Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models

Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models

PURPOSEThis study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI).METHODSPre-treatment MRI images of patients with cervical cancer were collected retr...

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Bibliographic Details
Main Authors: Okan İnce, Emre Uysal, Görkem Durak, Suzan Önol, Binnur Dönmez Yılmaz, Şükrü Mehmet Ertürk, Hakan Önder
Format: Article
Language:English
Published: Galenos Publishing House 2023-05-01
Series:Diagnostic and Interventional Radiology
Subjects:
artificial intelligence
human papillomavirus dna tests
machine learning
radiology
uterine cervical neoplasms
Online Access: http://www.dirjournal.org/archives/archive-detail/article-preview/prediction-of-carcinogenic-human-papillomavirus-ty/57629

Internet

http://www.dirjournal.org/archives/archive-detail/article-preview/prediction-of-carcinogenic-human-papillomavirus-ty/57629

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