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European Radiology
Published in: European Radiology 10/2022

26-07-2022 | Magnetic Resonance Imaging | Oncology

GEP-NET radiomics: a systematic review and radiomics quality score assessment

Authors: Femke C. R. Staal, Else A. Aalbersberg, Daphne van der Velden, Erica A. Wilthagen, Margot E. T. Tesselaar, Regina G. H. Beets-Tan, Monique Maas

Published in: European Radiology | Issue 10/2022

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Abstract

Objective

The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence.

Methods

PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data.

Results

In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74–0.96 and AUCs 0.80–0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%.

Conclusion

Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce.

Key Points

• The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality.
• Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms.
• Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.
Appendix
Available only for authorised users
Literature
1.
Marusyk A, Almendro V, Polyak K (2012) Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer 12:323PubMed
2.
Marusyk A, Polyak K (2010) Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805:105–117
3.
Keck KJ, Choi A, Maxwell JE et al (2017) Increased grade in neuroendocrine tumor metastases negatively impacts survival. Ann Surg Oncol 24:2206–2212PubMedPubMedCentral
4.
Walter D, Harter PN, Battke F et al (2018) Genetic heterogeneity of primary lesion and metastasis in small intestine neuroendocrine tumors. Sci Rep 8:3811PubMedPubMedCentral
5.
Yang Z, Tang LH, Klimstra DS (2011) Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: implications for prognostic stratification. Am J Surg Pathol 35:853–860PubMed
6.
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577PubMed
7.
8.
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762PubMed
9.
Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006PubMed
10.
Limkin EJ, Sun R, Dercle L et al (2017) Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 28:1191–1206PubMed
11.
Ponsiglione A, Stanzione A, Cuocolo R et al (2022) Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol 32:2629–2638PubMed
12.
Spadarella G, Ugga L, Calareso G, Villa R, D’Aniello S, Cuocolo R (2022) The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment. Neuroradiology. https://​doi.​org/​10.​1007/​s00234-022-02959-0
13.
McInnes MDF, Moher D, Thombs BD et al (2018) Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA Statement. JAMA 319:388–396PubMed
14.
Azoulay A, Cros J, Vullierme MP et al (2020) Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma. Diagn Interv Imaging 101:821–830PubMed
15.
Beleù A, Rizzo G, De Robertis R et al (2020) Liver tumor burden in pancreatic neuroendocrine tumors: CT features and texture analysis in the prediction of tumor grade and (18)F-FDG uptake. Cancers (Basel) 12
16.
Benedetti G, Mori M, Panzeri MM et al (2021) CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors. Radiol Med 126:745–760
17.
Bevilacqua A, Calabro D, Malavasi S et al (2021) A [68Ga]Ga-DOTANOC PET/CT Radiomic model for non-invasive prediction of tumour grade in pancreatic neuroendocrine tumours. Diagnostics (Basel) 11.5: 870.
18.
Bian Y, Jiang H, Ma C et al (2020) CT-Based radiomics score for distinguishing between grade 1 and grade 2 nonfunctioning pancreatic neuroendocrine tumors. AJR Am J Roentgenol 215:852–863PubMed
19.
Bian Y, Li J, Cao K et al (2020) Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs. Abdom Radiol (NY) 46:667–680
20.
Bian Y, Zhao Z, Jiang H et al (2020) Noncontrast radiomics approach for predicting grades of nonfunctional pancreatic neuroendocrine tumors. J Magn Reson Imaging 52:1124–1136PubMedPubMedCentral
21.
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 210:341–346PubMed
22.
Chakraborty J, Pulvirenti A, Yamashita R et al (2018) Quantitative CT analysis for the preoperative prediction of pathologic grade in pancreatic neuroendocrine tumors. SPIE, Department of Surgery, United States Department of Radiology, United States Department of Epidemiology and Biostatistics, United States Department of Pathology, United States Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States
23.
Cheng SH, Jin ZY, Xue HD (2019) Evaluation of the histologic grade of pancreatic neuroendocrine tumors using CT texture analysis and perfusion parameters. Int J Clin Exp Med 12:771–779
24.
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
25.
D'Onofrio M, Ciaravino V, Cardobi N et al (2019) CT enhancement and 3D texture analysis of pancreatic neuroendocrine neoplasms. Sci Rep 9:2176PubMedPubMedCentral
26.
De Robertis R, Maris B, Cardobi N et al (2018) Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol 28:2582–2591PubMed
27.
Gao X, Wang X (2019) Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study. Int J Comput Assist Radiol Surg 14:1981–1991PubMed
28.
