AIMC Topic: Keratin-19

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Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole-slide images.

Cancer medicine May 12, 2020
BACKGROUND: It is unclear whether clinical factors and immune microenvironment (IME) factors are associated with tumor mutation burden (TMB) in patients with nonsmall cell lung cancer (NSCLC).
Adenocarcinoma Carcinoma, Non-Small-Cell Lung B7-H1 Antigen Antigens, Neoplasm Mutation Forkhead Transcription Factors Lymphocytes, Tumor-Infiltrating Regression Analysis Tumor Microenvironment Male Middle Aged Lung Neoplasms Aged, 80 and over Adult Aged Ex-Smokers Non-Smokers Exome Sequencing Keratin-19 Carcinoembryonic Antigen Smokers Humans Machine Learning Female Carcinoma, Squamous Cell
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Prediction of Immunohistochemistry of Suspected Thyroid Nodules by Use of Machine Learning-Based Radiomics.

AJR. American journal of roentgenology Aug 28, 2019
The purpose of this study was to develop and validate a radiomics model for evaluating immunohistochemical characteristics in patients with suspected thyroid nodules. A total of 103 patients (training cohort-to-validation cohort ratio, ≈ 3:1) with ...
Humans Machine Learning Tomography, X-Ray Computed Male Iodide Peroxidase Female Aged Middle Aged Galectin 3 Biomarkers Adult Reproducibility of Results Keratin-19 Thyroidectomy Retrospective Studies Immunohistochemistry Thyroid Nodule
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Highly sensitive detection of multiple tumor markers for lung cancer using gold nanoparticle probes and microarrays.

Analytica chimica acta Dec 26, 2016
Assay of multiple serum tumor markers such as carcinoembryonic antigen (CEA), cytokeratin 19 fragment antigen (CYFRA21-1), and neuron specific enolase (NSE), is important for the early diagnosis of lung cancer. Dickkopf-1 (DKK1), a novel serological ...
Carcinoembryonic Antigen Keratin-19 Lung Neoplasms Antigens, Neoplasm Biomarkers, Tumor Gold Humans Hydrogen Peroxide Metal Nanoparticles
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[A deep learning model based on magnetic resonance imaging and clinical feature fusion for predicting preoperative cytokeratin 19 status in hepatocellular carcinoma].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University Sep 20, 2024
OBJECTIVE: To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging (MRI) deep learning features with clinical features for preoperative prediction of cytokeratin 19 (CK19) status of hepatocellular carcin...
Middle Aged Deep Learning Female Male Magnetic Resonance Imaging Liver Neoplasms Humans Carcinoma, Hepatocellular Retrospective Studies Keratin-19
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