Significant advances have been made in the understanding of clinicopathologic prognostic factors for soft tissue sarcoma over the past decade. Foremost among these advances is an improved ability to recognize the subset of patients at high risk for recurrent disease based on clinicopathologic data available at the time of initial presentation. Progress has also helped to elucidate specific molecular factors that have independent prognostic significance. This review outlines the updated American Joint Committee on Cancer staging system for soft tissue sarcoma and summarizes the available data on traditional clinicopathologic and molecular prognostic factors.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/s1053-4296(99)80025-3 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Effects of miRNAs in inborn error of metabolism and treatment strategies.
Postgrad Med J
January 2025
Department of Pediatric Metabolic Diseases, University of Health Sciences, Ankara Etlik City Hospital, Ankara 06170, Turkey.
Metabolism is the name given to all of the chemical reactions in the cell involving thousands of proteins, including enzymes, receptors, and transporters. Inborn errors of metabolism (IEM) are caused by defects in the production and breakdown of proteins, fats, and carbohydrates. Micro ribonucleic acids (miRNAs) are short non-coding RNA molecules, ⁓19-25 nucleotides long, hairpin-shaped, produced from DNA.
View Article and Find Full Text PDFInvestigation of Anti-Apoptotic Effects and Mechanisms of Astragaloside IV in a Rat Model of Cerebral Ischemia-Reperfusion Injury.
CNS Neurosci Ther
January 2025
Qingshan Lake Science and Technology Innovation Center, Hangzhou Medical College, Hangzhou, China.
Background: Ischemic stroke is a prevalent and life-threatening cerebrovascular disease that is challenging to treat and associated with a poor prognosis. Astragaloside IV (AS-IV), a primary bioactive component of Astragali radix, has demonstrated neuroprotective benefits in previous studies. This study aimed to explore the mechanisms through which AS-IV may treat cerebral ischemia-reperfusion injury (CIRI).
View Article and Find Full Text PDFDiagnostic utility of magnetic resonance imaging in autoimmune encephalitis: prognostic implications.
Acta Radiol
January 2025
R Madhavan Nayar Center for Comprehensive Epilepsy Care, Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India.
Background: The role of imaging in autoimmune encephalitis (AIE) remains unclear, and there are limited data on the utility of magnetic resonance imaging (MRI) to diagnose, treat, or prognosticate AIE.
Purpose: To evaluate whether MRI is a diagnostic and prognostic marker for AIE and assess its efficacy in distinguishing between various AIE subtypes.
Material And Methods: We analyzed data from 96 AIE patients from our prospective autoimmune registry.
Artificial Intelligence Predicts Multiclass Molecular Signatures and Subtypes Directly From Breast Cancer Histology: a Multicenter Retrospective Study.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
View Article and Find Full Text PDFAI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review.
Hum Reprod Open
November 2024
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!