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European Radiology

, Volume 29, Issue 12, pp 6880–6890 | Cite as

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

  • Dongsheng Gu
  • Yabin Hu
  • Hui Ding
  • Jingwei Wei
  • Ke Chen
  • Hao Liu
  • Mengsu ZengEmail author
  • Jie TianEmail author
Computer Applications

Abstract

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 < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950–0.998) in the training cohort and 0.902 (95% CI 0.798–1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram.

Conclusion

We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients.

Key Points

• Radiomic signature has strong discriminatory ability for the histologic grade of PNETs.

• Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading.

• The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.

Keywords

Neoplasm grading Pancreas Neuroendocrine tumor Radiomics CT 

Abbreviations

ACC

Accuracy

AFP

α-Fetoprotein

AUC

Area under the curve

CA199

Carbohydrate antigen 19-9

CEA

Carcinoembryonic antigen

CI

Confidence interval

CT

Computed tomography

DMPD

Dilatation of the main pancreatic duct

GLCM

Gray level co-occurrence matrix

GLDM

Gray level dependence matrix

GLRLM

Gray level run length matrix

GLSZM

Gray level size zone matrix

ICCs

Intra- and inter-class correlation coefficient

MR

Magnetic resonance

MRMR

Minimum redundancy maximum relevance

NGTDM

Neighboring gray tone difference matrix

NPV

Negative predictive value

PA

Pancreatic atrophy

PACS

Picture archiving and communication system

PBG

Preoperative blood glucose

PFP

Protrusion from the outline of the pancreas

PLM

Preoperative liver metastasis

PNETs

Pancreatic neuroendocrine tumors

PPV

Positive predictive value

RF

Random forest

ROC

Receiver operating characteristics

ROI

Region of interest

SENS

Sensitivity

SPEC

Specificity

WHO

World Health Organization

Notes

Funding

This study has received funding by the National Natural Science Foundation of China (No. 81227901, 81527805, 61231004, 81771924, 81501616), National Key Research and Development Program of China (2017YFA0205200, 2017YFC1308700), and the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Jie Tian.

Conflict of interest

The authors declare that they have no competing interests.

Statistics and biometry

Dr. Jingwei Wei from the University of Chinese Academy of Sciences, who is one of the authors, has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board of Zhongshan Hospital Affiliated to Shanghai Fudan University and Affiliated Hospital (Laoshan hospital) of Qingdao University.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

Supplementary material

330_2019_6176_MOESM1_ESM.docx (1.2 mb)
ESM 1 (DOCX 1238 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Department of Radiology, Zhongshan HospitalFudan University and Shanghai Institute of Medical ImagingShanghaiChina
  4. 4.Department of RadiologyAffiliated Hospital (Laoshan hospital) of Qingdao UniversityQingdaoChina
  5. 5.Department of Pathology, Zhongshan HospitalFudan UniversityShanghaiChina
  6. 6.Department of RadiologyCentral Hospital of ZiBoShandongChina
  7. 7.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of MedicineBeihang UniversityBeijingChina
  8. 8.Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and TechnologyXidian UniversityXi’anChina

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