Skip to main content

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

European Radiology volume 29, pages 6880–6890 (2019)Cite this article

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.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

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

References

  1. Yao JC, Hassan M, Phan A et al (2008) One hundred years after “carcinoid”: epidemiology of and prognostic factors for neuroendocrine tumors in 35,825 cases in the United States. J Clin Oncol 26:3063–3072

    PubMed  PubMed Central  Article  Google Scholar 

  2. Ohmoto A, Rokutan H, Yachida S (2017) Pancreatic neuroendocrine neoplasms: basic biology, current treatment strategies and prospects for the future. Int J Mol Sci 18:143

    PubMed Central  Article  CAS  Google Scholar 

  3. Bosman FT, Carneiro F, Hruban RH, Theise ND (2010) WHO classification of tumours of the digestive system, 4th edn. International Agency for Research on Cancer, Lyon

    Google Scholar 

  4. Reid MD, Balci S, Saka B, Adsay NV (2014) Neuroendocrine tumors of the pancreas: current concepts and controversies. Endocr Pathol 25:65–79

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  5. Klimstra DS, Modlin IR, Coppola D, Lloyd RV, Suster S (2010) The pathologic classification of neuroendocrine tumors: a review of nomenclature, grading, and staging systems. Pancreas 39:707–712

    PubMed  Article  PubMed Central  Google Scholar 

  6. Scarpa A, Mantovani W, Capelli P et al (2010) Pancreatic endocrine tumors: improved TNM staging and histopathological grading permit a clinically efficient prognostic stratification of patients. Mod Pathol 23:824–833

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  7. Zhou C, Zhang J, Zheng Y, Zhu Z (2012) Pancreatic neuroendocrine tumors: a comprehensive review. Int J Cancer 131:1013–1022

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. Öberg K (2012) Neuroendocrine tumors of the digestive tract: impact of new classifications and new agents on therapeutic approaches. Curr Opin Oncol 24:433–440

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  9. Larghi A, Capurso G, Carnuccio A et al (2012) Ki-67 grading of nonfunctioning pancreatic neuroendocrine tumors on histologic samples obtained by EUS-guided fine-needle tissue acquisition: a prospective study. Gastrointest Endosc 76:570–577

    PubMed  Article  PubMed Central  Google Scholar 

  10. Horiguchi S, Kato H, Shiraha H et al (2017) Dynamic computed tomography is useful for prediction of pathological grade in pancreatic neuroendocrine neoplasm. J Gastroenterol Hepatol 32:925–931

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. De Robertis R, Cingarlini S, Martini PT et al (2017) Pancreatic neuroendocrine neoplasms: magnetic resonance imaging features according to grade and stage. World J Gastroenterol 23:275–285

    PubMed  PubMed Central  Article  Google Scholar 

  12. Kim JH, Eun HW, Kim YJ, Han JK, Choi BI (2013) Staging accuracy of MR for pancreatic neuroendocrine tumor and imaging findings according to the tumor grade. Abdom Imaging 38:1106–1114

    PubMed  Article  PubMed Central  Google Scholar 

  13. Jang KM, Kim SH, Lee SJ, Choi D (2014) The value of gadoxetic acid-enhanced and diffusion-weighted MRI for prediction of grading of pancreatic neuroendocrine tumors. Acta Radiol 55:140–148

    PubMed  Article  PubMed Central  Google Scholar 

  14. Besa C, Ward S, Cui Y, Jajamovich G, Kim M, Taouli B (2016) Neuroendocrine liver metastases: value of apparent diffusion coefficient and enhancement ratios for characterization of histopathologic grade. J Magn Reson Imaging 44:1432–1441

    PubMed  Article  PubMed Central  Google Scholar 

  15. Belousova E, Karmazanovsky G, Kriger A et al (2017) Contrast-enhanced MDCT in patients with pancreatic neuroendocrine tumours: correlation with histological findings and diagnostic performance in differentiation between tumour grades. Clin Radiol 72:150–158

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  16. Min JH, Kang TW, Kim YK et al (2018) Hepatic neuroendocrine tumour: apparent diffusion coefficient as a potential marker of prognosis associated with tumour grade and overall survival. Eur Radiol 28:2561–2571

    PubMed  Article  PubMed Central  Google Scholar 

  17. Hwang EJ, Lee JM, Yoon JH et al (2014) Intravoxel incoherent motion diffusion-weighted imaging of pancreatic neuroendocrine tumors: prediction of the histologic grade using pure diffusion coefficient and tumor size. Invest Radiol 49:396–402

    PubMed  Article  PubMed Central  Google Scholar 

  18. Choi TW, Kim JH, Yu MH, Park SJ, Han JK (2018) Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol 59:383–392

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  19. Canellas R, Burk KS, Parakh A, Sahani DV (2018) Prediction of pancreatic neuroendocrine tumor grade based on CT features and texture analysis. AJR Am J Roentgenol 210:341–346

    PubMed  Article  PubMed Central  Google Scholar 

  20. Guo C, Chen X, Xiao W, Wang Q, Sun K, Wang Z (2017) Pancreatic neuroendocrine neoplasms at magnetic resonance imaging: comparison between grade 3 and grade 1/2 tumors. Onco Targets Ther 10:1465

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Kim DW, Kim HJ, Kim KW et al (2015) Neuroendocrine neoplasms of the pancreas at dynamic enhanced CT: comparison between grade 3 neuroendocrine carcinoma and grade 1/2 neuroendocrine tumour. Eur Radiol 25:1375–1383

