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  3. Prediction of synchronous distant metastasis of primary pancreatic ductal adenocarcinoma using the radiomics features derived from 18F-FDG PET and MRI.

Prediction of synchronous distant metastasis of primary pancreatic ductal adenocarcinoma using the radiomics features derived from 18F-FDG PET and MRI.

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DOI
10.48350/185051
Publisher DOI
10.1016/j.crad.2023.06.011
PubMed ID
37487840
Abstract
AIM

To explore the potential of the joint radiomics analysis of positron-emission tomography (PET) and magnetic resonance imaging (MRI) of primary tumours for predicting the risk of synchronous distant metastasis (SDM) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

18F-FDG PET and MRI images of PDAC patients from January 2011 to December 2020 were collected retrospectively. Patients (n=66) who received 18F-FDG PET/CT and MRI were included in a development group. Patients (n=25) scanned with hybrid PET/MRI were incorporated in an external test group. A radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm to select PET-MRI radiomics features of primary PDAC tumours. A radiomics nomogram was developed by combining the radiomics signature and important clinical indicators using univariate and multivariate analysis to assess patients' metastasis risk. The nomogram was verified with the employment of an external test group.

RESULTS

Regarding the development cohort, the radiomics nomogram was found to be better for predicting the risk of distant metastasis (area under the curve [AUC]: 0.93, sensitivity: 87%, specificity: 85%) than the clinical model (AUC: 0.70, p<0.001; sensitivity:70%, specificity: 65%) and the radiomics signature (AUC: 0.89, p>0.05; sensitivity: 65%, specificity:100%). Concerning the external test cohort, the radiomics nomogram yielded an AUC of 0.85.

CONCLUSION

PET-MRI based radiomics analysis exhibited effective prediction of the risk of SDM for preoperative PDAC patients and may offer complementary information and provide hints for cancer staging.
Date Issued
2023-10
Publication Type
Article
Subject(s)
600 Technology > 610 Medicine & health
Language(s)
en
Author(s)
Gao, J
Bai, Y
Miao, F
Huang, X
Schwaiger, M
Rominger, Axel Oliver  
Universitätsklinik für Nuklearmedizin  
Li, B
Zhu, H
Lin, X
Shi, Kuangyu  
Universitätsklinik für Nuklearmedizin  
Additional Credits
Universitätsklinik für Nuklearmedizin  
Journal
Clinical radiology
Publisher
Elsevier Science
ISSN
0009-9260
Access(Rights)
restricted
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