Research progress in predicting local invasion and distant metastasis of pancreatic ductal adenocarcinoma based on machine learning and radiomics
LI Sheng
RAN Xun
ZHOU Yutong
HAN Min
Abstract：Accurate prediction of local invasion and distant metastasis in pancreatic ductal adenocarcinoma(PDAC)is crucial for therapeutic decision-making and prognosis.Radiomics enables high-throughput extraction of quantitative features from medical images.When combined with machine learning algorithms,it can further explore tumor heterogeneity in PDAC and provide a new approach for the preoperative,noninvasive assessment of tumor invasion and metastasis.Machine learning-based radiomics has been applied to predict vascular invasion,perineural invasion,lymph node metastasis,and liver metastasis in PDAC.Its performance is often superior to that of conventional imaging evaluation methods and can assist clinicians in formulating personalized treatment strategies.This article reviews the research progress and challenges of machine learning-based radiomics in predicting local invasion and distant metastasis of PDAC.
Keywords：Pancreatic ductal adenocarcinomaLocal invasionDistant metastasisRadiomicsMachine learningTomographyX-ray computedMagnetic resonance imaging
Publication Date：2026-03-15
Online Publishing Date：2026-04-01(First online date of this platform, not the publication date of the document)
Pages：7( 202-208 )