Search

Objective Advanced gastric cancer (AGC) is a severe malignant tumor, and overexpression of HER2/ERBB2 may play a crucial role in its development. The purpose of this study is to investigate the overexpression of HER2/ERBB2 in gastric cancer through a meta-analysis and examine its relationship with perfusion parameters using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technology. Methods We conducted an extensive literature search and collected relevant studies for the meta-analysis. We used a random-effects model and assigned weights using the “inverse variance” method. Additionally, we included 95 AGC patients diagnosed pathologically between April 2018 and October 2021. They all underwent DCE-MRI scans, and the data were subsequently analyzed using the Omni kinetic software. HER2 expression was assessed using immunohistochemistry. Results The meta-analysis revealed an overall odds ratio (OR) of .21 for HER2/ERBB2 overexpression in gastric cancer, with a 95% confidence interval of [.14, .30]. DCE-MRI results showed a significant association between high HER2 expression and poor tumor differentiation ( P < .005). The extracellular volume fraction (Ecv) quantile, mean, relative deviation, median intensity, and difference entropy were significantly higher in the low HER2 expression group compared to the high HER2 expression group. Receiver operating characteristic (ROC) curve analysis demonstrated that the area under the curve (AUC) values of DCE-MRI radiomic parameters with significant differences were close to .7. Conclusion Overexpression of HER2/ERBB2 in gastric cancer is significantly associated with certain radiomic parameters of DCE-MRI, providing a valuable diagnostic tool for clinical practice. Furthermore, the meta-analysis further confirmed the critical role of HER2/ERBB2 in the development of gastric cancer.

Objective: Proton magnetic resonance spectroscopy (1H-MRS) was applied in this study to detect metabolite changes in the brain of post-stroke cognitive impairment (PSCI) and normal volunteers. The levels of N-acetylaspartate (NAA) and creatinine (Cr) and in the frontal lobe, hippocampus and cingulate gyrus were measured to distinguish patients with post-stroke cognitive impairment (PSCI) and normal control group (NC). The relationship between them and cognitive function was explored and a critical value of the metabolite ratio was predicted. This study may serve as a reference for the diagnosis of cognitive dysfunction after stroke. Methods: A total of 46 patients with PSCI (PSCI group, all patients are unilateral cerebral infarction or intracerebral haemorrhage) were screened by the Mini-Mental Status Examination (MMSE), and 35 healthy volunteers were selected as normal control group (NC group). The general information of gender, age, and education level was matched between the two groups. Two groups of subjects were examined using MRS and evaluated for cognitive function using the MMSE test and the Montreal Cognitive Assessment Scale (MoCA). The correlation between MRS and neurobehavioral scale (MMSE test and MoCA scale) was analysed, and the possible demarcation points of the brain metabolism of PSCI were evaluated. Result: The MMSE and MoCA scores of patients with PSCI were lower significantly when compared with those of the NC group (P

ObjectiveTo explore the effectiveness of machine learning classifiers based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes (TILs) in patients with advanced gastric cancer (AGC).MethodsThis study investigated 103 patients with confirmed AGC through DCE-MRI and immunohistochemical staining. Immunohistochemical staining was used to evaluate CD3+, CD4+, and CD8+ T-cell expression. Utilizing Omni Kinetics software, radiomics features (Ktrans, Kep, and Ve) were extracted and underwent selection via variance threshold, SelectKBest, and LASSO methods. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) are the four classifiers used to build four machine learning (ML) models, and their performance was evaluated using 10-fold cross-validation. The model’s performance was evaluated and compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.ResultsIn terms of CD3+, CD4+, and CD8+ T lymphocyte prediction models, the random forest model outperformed the other classifier models in terms of CD4+ and CD8+ T cell prediction, with AUCs of 0.913 and 0.970 on the training set and 0.904 and 0.908 on the validation set, respectively. In terms of CD3+ T cell prediction, the logistic regression model fared the best, with AUCs on the training and validation sets of 0.872 and 0.817, respectively.ConclusionMachine learning classifiers based on DCE-MRI have the potential to accurately predict CD3+, CD4+, and CD8+ tumor-infiltrating lymphocyte expression levels in patients with AGC.

