AIMC Topic: Prognosis

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Identification and Validation of Phagocytosis-Regulating Factors as Potential Prognostic and Diagnostic Biomarkers for Intracerebral Hemorrhage Through Single-Cell and Bulk Transcriptomic.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Intracerebral hemorrhage (ICH) is a major cause of death and disability worldwide. Despite treatment advances, reliable prognostic biomarkers are still lacking. While phagocytosis regulation is implicated in ICH pathogenesis, its potential for diagno...

Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.

Cancer research
UNLABELLED: Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. Whereas molecular data provide comprehensive insights, obtaining such data remains costly and time-consum...

The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis.

Annals of medicine
BACKGROUND: Accurately predicting hematoma enlargement (HE) is crucial for improving the prognosis of patients with cerebral haemorrhage. Artificial intelligence (AI) is a potentially reliable assistant for medical image recognition. This study syste...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Computational biology and chemistry
Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked differences in treatment strategies and prognoses for gliomas of different grades and molecular types. However, current glioma molecular subtyping systems ...

End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study.

European radiology
OBJECTIVES: Pancreatic cancer treatment plans involving surgery and/or chemotherapy are highly dependent on disease stage. However, current staging systems are ineffective and poorly correlated with survival outcomes. We investigate how artificial in...

A machine learning-based glycolysis and fatty acid metabolism-related prognostic signature is constructed and identified ACSL5 as a novel marker inhibiting the proliferation of breast cancer.

Computational biology and chemistry
INTRODUCTION: A new perspective on cancer metabolism suggests that it varies by context and is diverse. Cancer metabolism reprogramming can create a heterogeneous microenvironment that affects immune cell infiltration and function, complicating the s...

Prediction of Poor Visual Outcomes at Idiopathic Intracranial Hypertension Diagnosis Using a Supervised Machine Learning Algorithm.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: Idiopathic intracranial hypertension (IIH) is a vision-threatening disorder mainly affecting women of a reproductive age. Prompt diagnosis and intervention are vital to prevent vision loss, but validated tools to predict visual outcomes a...

Predicting survival outcomes in renal cell carcinoma spinal metastases: a multicenter evaluation of existing prognostic systems.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Survival prediction models for patients with spinal metastases are crucial for guiding clinical decision-making and optimizing treatment strategies. Renal cell carcinoma spinal metastases (RCC-SM) present unique challenges due to ...

Artificial intelligence predicts pregnancy complications based on cytokine profiles.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
BACKGROUND: Early prediction of pregnancy complications is important for adequate and timely prevention, management, and reducing maternal/fetal pathogenesis.