BACKGROUND: Neoadjuvant chemotherapy (NAC) can improve the prognosis of patients with locally advanced gastric cancer (LAGC). However, precise models for accurate prognostic predictions are lacking. We aimed to utilize Cox regression and integrate va...
PURPOSE: In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastric junction (AEG), developing a preoperative CT-based prediction model for AEG invasion and lymph nod...
This study developed a 5-year survival prediction model for gastric cancer patients by combining radiomics and deep learning, focusing on CT-based 2D and 3D features of the iliopsoas and erector spinae muscles. Retrospective data from 705 patients ac...
OBJECTIVE: To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation...
OBJECTIVE: To develop and validate a machine learning framework combined with a nomogram for predicting recurrence after radical gastrectomy in patients with vascular and neural invasion.
Gastric cancer (GC) ranks as the fifth most commonly diagnosed malignancy and the fourth leading cause of cancer-related mortality worldwide. The integration of machine learning in the analysis of GC metabolomics data for biomarker identification rem...
BACKGROUND AND OBJECTIVE: Predicting overall survival (OS) for advanced gastric cancer patients after radiotherapy is critical for developing an individualized treatment plan. However, existing studies have focused on gastric cancer CT images with a ...
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Jun 17, 2025
BACKGROUND: Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have emerged as a valuable data modality, offering essential cellular and molecular insights wh...
Gastric cancer is one of the most common malignant tumors of the digestive system, with a high mortality rate due to late-stage diagnosis. Current clinical diagnosis relies on endoscopic biopsy and histopathological analysis, which are highly depende...
Computer methods and programs in biomedicine
Jun 4, 2025
OBJECTIVE: Evaluate the utility of a machine learning-based pathomics model in predicting overall survival (OS) post-surgery for gastric cancer patients.
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