AIMC Topic: CA-125 Antigen
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Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study.
Journal of ovarian research
Nov 6, 2024
OBJECTIVES: The study aimed to compare the diagnostic efficacy of the machine learning models with expert subjective assessment (SA) in assessing the malignancy risk of ovarian tumors using transvaginal ultrasound (TVUS).
Application of machine learning techniques in the diagnosis of endometriosis.
BMC women's health
Sep 5, 2024
OBJECTIVE: The aim of this study is to assess the use of machine learning methodologies in the diagnosis of endometriosis (EM).
Indirect reference interval estimation using a convolutional neural network with application to cancer antigen 125.
Scientific reports
Aug 20, 2024
Indirect methods for reference interval (RI) estimation, which use data acquired from routine pathology testing, have the potential to accelerate the establishment of RIs to account for variables such as gender and age to improve clinical assessments...
Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data.
Scientific data
Jun 25, 2024
Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow...
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.
Fragmentomics features of ovarian cancer.
International journal of cancer
May 20, 2024
Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an adv...
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...
Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane.
Biochemical and biophysical research communications
Jul 10, 2021
An early and accurate pregnancy diagnosis method is required to improve the reproductive performance of cows. Here we developed an easy pregnancy detection method using vaginal mucosal membrane (VMM) with application of Reverse Transcription-Loop-med...
Cattle
RNA-Binding Proteins
Molecular Diagnostic Techniques
Membrane Proteins
Nucleic Acid Amplification Techniques
Adaptor Proteins, Signal Transducing
Gene Expression
Mucous Membrane
Female
Reproducibility of Results
Machine Learning
Sensitivity and Specificity
Vagina
Cytokines
Animals
CA-125 Antigen
Biomarkers
Ubiquitins
Pregnancy
Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics.
PloS one
Dec 31, 2020
BACKGROUND: The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.
Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.
Journal of gynecologic oncology
Mar 11, 2019
OBJECTIVES: The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.
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