AIMC Topic: Middle Aged

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High density of TCF1+ stem-like tumor-infiltrating lymphocytes is associated with favorable disease-specific survival in NSCLC.

Frontiers in immunology
INTRODUCTION: Tumor-infiltrating lymphocytes are both prognostic and predictive biomarkers for immunotherapy response. However, less is known about the survival benefits oftheir subpopulations.

Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response.

Frontiers in immunology
INTRODUCTION: Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical inter...

The Influence of Blood Titanium Levels on DNA Damage in Brazilian Workers Occupationally Exposed to Different Chemical Agents.

Biological trace element research
Occupational exposure to pollutants may cause health-damaging effects in humans. Genotoxicity assays can be used to detect the toxic effects of pollutants. In the present study, we evaluated genetic damage in three populations occupationally exposed ...

Feasibility/clinical utility of half-Fourier single-shot turbo spin echo imaging combined with deep learning reconstruction in gynecologic magnetic resonance imaging.

Abdominal radiology (New York)
BACKGROUND: When antispasmodics are unavailable, the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER; called BLADE by Siemens Healthineers) or half Fourier single-shot turbo spin echo (HASTE) is clinically used...

A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.

Abdominal radiology (New York)
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.

Deep learning and radiomics-based vascular calcification characterization in dental cone beam computed tomography as a predictive tool for cardiovascular disease: a proof-of-concept study.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVES: This study evaluated an automated deep learning method for detecting calcifications in the extracranial and intracranial carotid arteries and vertebral arteries in cone beam computed tomography (CBCT) scans. Additionally, a model utilizin...

Generative Adversarial Network Based Contrast Enhancement: Synthetic Contrast Brain Magnetic Resonance Imaging.

Academic radiology
RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI) is a vital tool for diagnosing neurological disorders, frequently utilising gadolinium-based contrast agents (GBCAs) to enhance resolution and specificity. However, GBCAs present certain risk...

A Machine Learning Model to Predict De Novo Hepatocellular Carcinoma Beyond Year 5 of Antiviral Therapy in Patients With Chronic Hepatitis B.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIMS: This study aims to develop and validate a machine learning (ML) model predicting hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) patients after the first 5 years of entecavir (ETV) or tenofovir (TFV) therapy.

Textbook outcome in liver surgery for intrahepatic cholangiocarcinoma: defining predictors of an optimal postoperative course using machine learning.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: We sought to define textbook outcome in liver surgery (TOLS) for intrahepatic cholangiocarcinoma (ICC) by considering the implications of perioperative outcomes on overall survival (OS).