Objective: The aim of this investigation was to explore the correlation between the levels of tumor-infiltrating CD8+ and CD4+ T cells and the quantitative pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced gastric cancer.Methods: We retrospectively analyzed the data of 103 patients with histopathologically confirmed advanced gastric cancer (AGC). Three pharmacokinetic parameters, Kep, Ktrans, and Ve, and their radiomics characteristics were obtained by Omni Kinetics software. Immunohistochemical staining was used to determine CD4+ and CD8+ TILs. Statistical analysis was subsequently performed to assess the correlation between radiomics characteristics and CD4+ and CD8+ TIL density.Results: All patients included in this study were finally divided into either a CD8+ TILs low-density group (n = 51) (CD8+ TILs < 138) or a high-density group (n = 52) (CD8+ TILs ≥ 138), and a CD4+ TILs low-density group (n = 51) (CD4+ TILs < 87) or a high-density group (n = 52) (CD4+ TILs ≥ 87). ClusterShade and Skewness based on Kep and Skewness based on Ktrans both showed moderate negative correlation with CD8+ TIL levels (r = 0.630–0.349, p < 0.001), with ClusterShade based on Kep having the highest negative correlation (r = −0.630, p < 0.001). Inertia-based Kep showed a moderate positive correlation with the CD4+ TIL level (r = 0.549, p < 0.001), and the Correlation based on Kep showed a moderate negative correlation with the CD4+ TIL level, which also had the highest correlation coefficient (r = −0.616, p < 0.001). The diagnostic efficacy of the above features was assessed by ROC curves. For CD8+ TILs, ClusterShade of Kep had the highest mean area under the curve (AUC) (0.863). For CD4+ TILs, the Correlation of Kep had the highest mean AUC (0.856).Conclusion: The radiomics features of DCE-MRI are associated with the expression of tumor-infiltrating CD8+ and CD4+ T cells in AGC, which have the potential to noninvasively evaluate the expression of CD8+ and CD4+ TILs in AGC patients.

Introduction: Arteriocolonic fistula of Inferior Mesenteric Artery Aneurysm (IMAA) refers to a spontaneous formation of pathological communication between the artery and the adjacent gastrointestinal tract. It is a rare, life-threatening condition primarily manifesting as abdominal pain, gastrointestinal bleeding, abdominal pulsating masses. However, its clinical manifestations are usually atypical with a difficult diagnosis and treatment. Case presentation: We report a rare case of a 50-year-old male with a hemorrhagic shock due to primary arteriocolonic fistula of IMAA. Instead of sigmoidectomy, super selective transcatheter arterial embolization (TAE) was performed after diagnostic angiography. Postoperatively, dynamic contrast-enhanced abdominal computed tomography (CT) demonstrated no recanalization of the aneurysm, absence of abnormal collateral vessels, no active hemorrhage. The patient was discharged uneventfully after 2 weeks without abdominal pain or tension. Discussion: Colorectal tumor rupture is a major cause of lower gastrointestinal bleeding (LGIB), with IMAA being an uncommon etiology. Because of the high mortality of explorative laparotomy with an unclear bleeding site, diagnostic angiography and therapeutic TAE are viable options for diagnosing hemodynamic instability. Conclusion: Arteriocolonic fistulas commonly occur secondary to a pseudoaneurysm formed at the anastomosis of the transplanted blood vessel after an artery surgery, which ruptures and penetrates into the intestine. We reported a unique case of primary arteriocolonic fistula of IMAA: aneurysm rupture and bleeding from the abdomen into the hematochezia. After multidisciplinary consultations, our patient obtained the best outcome using the most minimally invasive surgical methods. With an abdominal artery aneurysm presenting with colorectal hemorrhage, arteriocolonic fistula of IMAA should be suspected.

