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.
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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
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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).
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The scientific guarantor of this publication is Jie Tian.
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The authors declare that they have no competing interests.
Statistics and biometry
Dr. **gwei Wei from the University of Chinese Academy of Sciences, who is one of the authors, has significant statistical expertise.
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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.
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Institutional Review Board approval was obtained.
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• retrospective
• diagnostic or prognostic study
• multicenter study
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Gu, D., Hu, Y., Ding, H. et al. CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol 29, 6880–6890 (2019). https://doi.org/10.1007/s00330-019-06176-x
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DOI: https://doi.org/10.1007/s00330-019-06176-x