AIMC Topic: Prognosis

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Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images.

Scientific reports
In computational pathology, extracting and representing spatial features from gigapixel whole slide images (WSIs) are fundamental tasks, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of analyzing WSIs...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

Scientific reports
The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study aimed to develop and validate a machine learning model using XGBoost to predict in-hospital mortality ...

[Clinical value of medical imaging artificial intelligence in the diagnosis and treatment of peritoneal metastasis in gastrointestinal cancers].

Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery
Peritoneal metastasis is a key factor in the poor prognosis of advanced gastrointestinal cancer patients. Traditional radiological diagnostic faces challenges such as insufficient sensitivity. Through technologies like radiomics and deep learning, ar...

Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis.

BMC pediatrics
OBJECTIVE: To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients.

Identification of molecular subtypes and a prognostic signature based on machine learning and purine metabolism-related genes in breast cancer.

Medicine
Breast cancer (BC), one of the most prevalent malignant tumors worldwide, lacks efficacious diagnostic biomarkers and therapeutic targets. This study harnesses multi-omics data to identify novel purine metabolism-related genes (PMRG) as potential bio...

Clinical prediction of pathological complete response in breast cancer: a machine learning study.

BMC cancer
BACKGROUND: This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy in patients with breast cancer patients.

Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis.

World journal of surgical oncology
BACKGROUND: As machine learning (ML) continuously develops in cancer diagnosis and treatment, some researchers have attempted to predict the expression of programmed death ligand-1 (PD-L1) in non-small cell lung cancer (NSCLC) by ML. However, there i...

Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer.

World journal of gastroenterology
BACKGROUND: Esophageal cancer is the sixth most common cancer worldwide, with a high mortality rate. Early prognosis of esophageal abnormalities can improve patient survival rates. The progression of esophageal cancer follows a sequence from esophagi...