AIMC Topic: Stomach Neoplasms
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Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features.
Journal of translational medicine
Jan 6, 2025
BACKGROUND: First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combined with chemotherapy. In clinical practice, HER2 positivity is identified through immunohistochemistr...
Machine Learning Prediction of Early Recurrence in Gastric Cancer: A Nationwide Real-World Study.
Annals of surgical oncology
Dec 30, 2024
BACKGROUND: Patients with gastric cancer (GC) who experience early recurrence (ER) within 2 years postoperatively have poor prognoses. This study aimed to analyze and predict ER after curative surgery for patients with GC using machine learning (ML) ...
Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer.
Scientific reports
Dec 28, 2024
Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed cell death (PCD) in cellular processes is critical, yet its specific function in GC is underexplored...
Machine learning-based prediction of duodenal stump leakage following laparoscopic gastrectomy for gastric cancer.
Surgery
Dec 27, 2024
BACKGROUND: Duodenal stump leakage is one of the most critical complications following gastrectomy surgery, with a high mortality rate. The present study aimed to establish a predictive model based on machine learning for forecasting the occurrence o...
Machine learning-based integration develops a disulfidptosis-related lncRNA signature for improving outcomes in gastric cancer.
Artificial cells, nanomedicine, and biotechnology
Dec 19, 2024
Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need for innovative prognostic methods. Disulfidptosis, a recently discovered programmed cell death trigger...
Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction.
Frontiers in immunology
Dec 13, 2024
BACKGROUND: Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously...
Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy.
Cell reports. Medicine
Dec 4, 2024
Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framew...
Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Dec 2, 2024
BACKGROUND: Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiated to address the variability in patient responses to combination chemotherapy, highlighting the nee...
Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT imaging: a multicenter study.
International journal of surgery (London, England)
Dec 1, 2024
INTRODUCTION: The postoperative recurrence of gastric cancer (GC) has a significant impact on the overall prognosis of patients. Therefore, accurately predicting the postoperative recurrence of GC is crucial.
-targeted AI-driven vaccines: a paradigm shift in gastric cancer prevention.
Frontiers in immunology
Nov 28, 2024
, a globally prevalent pathogen Group I carcinogen, presents a formidable challenge in gastric cancer prevention due to its increasing antimicrobial resistance and strain diversity. This comprehensive review critically analyzes the limitations of con...
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