AIMC Topic: Prognosis

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Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy.

Frontiers in immunology
BACKGROUND: Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, met...

Plasma Epstein-Barr Virus DNA load for diagnostic and prognostic assessment in intestinal Epstein-Barr Virus infection.

Frontiers in cellular and infection microbiology
BACKGROUND: The prospective application of plasma Epstein-Barr virus (EBV) DNA load as a noninvasive measure of intestinal EBV infection remains unexplored. This study aims to identify ideal threshold levels for plasma EBV DNA loads in the diagnosis ...

ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism.

Neural networks : the official journal of the International Neural Network Society
Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced...

Machine learning-based 28-day mortality prediction model for elderly neurocritically Ill patients.

Computer methods and programs in biomedicine
BACKGROUND: The growing population of elderly neurocritically ill patients highlights the need for effective prognosis prediction tools. This study aims to develop and validate machine learning (ML) models for predicting 28-day mortality in intensive...

Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment.

Journal of translational medicine
INTRODUCTION: Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment de...

Anti-ceramide antibody and sphingosine-1-phosphate as potential biomarkers of unresectable non-small cell lung cancer.

Pathology oncology research : POR
OBJECTIVES: Spingosine-1-phosphate (S1P) and ceramides are bioactive sphingolipids that influence cancer cell fate. Anti-ceramide antibodies might inhibit the effects of ceramide. The aim of this study was to assess the potential role of circulating ...

Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to develop and validate machine learning (ML) models utilizing positron emission tomography (PET)-habitat of the tumor and its peritumoral microenvironment to predict progression-free survival (PFS) in patie...

Dual-path neural network extracts tumor microenvironment information from whole slide images to predict molecular typing and prognosis of Glioma.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Utilizing AI to mine tumor microenvironment information in whole slide images (WSIs) for glioma molecular subtype and prognosis prediction is significant for treatment. Existing weakly-supervised learning frameworks based on...

Assessing the feasibility and external validity of natural language processing-extracted data for advanced lung cancer patients.

Lung cancer (Amsterdam, Netherlands)
BACKGROUND: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with ad...