AIMC Topic: Aged

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An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer.

Technology in cancer research & treatment
IntroductionTumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and...

Development and Validation of an Artificial Intelligence Digital Pathology Biomarker to Predict Benefit of Long-Term Hormonal Therapy and Radiotherapy in Men With High-Risk Prostate Cancer Across Multiple Phase III Trials.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Long-term androgen deprivation therapy (ADT) improves survival in men with high-risk localized prostate cancer (PCa) receiving radiotherapy (RT). Predictive biomarkers are needed to guide ADT duration.

Quantification of physiological crystalline Lens decentration using swept source OCT.

European journal of ophthalmology
PurposeTo quantify the decentration of the crystalline lens in a large Austrian adult cataractous and non-cataractous cohort and to predict decentration using biometric parameters.SettingKepler University Clinic, Linz, Austria.DesignRetrospective sin...

Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias.

The journal of prevention of Alzheimer's disease
BACKGROUND: Over 6 million patients in the United States are affected by Alzheimer's Disease and Related Dementias (ADRD). Early detection of ADRD can significantly improve patient outcomes through timely treatment.

Prediction of Tumor Budding Grading in Rectal Cancer Using a Multiparametric MRI Radiomics Combined with a 3D Vision Transformer Deep Learning Approach.

Academic radiology
RATIONALE AND OBJECTIVES: The objective is to assess the effectiveness of a multiparametric MRI radiomics strategy combined with a 3D Vision Transformer (ViT) deep learning (DL) model in predicting tumor budding (TB) grading in individuals diagnosed ...

Assessment of AI-accelerated T2-weighted brain MRI, based on clinical ratings and image quality evaluation.

European journal of radiology
OBJECTIVE: To compare clinical ratings and signal-to-noise ratio (SNR) measures of a commercially available Deep Learning-based MRI reconstruction method (T2) against conventional T2- turbo spin echo brain MRI (T2).

Comparison of CNNs and Transformer Models in Diagnosing Bone Metastases in Bone Scans Using Grad-CAM.

Clinical nuclear medicine
PURPOSE: Convolutional neural networks (CNNs) have been studied for detecting bone metastases on bone scans; however, the application of ConvNeXt and transformer models has not yet been explored. This study aims to evaluate the performance of various...

Quantitative Ischemic Lesions of Portable Low-Field Strength MRI Using Deep Learning-Based Super-Resolution.

Stroke
BACKGROUND: Deep learning-based synthetic super-resolution magnetic resonance imaging (SynthMRI) may improve the quantitative lesion performance of portable low-field strength magnetic resonance imaging (LF-MRI). The aim of this study is to evaluate ...

Histological tumor necrosis predicts decreased survival after neoadjuvant chemotherapy in head and neck squamous cell carcinoma.

Oral oncology
OBJECTIVE: Despite growing interest in neoadjuvant therapies, there are no methods to predict radio- (RT) or chemoradiotherapy (CRT) response in head and neck squamous cell carcinoma (HNSCC). The aim of this research was to study the effect of neoadj...