Radiomics and machine learning models for diagnosing microvascular invasion in cholangiocarcinoma: a systematic review and meta-analysis of diagnostic test accuracy studies.

Journal: Clinical imaging

PMID: 40088548

Abstract

PURPOSE: We aimed to systematically assess the value of radiomics/machine learning (ML) models for diagnosing microvascular invasion (MVI) in patients with cholangiocarcinoma (CCA) using various radiologic modalities.

Authors

Amir Mahmoud Ahmadzadeh

Department of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Nima Broomand Lomer

Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Drew A Torigian

Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.

Keywords

Bile Duct Neoplasms Cholangiocarcinoma Humans Machine Learning Microvessels Neoplasm Invasiveness Radiomics Sensitivity and Specificity

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View on PubMed Access via DOI PubMed (40088548)

Radiomics and machine learning models for diagnosing microvascular invasion in cholangiocarcinoma: a systematic review and meta-analysis of diagnostic test accuracy studies.

Abstract

Authors

Keywords

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