AIMC Topic: Lymphocytes, Tumor-Infiltrating

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Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework.

Scientific reports
Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrating lymphocytes (TILs) are recognized as a key prognostic indicator and play a vital role in OSCC gr...

The global trends and distribution in tumor-infiltrating lymphocytes over the past 49 years: bibliometric and visualized analysis.

Frontiers in immunology
BACKGROUND: The body of research on tumor-infiltrating lymphocytes (TILs) is expanding rapidly; yet, a comprehensive analysis of related publications has been notably absent.

Value of the combination of intraepithelial tumor-infiltrating lymphocyte density and the heterogeneity of density as a prognostic marker in stage III colorectal cancers.

Histology and histopathology
Tumor-infiltrating lymphocyte (TIL) density is both a prognostic and a predictive factor in colorectal cancer (CRC). Whether the heterogeneity of TIL density across the tumor plays an important role in the clinical outcome of CRC is not well known. A...

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-driven estimation of mutational burden highlights DNAH5 as a prognostic marker in colorectal cancer.

Biology direct
BACKGROUND: Tumor Mutational Burden (TMB) have emerged as pivotal predictive biomarkers in determining prognosis and response to immunotherapy in colorectal cancer (CRC) patients. While Whole Exome Sequencing (WES) stands as the gold standard for TMB...

Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis.

Scientific reports
The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, focusing on early immune infiltration. Feature selection was performed on The Cancer Genome Atlas (TCGA...

Prediction of CD8+T lymphocyte infiltration levels in gastric cancer from contrast-enhanced CT and clinical factors using machine learning.

Medical physics
BACKGROUND: CD8+ T lymphocyte infiltration is closely associated with the prognosis and immunotherapy response of gastric cancer (GC). For now, the examination of CD8 infiltration levels relies on endoscopic biopsy, which is invasive and unsuitable f...

Artificial Intelligence-Based Histopathological Subtyping of High-Grade Serous Ovarian Cancer.

The American journal of pathology
Four subtypes of ovarian high-grade serous carcinoma (HGSC) have previously been identified, each with different prognoses and drug sensitivities. However, the accuracy of classification depended on the assessor's experience. This study aimed to deve...