched for studies reporting the efficacy and safety of thermal ablation for treating LNM in PTC. After selecting the relevant literature (including 11 papers, 208 patients, 412 lymph nodes), the QUADAS-2 tool was used to evaluate its quality. Then, both the fixed-effects and random-effects models combined with subgroup analysis were used to calculate data on volume changes in metastatic lymph nodes and changes in serum thyroglobulin (Tg) levels. We pooled the proportion of major and overall complication rates and complete disappearance rates and used subgroup forest plots and funnel plots for visual representation. Because of publication bias, we also performed a trim-and-filled model for correction. The rate of recurrence and distant metastasis with ablated details were pooled. Results: In the 11 articles (208 patients and 412 diseased lymph nodes), all thermal ablation methods showed effectiveness in reducing lymph node volume (P = 0.02) and serum Tg levels (P < 0.01) which showed no between-group difference. The pooled proportion of major complications was 0%(95% CI: -0.14; 0.15, P = 1) and the overall complication rate was 5% (95% CI: -0.09; 0.20, P = 1), which revealed no significant difference among modalities. The pooled proportion of the complete disappearance rate was 82% (95% CI: 0.43; 0.96, P < 0.01) and the data with statistical significance which contains RFA and LA showed complete disappearance rate was 59% and 81% respectively. Conclusion: All thermal ablation methods, including RFA, MWA, and LA, were effective and safe for treating LNM in PTC and were especially suitable for nonsurgical patients. Besides, subgroup analysis showed no significant difference, except for LA is better than RFA in complete disappearance rate.

Effect of Q-Box size on liver stiffness measurement by two-dimensional shear wave elastography.

PURPOSE: To investigate the effect of the Q-Box size on liver stiffness (LS) measurement by two-dimensional shear wave elastography (2D SWE). METHODS: Ninety-eight patients with chronic liver disease were enrolled. Each patient was continuously measured five times. The Q-Box diameter was adjusted to 10, 20, and 30 mm each time. The liver stiffness values (LSVs) at different diameters were compared in the following groups: LSVs ≤6.2 kPa, 6.2 kPa < LSVs ≤11 kPa, LSVs >11 kPa. The reliability and repeatability of LS measurement at different diameters were evaluated. RESULTS: The differences in LSVs at different Q-Box diameters were statistically significant only when LSV ≤6.2 kPa (p = 0.004). There were no statistically significant differences in standard deviation (SD), SD/median, coefficient of variation (CV), and interquartile range (IQR)/median at different Q-Box diameters (p > 0.05). There were statistical differences in minimum LSVs and percentage of minimum LSVs ≤0.2 kPa as well as in stability index (SI) and percentage of SI <90% at different Q-Box diameters (p < 0.05). The intraclass correlation coefficients (ICCs) were up to 0.98 at Q-Box diameters of 10, 20, and 30 mm. CONCLUSIONS: Our study showed that Q-Box size may lead to significant differences in LSVs, especially when LSVs ≤6.2 kPa. The Q-Box size had a large effect on the reliability of a single LS measurement but did not affect the repeatability of multiple measurements.

The N6-Methyladenosine Features of mRNA and Aberrant Expression of m6A Modified Genes in Gastric Cancer and Their Potential Impact on the Risk and Prognosis.

Although N6-methyladenosine (m6A) mRNA methylation is known to be closely related to tumor events, its role in carcinogenesis and the development of gastric cancer (GC) is not yet clear. The aim of this study was to identify common m6A features and novel aberrant expression of m6A modified genes in GC and to further explore their potential impact on risk and prognosis. Three paired GC and paracancerous (PCa) tissues were collected to perform an m6A sequencing by MeRIP-seq and microarray assays. The expression profile of m6A and mRNA were determined. Gene function note and enrichment analysis were performed, and protein-protein interaction networks of differentially m6A methylated genes (DMGs) were generated using the DAVID and STRING databases, respectively. Validation of the m6A related differentially expressed genes by matching TCGA and GTEx data and human tissues. Clinical and pathological correlation and survival analysis were performed by TCGA data. The m6A motif sequence GGACAR (R = U or A) C was the consensus in both GC and PCa tissues. m6A peaks were significantly related to different coordinates, however, for most samples, the end of the coding sequence (CDS) was more prominent than the start of CDS. The genes with higher levels of m6A in their mRNAs were mainly enriched in transcriptional misregulation in carcinogenesis pathways, whereas the genes with decreased methylation mainly regulated digestion and absorption of protein. There are genes with differential m6A modifications in GC and paired PCa tissues, and these genes are mainly enriched in transcriptional misregulation and digestion/absorption pathways. m6A-GC with the down- and up-regulated genes may play an important role in gastric carcinogenesis, which can affect the risk and prognosis in GC.

