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Construction of a prognostic signature based on T-helper 17 cells differentiation-related genes for predicting survival and tumor microenvironment in head and neck squamous cell carcinoma.
Medicine (Baltimore)
January 2025
Department of Otolaryngology, Hangzhou Red Cross Hospital (Zhejiang Hospital of Integrated Traditional Chinese and Western Medicine), Hangzhou, Zhejiang, China.
T-helper 17 (Th17) cells significantly influence the onset and advancement of malignancies. This study endeavor focused on delineating molecular classifications and developing a prognostic signature grounded in Th17 cell differentiation-related genes (TCDRGs) using machine learning algorithms in head and neck squamous cell carcinoma (HNSCC). A consensus clustering approach was applied to The Cancer Genome Atlas-HNSCC cohort based on TCDRGs, followed by an examination of differential gene expression using the limma package.
View Article and Find Full Text PDFDevelopment and validation of a radiomics-visual evoked potential nomogram for preoperative prediction of visual outcome after endoscopic craniopharyngioma resection.
J Neurosurg
January 2025
Departments of1Neurosurgery.
Objective: Craniopharyngiomas are rare, benign brain tumors that are primarily treated with surgery. Although the extended endoscopic endonasal approach (EEEA) has evolved as a more reliable surgical alternative and yields better visual outcomes than traditional craniotomy, postoperative visual deterioration remains one of the most common complications, and relevant risk factors are still poorly defined. Hence, identifying risk factors and developing a predictive model for postoperative visual deterioration is indeed necessary.
View Article and Find Full Text PDFOne hundred thirty-four germ line PU.1 variants and the agammaglobulinemic patients carrying them.
Blood
January 2025
Division of Immunology and Allergy, Children's Hospital of Philadelphia; Department of Pediatrics, Perelman School of Medicine; Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Leukopoiesis is lethally arrested in mice lacking the master transcriptional regulator PU.1. Depending on the animal model, subtotal PU.
View Article and Find Full Text PDFDelta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
JCO Clin Cancer Inform
January 2025
Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.
View Article and Find Full Text PDFMachine Learning to Predict Mortality in Older Patients With Cancer: Development and External Validation of the Geriatric Cancer Scoring System Using Two Large French Cohorts.
J Clin Oncol
January 2025
INSERM, IMRBU955, Univ Paris Est Créteil, Créteil, France.
Purpose: Establishing an accurate prognosis remains challenging in older patients with cancer because of the population's heterogeneity and the current predictive models' reduced ability to capture the complex interactions between oncologic and geriatric predictors. We aim to develop and externally validate a new predictive score (the Geriatric Cancer Scoring System [GCSS]) to refine individualized prognosis for older patients with cancer during the first year after a geriatric assessment (GA).
Materials And Methods: Data were collected from two French prospective multicenter cohorts of patients with cancer 70 years and older, referred for GA: ELCAPA (training set January 2007-March 2016) and ONCODAGE (validation set August 2008-March 2010).
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