Recent Trends and Advancements in the Diagnosis and Management of Gastric Cancer

Haque, Emaan; Esmail, Abdullah; Muhsen, Ibrahim N.; Salah, Haneen; Abdelrahim, Maen

doi:10.3390/cancers14225615

Cited by 12 publications

(7 citation statements)

References 208 publications

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“…The most common form of treatment for stomach cancer is still traditional surgical resection ( 2 ). Although only approximately 30% of stomach cancer patients are thought to be suitable candidates for radical resection, the alarming truth is that the great majority of patients receive a diagnosis when the disease has already progressed ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning models based on quantitative dynamic contrast-enhanced MRI parameters assess the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes in advanced gastric carcinoma

Huang,

Li,

Wang

et al. 2024

Front. Oncol.

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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.

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Section: Introductionmentioning

confidence: 99%

Machine learning models based on quantitative dynamic contrast-enhanced MRI parameters assess the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes in advanced gastric carcinoma

Huang,

Li,

Wang

et al. 2024

Front. Oncol.

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“…Gastric cancer (GC) has more than one million new cases each year, 700,000 of which occur in developing countries, and is the fifth most common cause of the disease nationwide [ 1 , 2 ]. With the improved understanding of GC molecules in recent years, we have come to believe that cancer may have unique pathogenic mechanisms and abundant oncogenic pathways.…”

Section: Introductionmentioning

confidence: 99%

MFG-E8 promotes M2 polarization of macrophages and is associated with poor prognosis in patients with gastric cancer

Li,

Qiu,

Meng

et al. 2024

Heliyon

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“…Although the incidence and mortality of gastric cancer have declined over the past decades, gastric cancer remains the fourth most prevalent cancer worldwide [ 1 ]. Traditional surgical resection remains the main treatment for gastric cancer [ 2 ]. However, most patients with gastric cancer are usually diagnosed at an advanced stage, and thus less than 30% are suitable for radical resection [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Association between radiomics features of DCE-MRI and CD8+ and CD4+ TILs in advanced gastric cancer

Huang¹,

Li²,

Xia³

et al. 2023

Pathol. Oncol. Res.

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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.

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Recent Trends and Advancements in the Diagnosis and Management of Gastric Cancer

Cited by 12 publications

References 208 publications

Machine learning models based on quantitative dynamic contrast-enhanced MRI parameters assess the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes in advanced gastric carcinoma

Machine learning models based on quantitative dynamic contrast-enhanced MRI parameters assess the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes in advanced gastric carcinoma

MFG-E8 promotes M2 polarization of macrophages and is associated with poor prognosis in patients with gastric cancer

Association between radiomics features of DCE-MRI and CD8+ and CD4+ TILs in advanced gastric cancer

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