Identification of methylated-differentially expressed genes and pathways in esophageal squamous cell carcinoma.

Methylation, as an epigenetic modification, can affect gene expression and play a role in the occurrence and development of cancer. This research is devoted to discover methylated-differentially expressed genes (MDEGs) in esophageal squamous cell carcinoma (ESCC) and explore special associated pathways. We downloaded GSE51287 methylation profiles and GSE26886 expression profiles from GEO DataSets, and performed a comprehensive bioinformatics analysis. Totally, 19 hypermethylated, lowly expressed genes (Hyper-LGs) were identified, and involved in regulation of cell proliferation, phosphorus metabolic process and protein kinase activity. Meanwhile, 17 hypomethylated, highly expressed genes (Hypo-HGs) were participated in collagen catabolic process, metallopeptidase and cytokine activity. Pathway analysis determined that Hyper-LGs were enriched in arachidonic acid metabolism pathway, while Hypo-HGs were primarily associated with the cytokine-cytokine receptor interaction pathway. IL 6, MMP3, MMP9, SPP1 were identified as hub genes based on the PPI network that combined 7 ranked methods included in cytoHubba, and verification was performed in human tissues. Our integrated analysis identified many novel genetic lesions in ESCC and provides a crucial molecular foundation to improve our understanding of ESCC. Hub genes, including IL 6, MMP3, MMP9 and SPP1, could be considered for use as aberrant methylation-based biomarkers to facilitate the accurate diagnosis and therapy of ESCC.

The combination of ATA classification and FNA results can improve the diagnostic efficiency of malignant thyroid nodules.

PURPOSE: To determine the diagnostic efficiency of the ATA classification and ultrasound-guided fine-needle aspiration (FNA) results in identifying the risk factors of malignancy, we analyzed the thyroid nodules of patients who underwent thyroidectomy and compared preoperative ATA classifications with FNA results. METHODS: We retrospectively analyzed 274 nodules of 196 patients who underwent ultrasonography, FNA and thyroidectomy. Histopathological findings of thyroid nodules were considered as the Au standard in the analysis of the diagnostic efficiency of the ATA classification and FNA results. Univariate analysis and binary multivariate logistic regression analysis were applied to identify the ultrasound features associated with malignancy. RESULTS: The overall malignancy rate of 274 nodules was 41.6%. The areas under the ROC curves (AUCs) for the ATA classification and FNA results were 0.88 and 0.878, respectively (P < 0.001). The sensitivity and specificity of the ATA classification were 86 and 86.9%, whereas those of FNA results were 68.5 and 91.4%, respectively. The specificity (98.7%) and sensitivity (94.3%) increased after the combined use of the ATA classification and FNA results. Taller-than-wide shape, microcalcifications, hypoechogenicity and irregular margins were independent risk factors for malignancy. Microcalcifications had the highest OR (7.58), and taller-than-wide shape had the highest specificity in BSRTC I, II, III and IV cytology. CONCLUSION: The diagnostic efficiency of the ATA classification and FNA results in identifying malignant nodules was high, and the use of both criteria improved the diagnostic accuracy. Taller-than-wide shape, microcalcifications, hypoechogenicity and irregular margins were independent risk factors for malignancy.

Greater Omentum Imaging-Reporting and Data System: establishing the grade of benign and malignant lesions of the greater omentum using ultrasonography.

