AIMC Topic: Carcinoembryonic Antigen

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The diagnostic value of serum cysteine protease inhibitor (CST4) in colorectal cancer: a preliminary study.

BMC gastroenterology
BACKGROUND: CST4 is associated with various cancers but its diagnostic value in colorectal cancer (CRC) has not been clearly established. This study aims to further validate the diagnostic value of CST4 in colorectal cancer using random forest models...

Identification and predictive machine learning model construction of gut microbiota associated with carcinoembryonic antigens in colorectal cancer.

mSphere
UNLABELLED: Carcinoembryonic antigen (CEA) is a critical colorectal cancer (CRC) biomarker, but its mechanistic link to gut microbiota remains unclear. This study characterized gut microbiota differences between high-CEA (H-CEA) and low-CEA (L-CEA) C...

Machine learning-based dynamic CEA trajectory and prognosis in gastric cancer.

BMC cancer
BACKGROUND: Static carcinoembryonic antigen (CEA) levels are well‑established prognostic markers in patients with gastric cancer, but the significance of their dynamic trajectories over time has rarely been reported.

Single-atom aptamer anchoring enables light-addressable multiplexed biosensing for early detection of pancreatic cancer.

Biosensors & bioelectronics
Pancreatic cancer remains one of the deadliest malignancies due to its silent progression and lack of early diagnostic tools. Here, we report a light-addressable photoelectrochemical (LAMT-PEC) biosensing system featuring single-atom Au-TiO photoelec...

Machine Learning-Assisted Multimodal Early Screening of Lung Cancer Based on a Multiplexed Laser-Induced Graphene Immunosensor.

ACS nano
Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis. Early detection is critical for improving patient outcomes, yet current screening methods, such as low-dose computed tomography (CT), of...

Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data.

Cancer control : journal of the Moffitt Cancer Center
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
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
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
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
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...