Article

Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological images

2024-04-08

Abstract excerpt

<title>Abstract</title> <p>Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds significant importance in enhancing patient treatment and prognosis. Machine learning technology and bioinformatics have provided novel approaches for cancer diagnosis. This study aims to develop a CRC diagnostic model based on immunohistochemical...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 894d276b-5ee0-5e14-9c13-d8c2b2c70d6b
DOI: 10.21203/rs.3.rs-4129792/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological imagesDOI 10.21203/rs.3.rs-4129792/v1Read

Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics GenesSemantic proximity Read

Machine Learning-Based Classifiers To Predict Metastasis In Colorectal Cancer PatientsSemantic proximity Read

Identifying metastatic biomarkers of colorectal cancer: Machine learning modeling and experimental validationSemantic proximity Read

Machine learning models for predicting lymph node and distant metastases in colorectal cancerSemantic proximity Read

Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosisSemantic proximity Read

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial IntelligenceSemantic proximity Read

Comprehensive clinical analysis of AKR1 expression profiles in the diagnosis of human colorectal cancerSemantic proximity Read

Multi gene mutation signatures in colorectal cancer patients: predict for the diagnosis, pathological classification, staging and prognosis.Semantic proximity Read

Integrative Analysis of Histopathological Images and Genomic Data in Colon AdenocarcinomaSemantic proximity Read

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.Semantic proximity Read

Novel Artificial Intelligence Combining Convolutional Neural Network and Support Vector Machine to Predict Colorectal Cancer Prognosis and Mutational Signatures From Hematoxylin and Eosin Images.Semantic proximity Read

Predicting biomarkers from classifier for liver metastasis of colorectal adenocarcinomas using machine learning modelsSemantic proximity Read