Open AccessTubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion
Author(s) -
Aytül Hande Yardımcı,
Burak Koçak,
Ceyda Turan Bektaş,
İpek Sel,
Enver Yarıkkaya,
Nevra Dursun,
Hasan Bektaş,
Çiğdem Usul Afşar,
Rıza Umar Gürsu,
Özgür Kılıçkesmez
Publication year - 2020
Publication title -
diagnostic and interventional radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.754
H-Index - 43
eISSN - 1305-3612
pISSN - 1305-3825
DOI - 10.5152/dir.2020.19507
Subject(s) - medicine , gastric adenocarcinoma , lymphovascular invasion , perineural invasion , adenocarcinoma , texture (cosmology) , radiology , artificial intelligence , cancer , metastasis , computer science , image (mathematics)
Lymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis in gastric cancers. In this work, we aimed to investigate the potential role of computed tomography (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoma (GAC) using a machine learning (ML) approach.