AIMC Topic: Gastrectomy
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Harnessing the machine learning and nomogram models: elevating prognostication in nonmetastatic gastric cancer with "double invasion" for personalized patient care.
European journal of medical research
Jun 23, 2025
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.
Transformer-based skeletal muscle deep-learning model for survival prediction in gastric cancer patients after curative resection.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Apr 15, 2025
BACKGROUND: We developed and evaluated a skeletal muscle deep-learning (SMDL) model using skeletal muscle computed tomography (CT) imaging to predict the survival of patients with gastric cancer (GC).
Comparative study of XGBoost and logistic regression for predicting sarcopenia in postsurgical gastric cancer patients.
Scientific reports
Apr 14, 2025
The use of machine learning (ML) techniques, particularly XGBoost and logistic regression, to predict sarcopenia among postsurgical gastric cancer patients has gained significant attention in recent research. Sarcopenia, characterized by the progress...
Randomized Trial on Electroacupuncture for Recovery of Postoperative Gastrointestinal Function Based on Long-Term Monitoring Device.
Annals of surgical oncology
Apr 4, 2025
BACKGROUND: This research aimed to explore the efficacy and safety of electroacupuncture in promoting the recovery of postoperative gastrointestinal function and to discuss the potential mechanism on the basis of heart rate variability (HRV).
Generation of surgical reports for lymph node dissection during laparoscopic gastric cancer surgery based on artificial intelligence.
International journal of computer assisted radiology and surgery
Apr 1, 2025
PURPOSE: This study aimed to develop an artificial intelligence (AI) model for the surgical report output of laparoscopic lymph node dissection in the suprapancreatic region during gastric cancer surgery.
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...
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...
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.
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) ...
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...
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