    PubMed  Article  PubMed Central  Google Scholar 

  22. Kang J, Ryu JK, Son JH et al (2018) Association between pathologic grade and multiphase computed tomography enhancement in pancreatic neuroendocrine neoplasm. J Gastroenterol Hepatol 33:1677–1682

    Article  Google Scholar 

  23. Zhao W, Quan Z, Huang X et al (2018) Grading of pancreatic neuroendocrine neoplasms using pharmacokinetic parameters derived from dynamic contrast-enhanced MRI. Oncol Lett 15:8349–8356

    PubMed  PubMed Central  Google Scholar 

  24. Lambin P, Leijenaar R, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749

    PubMed  PubMed Central  Article  Google Scholar 

  25. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Chen J, Tian J (2009) Real-time multi-modal rigid registration based on a novel symmetric-SIFT descriptor. Progress in Natural Science-Materials International 19:643-651

    Article  Google Scholar 

  27. Cameron A, Khalvati F, Haider MA, Wong A (2016) MAPS: a quantitative radiomics approach for prostate cancer detection. IEEE Trans Biomed Eng 63:1145–1156

    PubMed  Article  PubMed Central  Google Scholar 

  28. Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164

    PubMed  PubMed Central  Article  Google Scholar 

  29. Liu Z, Zhang XY, Shi YJ et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 23:7253–7262

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Elhalawani H, Kanwar A, Mohamed ASR et al (2018) Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients. Sci Rep 8:13

    Article  CAS  Google Scholar 

  31. Ger RB, Cardenas CE, Anderson BM, Yang J, Mackin DS, Zhang L (2018) Guidelines and experience using imaging biomarker explorer (IBEX) for Radiomics. J Vis Exp:e57132

  32. Zhang B, Tian J, Dong D et al (2017) Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma. Clin Cancer Res 23:4259–4269

    PubMed  Article  PubMed Central  Google Scholar 

  33. Ding JL, Xing ZY, Jiang ZX et al (2018) CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol 103:51–56

    PubMed  Article  Google Scholar 

  34. Huang X, Cheng Z, Huang Y et al (2018) CT-based radiomics signature to discriminate high-grade from Low-grade colorectal adenocarcinoma. Acad Radiol. https://doi.org/10.1016/j.acra.2018.01.020

    PubMed  Article  PubMed Central  Google Scholar 

  35. Tian Q, Yan LF, Zhang X et al (2018) Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging. https://doi.org/10.1002/jmri.26010

    PubMed  Article  PubMed Central  Google Scholar 

  36. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238

    PubMed  Article  PubMed Central  Google Scholar 

  37. Phuong TM, Lin Z, Altman RB (2005) Choosing SNPs using feature selection. Computational Systems Bioinformatics Conference 301–309

  38. Kramer AA, Zimmerman JE (2007) Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited. Crit Care Med 35:2052–2056

    PubMed  PubMed Central  Article  Google Scholar 

  39. Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak 26:565–574

    Article  Google Scholar 

  40. Hu Y, Rao S, Xu X, Tang Y, Zeng M (2018) Grade 2 pancreatic neuroendocrine tumors: overbroad scope of Ki-67 index according to MRI features. Abdom Radiol (NY). https://doi.org/10.1007/s00261-018-1573-5

    PubMed  Article  PubMed Central  Google Scholar 

  41. Kasajima A, Yazdani S, Sasano H (2015) Pathology diagnosis of pancreatic neuroendocrine tumors. J Hepatobiliary Pancreat Sci 22:586–593

    PubMed  Article  PubMed Central  Google Scholar 

  42. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  44. Horton KM, Hruban RH, Yeo C, Fishman EK (2006) Multi–detector row CT of pancreatic islet cell tumors. Radiographics 26:453–464

    PubMed  Article  PubMed Central  Google Scholar 

  45. Low G, Panu A, Millo N, Leen E (2011) Multimodality imaging of neoplastic and non-neoplastic solid lesions of the pancreas. Radiographics 31:993–1015

    PubMed  Article  PubMed Central  Google Scholar 

  46. Valle JW, Eatock M, Clueit B, Gabriel Z, Ferdinand R, Mitchell S (2014) "A systematic review of non-surgical treatments for pancreatic neuroendocrine tumours" (vol 40, pg 376, 2014). Cancer Treat Rev 40:1037–1037

    CAS  Article  Google Scholar 

Download references

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).

Author information

Author notes

  1. *Dongsheng Gu and Yabin Hu have contributed equally to this study.

Affiliations

  1. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 East Zhongguancun Road, Beijing, 100190, China

    Dongsheng Gu, Jingwei Wei & Jie Tian

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Dongsheng Gu, Jingwei Wei & Jie Tian

  3. Department of Radiology, Zhongshan Hospital, Fudan University and Shanghai Institute of Medical Imaging, 180 Fenglin Rd., Shanghai, 200032, China

    Yabin Hu & Mengsu Zeng

  4. Department of Radiology, Affiliated Hospital (Laoshan hospital) of Qingdao University, Qingdao, 266061, Shandong, China

    Yabin Hu & Hui Ding

  5. Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Ke Chen

  6. Department of Radiology, Central Hospital of ZiBo, Shandong, 255036, China

    Hao Liu

  7. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China

    Jie Tian

  8. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shanxi, 710126, China

    Jie Tian

Authors
  1. Dongsheng Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yabin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingwei Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ke Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mengsu Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jie Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mengsu Zeng or Jie Tian.

Ethics declarations

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

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 1238 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-019-06176-x

Keywords

  • Neoplasm grading
  • Pancreas
  • Neuroendocrine tumor
  • Radiomics
  • CT