OBJECTIVE: To establish Greater Omentum Imaging-Reporting and Data System (GOI-RADS) to evaluate the possibility of omental diseases being malignant. METHOD: A retrospective analysis was made of 883 patients who had undergone biopsy of the greater omentum in our center from October 2009 to October 2019. Twelve parameters of ultrasonographic images were evaluated, and the odds ratio of each group calculated. We assigned scores for the direct signs (omental echo, omental structure, and omental nodules) and indirect signs (separation of ascites, echo of ascites, mesenteric lymph nodes, and thickening of parietal peritoneum) of omental lesions. We created an omental score (OS) for each patient and receiver operating characteristic (ROC) curve to analyze its effectiveness in the differential diagnosis of benign and malignant omental diseases. RESULTS: The OS was divided into ≤5, 6, 7, 8, 9, 10, 11, 12, 13, and ≥ 14 points, and the malignant rate was 0, 1.85, 5.56, 30.36, 37.25, 87.72, 96.72, 98.28, 99.08, and 100%, respectively. The area under the ROC curve (AUC) was 0.976. When taking 10 points as the cutoff value to diagnose benign and malignant omental diseases, the sensitivity and specificity was 93.85 and 98.21%, respectively. A grading system was established: grade 1: omental score ≤ 5, malignant rate 0%; grade 2: omental score 6-7, malignant rate ≤ 5.56%; grade 3: omental score 8--9, malignant rate ≤ 37.25%; grade 4: omental score ≥ 10, malignant rate ≥ 87.72. CONCLUSION: GOI-RADS had high sensitivity and specificity in the differential diagnosis of benign and malignant omental lesions. We believe that GOI-RADS will aid the diagnosis of omental diseases based on objective and accurate interpretation of ultrasound features, and also to promote the ultrasonography of omental diseases in clinical application.

Expression and regulation effects of chemokine receptor 7 in colon cancer cells.

In China the incidence and mortality rates of colon cancer have been increasing annually. Studies have revealed that CXCR7 is expressed in many tumors. The aim of the present study was to investigate the function of CXCR7 in colon cancer. The expression level of chemokine receptor 7 (CXCR7) in Caco-2 and HCT116 cells was investigated to elucidate the effect of CXCR7 on cell biological behavior. Reverse transcription-quantitative PCR and western blot analysis were used to detect the expression level of CXCR7 in Caco-2 and HCT116 cells after transfection with small interfering (si)RNA. To analyze the in vitro biological function of CXCR7, cell proliferation was measured using a Cell Counting Kit-8 assay, and cell invasion and migration were measured using Matrigel, and Transwell and wound healing assays. siRNAs were successfully transfected into Caco-2 and HCT116 cells and resulted in a decrease in CXCR7 protein and mRNA expression. Downregulation of CXCR7 inhibited Caco-2 and HCT116 cell proliferation, invasion, and migration. Regulation of CXCR7 expression may affect the biological behavior of Caco-2 and HCT116 cells, suggesting that CXCR7 has a potential role in molecular therapy in colon cancer.

Does Operator Experience and the Q-Box Diameter Affect the Repeatability of Liver Stiffness Measurements Obtained by 2-Dimensional Shear Wave Elastography?

OBJECTIVES: The purpose of this research was to evaluate whether operator experience and the quantitative analysis system (Q-Box; SuperSonic Imagine, Aix-en-Provence, France) diameter affect the repeatability of liver stiffness measurements. METHODS: We enrolled 417 outpatients. All measurements were performed by 2 operators, including an expert and a novice. Each patient was continuously measured 3 times by the 2 operators. The Q-Box diameter was adjusted to 10, 20, and 30 mm each time, and the mean elasticity values were recorded. Intraobserver repeatability was evaluated by the intraclass correlation coefficient (ICC). Interobserver repeatability was evaluated by the ICC, coefficient of variation (CV), and Bland-Altman plots. RESULTS: The study group included 241 male and 176 female patients. The expert operator had higher ICCs than the novice operator at each Q-Box diameter. The overall interobserver agreement was excellent, and the results showed that compared to other groups, the ICC was the lowest and the CV was the largest for the 30-mm-diameter group. The ICC and CV values were similar between the 10- and 20 mm-diameter groups. The Bland-Altman plots showed that the mean difference was -0.2 kPa for the 10-, 20-, and 30 mm-diameter groups. However, the limits of agreement were the largest in the 30-mm-diameter group and were similar between the 10- and 20-mm-diameter groups. CONCLUSIONS: The repeatability of liver stiffness measurements is affected not only by experience but also by the Q-Box diameter.