ObjectiveLow-density lipoprotein receptor-related protein-1 (LRP-1) and survivin are associated with radiotherapy resistance in patients with locally advanced rectal cancer (LARC). This study aimed to evaluate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the preoperative assessment of LRP-1 and survivin expressions in these patients.MethodsOne hundred patients with pathologically confirmed LARC who underwent DCE-MRI before surgery between February 2017 and September 2021 were included in this retrospective study. DCE-MRI perfusion histogram parameters were calculated for the entire lesion using post-processing software (Omni Kinetics, G.E. Healthcare, China), with three quantitative parameter maps. LRP-1 and survivin expressions were assessed by immunohistochemical methods and patients were classified into low- and high-expression groups.ResultsFour radiomics features were selected to construct the LRP-1 discrimination model. The LRP-1 predictive model achieved excellent diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.853 and 0.747 in the training and validation cohorts, respectively. The other four radiomics characteristics were screened to construct the survivin predictive model, with AUCs of 0.780 and 0.800 in the training and validation cohorts, respectively. Decision curve analysis confirmed the clinical usefulness of the radiomics models.ConclusionDCE-MRI radiomics models are particularly useful for evaluating LRP-1 and survivin expressions in patients with LARC. Our model has significant potential for the preoperative identification of patients with radiotherapy resistance and can serve as an essential reference for treatment planning.

Reactive lymphoid hyperplasia (RLH) of the liver is a rare benign disease. This article describes a 77-year-old female patient with RLH of the liver. The patient was admitted to the hospital due to atrial fibrillation. A liver tumor was incidentally found during abdominal enhanced CT. Further magnetic resonance imaging (MRI) and PET/CT showed four lesions in the liver. The imaging findings suggested hepatocellular carcinoma (HCC), but it was not consistent that the patient had no history of liver cirrhosis and hepatitis, and a variety of tumor markers were within the normal range. The largest lesion was surgically removed and microscopically diagnosed as RLH of the liver. The pathology included a large number of reactive hyperplastic lymphoid follicles. Immunohistochemical examination showed that the infiltrating lymphocytes were polyclonal. The authors believe that the perinodular enhancement on MRI, the obvious limitation of diffusion on DWI, the insignificant increase of SUVmax on PET-CT delayed phase, and the support of clinical data can help distinguish liver RLH from lymphoma and HCC.

Gallbladder carcinoma (GBC) is a rare and highly aggressive tumor. Early diagnosis is challenging, which results in a poor prognosis using systemic therapy. Recent studies have identified a subset of GBC patients with HER2 gene (ERBB2) amplification that could benefit from HER2-targeted therapy. Here, we report one patient with recurrent metachronous GBC with metastasis, who received the combination of trastuzumab and lapatinib. This approach achieved a partial response for both the brain and the lung metastases. This study demonstrated that HER2 inhibition is a promising therapeutic strategy for GBC with HER2 amplification and, combined with lapatinib, it can effectively target brain metastasis.

Objective: To explore the effectiveness of machine learning classifiers based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes (TILs) in patients with advanced gastric cancer (AGC). Methods: This study investigated 103 patients with confirmed AGC through DCE-MRI and immunohistochemical staining. Immunohistochemical staining was used to evaluate CD3+, CD4+, and CD8+ T-cell expression. Utilizing Omni Kinetics software, radiomics features (Ktrans, Kep, and Ve) were extracted and underwent selection via variance threshold, SelectKBest, and LASSO methods. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) are the four classifiers used to build four machine learning (ML) models, and their performance was evaluated using 10-fold cross-validation. The model's performance was evaluated and compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results: In terms of CD3+, CD4+, and CD8+ T lymphocyte prediction models, the random forest model outperformed the other classifier models in terms of CD4+ and CD8+ T cell prediction, with AUCs of 0.913 and 0.970 on the training set and 0.904 and 0.908 on the validation set, respectively. In terms of CD3+ T cell prediction, the logistic regression model fared the best, with AUCs on the training and validation sets of 0.872 and 0.817, respectively. Conclusion: Machine learning classifiers based on DCE-MRI have the potential to accurately predict CD3+, CD4+, and CD8+ tumor-infiltrating lymphocyte expression levels in patients with AGC. [ABSTRACT FROM AUTHOR]