CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study

Objective To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs). Methods One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the t...

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Bibliographic Details
Published in:European radiology 2019-12, Vol.29 (12), p.6880-6890
Main Authors: Gu, Dongsheng, Hu, Yabin, Ding, Hui, Wei, Jingwei, Chen, Ke, Liu, Hao, Zeng, Mengsu, Tian, Jie
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Calibration
Clinical decision making
Computed tomography
Computer Applications
Decision analysis
Decision making
Decision trees
Diagnostic Radiology
Feature extraction
Female
Humans
Imaging
Internal Medicine
Interventional Radiology
Male
Medicine
Medicine & Public Health
Middle Aged
Neoplasm Grading
Neuroendocrine tumors
Neuroendocrine Tumors - pathology
Neuroradiology
Nomograms
Pancreas
Pancreatic cancer
Pancreatic Neoplasms - pathology
Portal Vein - pathology
Predictions
Radiology
Radiomics
Redundancy
Retrospective Studies
Risk analysis
Risk Factors
Tomography, X-Ray Computed - methods
Training
Tumors
Ultrasound
Young Adult
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Summary:Objective To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs). Methods One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use. Results The fusion radiomic signature has significant association with histologic grade ( p  
ISSN:0938-7994
1432-1084
DOI:10.1007/s00330-019-06176-x