CT findings and clinical features of Epstein-Barr virus-associated lymphoepithelioma-like gastric carcinoma

Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction

PURPOSE

To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC.

METHODS

This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM).

RESULTS

T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively.

CONCLUSION

A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.

A computed tomography-based preoperative risk scoring system to distinguish lymphoepithelioma-like gastric carcinoma from non-lymphoepithelioma-like gastric carcinoma

Purpose

The aim of this study was to develop a preoperative risk scoring model for distinguishing lymphoepithelioma-like gastric carcinoma (LELGC) from non-LELGC based on contrast-enhanced computed tomography (CT) images.

Methods

Clinicopathological features and CT findings of patients with LELGC and non-LELGC in our hospital from January 2016 to July 2022 were retrospectively analyzed and compared. A preoperative risk stratification model and a risk scoring system were developed using logistic regression.

Results

Twenty patients with LELGC and 40 patients with non-LELGC were included in the training cohort. Significant differences were observed in Epstein–Barr virus (EBV) infection and vascular invasion between the two groups (p < 0.05). Significant differences were observed in the distribution of location, enhancement pattern, homogeneous enhancement, CT-defined lymph node status, and attenuations in the non-contrast, arterial, and venous phases (all p < 0.05). Enhancement pattern, CT-defined lymph node status, and attenuation in venous phase were independent predictors of LELGC. The optimal cutoff score of distinguishing LELGC from non-LELGC was 3.5. The area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of risk identification model in the training cohort were 0.904, 87.5%, 80.0%, and 85.0%, respectively. The area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of risk identification model in the validation cohort were 0.705 (95% CI 0.434–0.957), 75.0%, 63.6%, and 66.7%, respectively.

Conclusion

A preoperative risk identification model based on CT imaging data could be helpful for distinguishing LELGC from non-LELGC.

Human Papillomavirus-Associated Lymphoepithelioma-Like Carcinoma of the Anal Canal: A Case Report and Literature Review

Lymphoepithelioma-like carcinoma is a rare type of tumor that is histologically identical to lymphoepithelial carcinoma of the nasopharynx. Lymphoepithelioma-like carcinomas (LELCs) are closely associated with viral infections. Human papillomavirus (HPV)-associated LELCs have been reported in a variety of anatomic sites. We reported an extremely rare case of a 25-year-old woman with LELC derived from the anal canal, which is the second case reported at this site. The tumor was diffusely positive for p16 staining, and was correlated with high-risk HPV-16; Epstein-Barr virus-encoded small RNA was negative; PD-L1 positivity and abundant CD8+ T cell infiltration were observed, indicating a “hot” immune microenvironment. In reporting this case, we highlight the potential for misdiagnosis and suggested an association of HPV infection with LELC in the anal canal.

Clinical value of miR-135 and miR-20a combined with multi-detector computed tomography in the diagnosis of gastric cancer

Background

To study the clinical value of miR-135 and miR-20a combined with multi-detector computed tomography (MDCT) in the diagnosis of gastric cancer (GC).

Method

A total of 146 patients with GC admitted to our hospital from January 2017 to June 2019 were selected and enrolled in the GC group. Another 103 patients with gastritis received in the same period were selected for the non-GC group. Besides, 95 healthy subjects who received physical examination in our hospital were selected into the healthy control group. Real-time fluorescence quantitative polymerase chain reaction (qRT-PCR) was used to detect the expression of serum miR-135 and miR-20a for each group. MDCT was used for detecting the clinical staging map of the enrolled patients. Pearson’s correlation analysis was used to analyze the correlation between serum miR-135 and miR-20a in patients with GC. The receiver operating characteristic (ROC) curve was drawn to analyze value of miR-135 and miR-20a in the diagnosis of GC.

Results

Compared with non-GC group and healthy control group, the levels of serum miR-135 and miR-20a increased significantly in the GC group, while no significant difference was found between non-GC group and healthy control group (P > 0.05). Analysis of the relationship with clinical characteristics showed that the expression of serum miR-135 and miR-20a in the GC group was significantly correlated with the progression of GC, TNM stage, degrees of differentiation, status of lymph node metastasis, and distant metastasis (P < 0.01). Pearson’s correlation analysis results showed positive correlations between miR-135 and miR-20a (r = 0.634, P = 0.000). The ROC analysis results showed that the optimal diagnostic values of miR-135 and miR-20a for GC were 7.56 and 5.82 respectively. The area under the curve (AUC) was 0.873 and 0.793 respectively. The 95% confidence interval (CI) was 0.811-0.935 and 0.697-0.890 respectively. The sensitivity and specificity of miR-135 and miR-20a combined with MDCT in the diagnosis of GC were 90.41% and 93.20% respectively. The sensitivity of combined use was significantly higher than that of single detection (P < 0.01).

