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European Radiology
Published in: European Radiology 12/2019

01-12-2019 | Computed Tomography | Computer Applications

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

Authors: Dongsheng Gu, Yabin Hu, Hui Ding, Jingwei Wei, Ke Chen, Hao Liu, Mengsu Zeng, Jie Tian

Published in: European Radiology | Issue 12/2019

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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.
Appendix
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Literature
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 26:3063–3072PubMedCrossRef
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 18:143PubMedCentralCrossRef
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
4.
Reid MD, Balci S, Saka B, Adsay NV (2014) Neuroendocrine tumors of the pancreas: current concepts and controversies. Endocr Pathol 25:65–79PubMedCrossRef
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 39:707–712PubMedCrossRef
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 23:824–833PubMedCrossRef
7.
Zhou C, Zhang J, Zheng Y, Zhu Z (2012) Pancreatic neuroendocrine tumors: a comprehensive review. Int J Cancer 131:1013–1022PubMedCrossRef
8.
Öberg K (2012) Neuroendocrine tumors of the digestive tract: impact of new classifications and new agents on therapeutic approaches. Curr Opin Oncol 24:433–440PubMedCrossRef
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 76:570–577PubMedCrossRef
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 32:925–931PubMedCrossRef
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 23:275–285PubMedPubMedCentralCrossRef
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 38:1106–1114PubMedCrossRef
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 55:140–148PubMedCrossRef
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 44:1432–1441PubMedCrossRef
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 72:150–158PubMedCrossRef
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 28:2561–2571PubMedCrossRef
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 49:396–402PubMedCrossRef
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 59:383–392PubMedCrossRef
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 210:341–346PubMedCrossRef
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 10:1465PubMedPubMedCentralCrossRef
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 25:1375–1383PubMedCrossRef
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 33:1677–1682CrossRef
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 15:8349–8356PubMedPubMedCentral
24.
Lambin P, Leijenaar R, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749PubMedCrossRef
25.
Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006PubMedCrossRef
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 19:643-651CrossRef
27.
Cameron A, Khalvati F, Haider MA, Wong A (2016) MAPS: a quantitative radiomics approach for prostate cancer detection. IEEE Trans Biomed Eng 63:1145–1156PubMedCrossRef
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 34:2157–2164PubMedCrossRef
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 23:7253–7262PubMedCrossRef
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 8:13CrossRef
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 23:4259–4269PubMedCrossRef
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 103:51–56PubMedCrossRef
34.
35.
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 27:1226–1238PubMedCrossRef
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 35:2052–2056PubMedCrossRef
39.
Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak 26:565–574CrossRef
40.
41.
Kasajima A, Yazdani S, Sasano H (2015) Pathology diagnosis of pancreatic neuroendocrine tumors. J Hepatobiliary Pancreat Sci 22:586–593PubMedCrossRef
42.
43.
44.
Horton KM, Hruban RH, Yeo C, Fishman EK (2006) Multi–detector row CT of pancreatic islet cell tumors. Radiographics 26:453–464PubMedCrossRef
45.
Low G, Panu A, Millo N, Leen E (2011) Multimodality imaging of neoplastic and non-neoplastic solid lesions of the pancreas. Radiographics 31:993–1015PubMedCrossRef
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 40:1037–1037CrossRef
Metadata
Title
CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study
Authors
Dongsheng Gu
Yabin Hu
Hui Ding
Jingwei Wei
Ke Chen
Hao Liu
Mengsu Zeng
Jie Tian
Publication date
01-12-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 12/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-019-06176-x

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