Literature DB >> 32002592

Imaging phenotype using 18F-fluorodeoxyglucose positron emission tomography-based radiomics and genetic alterations of pancreatic ductal adenocarcinoma.

Chae Hong Lim1, Young Seok Cho1, Joon Young Choi1, Kyung-Han Lee1, Jong Kyun Lee2, Ji Hye Min3, Seung Hyup Hyun4.   

Abstract

PURPOSE: This study aimed to determine if major gene mutations including in KRAS, SMAD4, TP53, and CDKN2A were related to imaging phenotype using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)-based radiomics in patients with pancreatic ductal adenocarcinoma (PDAC).
METHODS: Data on 48 PDAC patients with pretreatment FDG PET/CT who underwent genomic analysis of their tumor tissue were retrospectively analyzed. A total of 35 unique quantitative radiomic features were extracted from PET images, including imaging phenotypes such as pixel intensity, shape, and textural features. Targeted exome sequencing using a customized cancer panel was used for genomic analysis. To assess the predictive performance of genetic alteration using PET-based radiomics, areas under the receiver operating characteristic curve (AUC) were used.
RESULTS: Mutation frequencies were KRAS 87.5%, TP53 70.8%, SMAD4 25.0%, and CDKN2A 18.8%. KRAS gene mutations were significantly associated with low-intensity textural features, including long-run emphasis (AUC = 0.806), zone emphasis (AUC = 0.794), and large-zone emphasis (AUC = 0.829). SMAD4 gene mutations showed significant relationships with standardized uptake value skewness (AUC = 0.727), long-run emphasis (AUC = 0.692), and high-intensity textural features such as run emphasis (AUC = 0.775), short-run emphasis (AUC = 0.736), zone emphasis (AUC = 0.750), and short-zone emphasis (AUC = 0.725). No significant associations were seen between the imaging phenotypes and genetic alterations in TP53 and CDKN2A.
CONCLUSION: Genetic alterations of KRAS and SMAD4 had significant associations with FDG PET-based radiomic features in PDAC. PET-based radiomics may help clinicians predict genetic alteration status in a noninvasive way.

Entities:  

Keywords:  FDG PET/CT; Gene mutation; Genetic alteration; Pancreatic cancer; Radiomics

Mesh:

Substances:

Year:  2020        PMID: 32002592     DOI: 10.1007/s00259-020-04698-x

Source DB:  PubMed          Journal:  Eur J Nucl Med Mol Imaging        ISSN: 1619-7070            Impact factor:   9.236


  9 in total

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9.  Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection.

Authors:  Xiawei Li; Litao Yang; Zheping Yuan; Jianyao Lou; Yiqun Fan; Aiguang Shi; Junjie Huang; Mingchen Zhao; Yulian Wu
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  9 in total

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