AIMC Topic: Prognosis

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When is imaging needed to assess the response to treatment in cardiac amyloidosis.

Current opinion in cardiology
PURPOSE OF REVIEW: Cardiac amyloidosis is characterized by systolic and diastolic abnormalities due to deposition of amyloid fibril within the myocardial extracellular space. Technological advances in multimodality cardiac imaging now helps in accura...

SurvGraph: A hybrid-graph attention network for survival prediction using whole slide pathological images in gastric cancer.

Neural networks : the official journal of the International Neural Network Society
Whole slide pathological images have shown significant potential for patient prognostication. Graph representation learning provides a robust framework for in-depth analysis of whole-slide images to construct predictive models. In this study, we intr...

The Development and Evaluation of a Convolutional Neural Network for Cutaneous Melanoma Detection in Whole Slide Images.

Archives of pathology & laboratory medicine
CONTEXT.—: The current melanoma staging system does not account for 26% of the variance seen in melanoma-specific survival, therefore our ability to predict patient outcome is not fully elucidated. Morphology may be of greater significance than in ot...

How to measure and model cardiovascular aging.

Cardiovascular research
Most acquired cardiovascular diseases are more common in older people, and the biological mechanisms and manifestations of aging provide insight into cardiovascular pathophysiology. Measuring aging within the cardiovascular system may help to better ...

Machine learning driven prediction of drug efficacy in lung cancer: based on protein biomarkers and clinical features.

Life sciences
Currently, chemotherapy drugs are the first-line treatment for lung cancer patients, and evaluating their efficacy is of utmost significance. However, assessing the clinical efficacy of chemotherapy drugs remains a challenging task. In recent years, ...

Development and Validation of Machine-Learning Algorithms to Predict the Onset of Depression Using Electronic Health Record Data: A Prognostic Modeling Study.

Studies in health technology and informatics
INTRODUCTION: Early detection and intervention are crucial for reducing the impacts of depression and associated healthcare costs. Few studies have used electronic health records (EHR) and machine learning (ML) with a longitudinal design to predict d...

Using Machine Learning Techniques for Lung Cancer Survival Prediction.

Studies in health technology and informatics
Lung cancer is one of the most common and lethal types of cancer. Early diagnosis and appropriate treatment play a crucial role in reducing mortality. Artificial intelligence techniques can be used to support clinical approaches to lung cancer, helpi...

Developing an Explainable Prognostic Model for Acute Ischemic Stroke: Combining Clinical and Inflammatory Biomarkers With Machine Learning.

Brain and behavior
BACKGROUND: Predicting the prognosis of patients with acute cerebral infarction (ACI) is crucial for clinical decision-making and personalized treatment. However, existing models often lack the comprehensive integration of clinical and biological ind...

Current Advances in Classification, Prediction and Management of Microvascular Invasion in Hepatocellular Carcinoma.

Journal of cellular and molecular medicine
Liver resection remains the mainstay curative treatment for hepatocellular carcinoma (HCC); however, the recurrence rate is reported to exceed 70% within 5 years after surgery. Microvascular invasion (MVI) has attracted great research interest in the...