Conclusion

There are high expression levels of serum miR-135 and miR-20a in patients with GC. A combined detection of miR-135 and miR-20a with MDCT can improve the diagnostic sensitivity of GC and improve the accuracy of the final diagnosis. Therefore, multiple combined detection is valuable in the diagnosis of GC.

Human Gut Microbiota-associated Gastrointestinal Malignancies: A Comprehensive Review

The human gastrointestinal tract houses trillions of microbes. The gut and various types of microorganisms, including bacteria, viruses, fungi, and archaea, form a complex ecosystem known as the gut microbiota, and the whole genome of the gut microbiota is referred to as the gut microbiome. The gut microbiota is essential for homeostasis and the overall well-being of a person and is increasingly considered an adjunct "virtual organ," with a complexity level comparable to that of the other organ systems. The gut microbiota plays an essential role in nutrition, local mucosal homeostasis, inflammation, and the mucosal immune system. An imbalanced state of the gut microbiota, known as dysbiosis, can predispose to development of various gastrointestinal malignancies through three speculated pathogenic mechanisms: (a) direct cytotoxic effects with damage to the host DNA, (b) disproportionate proinflammatory signaling inducing inflammation, and (c) activation of tumorigenic pathways or suppression of tumor-suppressing pathways. Several microorganisms, including Helicobacter pylori, Epstein-Barr virus, human papillomavirus, Mycoplasma species, Escherichia coli, and Streptococcus bovis, are associated with gastrointestinal malignancies such as esophageal adenocarcinoma, gastric adenocarcinoma, gastric mucosa-associated lymphoid tissue lymphoma, colorectal adenocarcinoma, and anal squamous cell carcinoma. Imaging plays a pivotal role in diagnosis and management of microbiota-associated gastrointestinal malignancies. Appropriate use of probiotics, fecal microbiota transplantation, and overall promotion of the healthy gut are ongoing areas of research for prevention and treatment of malignancies. Online supplemental material is available for this article. ^©RSNA, 2021.

TCGA-TCIA-Based CT Radiomics Study for Noninvasively Predicting Epstein-Barr Virus Status in Gastric Cancer

OBJECTIVE. The purpose of this study was to investigate the value of TCGA-TCIA (The Cancer Genome Atlas and The Cancer Imaging Archive)-based CT radiomics for noninvasive prediction of Epstein-Barr virus (EBV) status in gastric cancer (GC). MATERIALS AND METHODS. A total of 133 patients with pathologically confirmed GC (94 in the training cohort and 39 in the validation cohort) who were identified from the TCGA-TCIA public data repository and two hospitals were retrospectively enrolled in the study. Two-dimensional and 3D radiomics features were extracted to construct corresponding radiomics signatures. Then, 2D and 3D nomograms were built by combining radiomics signatures and clinical information on the basis of multivariable analysis. Their performance and clinical practicability were determined, validated, and compared with respect to discrimination, calibration, reclassification, and time spent on tumor segmentation. RESULTS. Both 2D and 3D nomograms were robust and showed good calibration. The AUCs of the 2D and 3D nomograms showed no significant difference in the training cohort (0.919 vs 0.945, respectively; p = .41) or validation cohort (0.939 vs 0.955, respectively; p = .71). The net reclassification index showed that the 3D nomogram revealed no significant improvement in risk reclassification when compared with the 2D nomogram in the training cohort (net reclassification index, 0.68%; p = .14) and the validation cohort (net reclassification index, 6.06%; p = .08). Of note, the time spent on 3D segmentation (median, 907 seconds) was higher than that spent on 2D segmentation (median, 129 seconds). CONCLUSION. The 2D and 3D radiomics nomograms might have the potential to be used as effective tools for prediction of EBV in GC. When time spent on segmentation is considered, the 2D nomogram is more highly recommended for clinical application.