AIMC Topic: Stomach Neoplasms
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Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer.
BMC cancer
Feb 7, 2025
BACKGROUND: Gastric cancer patients are prone to lower extremity deep vein thrombosis (DVT) after surgery, which is an important cause of death in postoperative patients. Therefore, it is particularly important to find a suitable way to predict the r...
Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.
Scientific reports
Feb 4, 2025
Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited nu...
Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.
Journal of cancer research and clinical oncology
Feb 3, 2025
OBJECTIVE: The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to...
Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.
Artificial intelligence in medicine
Feb 1, 2025
Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be taken by radiographers, thu...
Predicting early recurrence in locally advanced gastric cancer after gastrectomy using CT-based deep learning model: a multicenter study.
International journal of surgery (London, England)
Feb 1, 2025
BACKGROUND: Early recurrence in patients with locally advanced gastric cancer (LAGC) portends aggressive biological characteristics and a dismal prognosis. Predicting early recurrence may help determine treatment strategies for LAGC. The goal is to d...
Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma.
Scientific reports
Jan 30, 2025
Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microR...
Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.
Annals of surgical oncology
Jan 28, 2025
BACKGROUND: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).
Non-invasive Assessment of Human Epidermal Growth Factor Receptor 2 Expression in Gastric Cancer Based on Deep Learning: A Computed Tomography-based Multicenter Study.
Academic radiology
Jan 26, 2025
RATIONALE AND OBJECTIVES: The expression of human epidermal growth factor receptor 2 (HER2) in gastric cancer is closely associated with its treatment outcomes and prognosis. This study aims to develop and validate a HER2 prediction model based on co...
Harnessing Machine Learning and Nomogram Models to Aid in Predicting Progression-Free Survival for Gastric Cancer Patients Post-Gastrectomy with Deficient Mismatch Repair(dMMR).
BMC cancer
Jan 24, 2025
OBJECTIVE: To assess the effectiveness of a machine learning framework and nomogram in predicting progression-free survival (PFS) post-radical gastrectomy in patients with dMMR.
Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images.
Journal of translational medicine
Jan 23, 2025
Accurate and fast histological diagnosis of cancers is crucial for successful treatment. The deep learning-based approaches have assisted pathologists in efficient cancer diagnosis. The remodeled microenvironment and field cancerization may enable th...
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