AIMC Topic: Carcinoembryonic Antigen
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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data.
Cancer control : journal of the Moffitt Cancer Center
May 7, 2025
IntroductionEarly diagnosis of colorectal cancer (CRC) poses a significant clinical challenge. This study aims to develop machine learning (ML) models for CRC risk prediction using clinical laboratory data.MethodsThis retrospective, single-center stu...
Simultaneous detection of trace protein biomarkers from a single drop of blood using AI-enhanced smartphone-based digital microscopy.
Biosensors & bioelectronics
Feb 13, 2025
The detection of early-stage diseases is often impeded by the low concentrations of protein biomarkers, necessitating sophisticated and costly technologies. In response, we have developed an advanced cyber-physical system that integrates blood plasma...
Detection of carcinoembryonic antigen specificity using microwave biosensor with machine learning.
Biosensors & bioelectronics
Nov 2, 2024
Early diagnosis and screening of tumor markers are essential for effective cancer treatment and improve the treatment efficiency and prognosis of tumor recurrence and metastasis. In this study, a split-ring resonator (SRR) circuit based on an interdi...
Enhancing the diagnostic accuracy of colorectal cancer through the integration of serum tumor markers and hematological indicators with machine learning algorithms.
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Jun 20, 2024
BACKGROUND: Colorectal cancer has a high incidence and mortality rate due to a low rate of early diagnosis. Therefore, efficient diagnostic methods are urgently needed.
Machine learning-based delta check method for detecting misidentification errors in tumor marker tests.
Clinical chemistry and laboratory medicine
Dec 14, 2023
OBJECTIVES: Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limita...
Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning.
Computational and mathematical methods in medicine
May 4, 2022
This study was aimed at two image segmentation methods of three-dimensional (3D) U-shaped network (U-Net) and multilevel boundary sensing residual U-shaped network (RUNet) and their application values on the auxiliary diagnosis of lung cancer. In thi...
Cuprous oxide coated silver/graphitic carbon nitride/cadmium sulfide nanocomposite heterostructure: Specific recognition of carcinoembryonic antigen through sandwich-type mechanism.
Journal of colloid and interface science
Nov 22, 2021
The development of the effective diagnostic method for the determination of cancer biomarkers is one of the most promising strategies for early clinical diagnosis of cancer. Here, based on the preparation of heterogeneous cuprous oxide coated silver ...
Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer.
Journal of healthcare engineering
May 31, 2021
In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnos...
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).
Antigens, Neoplasm
Adenocarcinoma
Carcinoma, Non-Small-Cell Lung
Lymphocytes, Tumor-Infiltrating
Regression Analysis
Mutation
Forkhead Transcription Factors
Machine Learning
Carcinoembryonic Antigen
Female
Non-Smokers
Male
Carcinoma, Squamous Cell
Middle Aged
Aged
Smokers
Lung Neoplasms
Aged, 80 and over
Humans
Exome Sequencing
Adult
B7-H1 Antigen
Keratin-19
Tumor Microenvironment
Ex-Smokers
Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions.
Scientific reports
May 3, 2019
The diagnosis of pancreatic cystic lesions remains challenging. This study aimed to investigate the diagnostic ability of carcinoembryonic antigen (CEA), cytology, and artificial intelligence (AI) by deep learning using cyst fluid in differentiating ...
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