Gu D, Hu Y, Ding H et al (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol 29:6880–6890PubMed
29.
Guo C, Zhuge X, Wang Z et al (2019) Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade. Abdom Radiol (NY) 44:576–585
30.
Guo C-G, Ren S, Chen X et al (2019) Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance. Cancer Manag Res 11:1933–1944PubMedPubMedCentral
31.
Han X, Yang J, Luo J et al (2021) Application of CT-based radiomics in discriminating pancreatic cystadenomas from pancreatic neuroendocrine tumors using machine learning methods. Front Oncol 11:606677PubMedPubMedCentral
32.
He M, Liu Z, Lin Y et al (2019) Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics. Eur J Radiol 117:102–111PubMed
33.
Li J, Lu J, Liang P et al (2018) Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole-tumor CT texture analysis as quantitative biomarkers. Cancer Med 7:4924–4931PubMedPubMedCentral
34.
Li X, Zhu H, Qian X, Chen N, Lin X (2020) MRI texture analysis for differentiating nonfunctional pancreatic neuroendocrine neoplasms from solid pseudopapillary neoplasms of the pancreas. Acad Radiol 27:815–823PubMed
35.
Liang W, Yang P, Huang R et al (2019) A combined nomogram model to preoperatively predict histologic grade in pancreatic neuroendocrine tumors. Clin Cancer Res: 25(2): 584-594
36.
Lin X, Xu L, Wu A, Guo C, Chen X, Wang Z (2019) Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography. Acta Radiol 60:553–560PubMed
37.
38.
Luo Y, Chen X, Chen J et al (2020) Preoperative prediction of pancreatic neuroendocrine neoplasms grading based on enhanced computed tomography imaging: validation of deep learning with a convolutional neural network. Neuroendocrinology 110:338–350PubMed
39.
Mapelli P, Partelli S, Salgarello M et al (2020) Dual tracer 68Ga-DOTATOC and 18F-FDG PET/computed tomography radiomics in pancreatic neuroendocrine neoplasms: an endearing tool for preoperative risk assessment. Nucl Med Commun 41:896–905.
40.
41.
Pereira JAS, Rosado E, Bali M, Metens T, Chao S-L (2015) Pancreatic neuroendocrine tumors: correlation between histogram analysis of apparent diffusion coefficient maps and tumor grade. Abdom Imaging 40:3122–3128PubMed
42.
Pulvirenti A, Yamashita R, Chakraborty J et al (2021) Quantitative computed tomography image analysis to predict pancreatic neuroendocrine tumor grade. JCO Clin Cancer Inform 5:679–694PubMed
43.
Reinert CP, Baumgartner K, Hepp T, Bitzer M, Horger M (2020) Complementary role of computed tomography texture analysis for differentiation of pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumors in the portal-venous enhancement phase. Abdom Radiol (NY) 45:750–758
44.
Shi YJ, Zhu HT, Liu YL et al (2020) Radiomics analysis based on diffusion kurtosis imaging and T2 weighted imaging for differentiation of pancreatic neuroendocrine tumors from solid pseudopapillary tumors. Front Oncol 10:1624PubMedPubMedCentral
45.
Shindo T, Fukukura Y, Umanodan T et al (2016) Histogram analysis of apparent diffusion coefficient in differentiating pancreatic adenocarcinoma and neuroendocrine tumor. Medicine (Baltimore) 95:e2574
46.
Song C, Wang M, Luo Y et al (2021) Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images. Ann Transl Med 9:833PubMedPubMedCentral
47.
Song T, Zhang Q-W, Duan S-F et al (2021) MRI-based radiomics approach for differentiation of hypovascular non-functional pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms of the pancreas. BMC Med Imaging 21:36PubMedPubMedCentral
48.
van der Pol CB, Lee S, Tsai S et al (2019) Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features. Abdom Radiol (NY) 44:992–999
49.
Wang YW, Zhang XH, Wang BT et al (2019) Value of texture analysis of intravoxel incoherent motion parameters in differential diagnosis of pancreatic neuroendocrine tumor and pancreatic adenocarcinoma. Chin Med Sci J 34:1-9
50.
Wang Z, Chen X, Wang J, Cui W, Ren S, Wang Z (2020) Differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma based on CT texture analysis. Acta Radiol 61:595–604PubMed
51.
Werner RA, Ilhan H, Lehner S et al (2019) Pre-therapy somatostatin receptor-based heterogeneity predicts overall survival in pancreatic neuroendocrine tumor patients undergoing peptide receptor radionuclide therapy. Mol Imaging Biol 21:582–590PubMed
52.
Xu L, Yang P, Yen EA et al (2019) A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis. Phys Med Biol 64:215009PubMed
53.
Yu H, Huang Z, Li M et al (2020) Differential diagnosis of nonhypervascular pancreatic neuroendocrine neoplasms from pancreatic ductal adenocarcinomas, based on computed tomography radiological features and texture analysis. Acad Radiol 27:332–341PubMed
54.
Zhang T, Zhang Y, Liu X et al (2020) Application of radiomics analysis based on CT combined with machine learning in diagnostic of pancreatic neuroendocrine tumors patient’s pathological grades. Front Oncol 10:521831PubMed
55.
56.
Martini I, Polici M, Zerunian M et al (2020) CT texture analysis of liver metastases in PNETs versus NPNETs: correlation with histopathological findings. Eur J Radiol 124:108812PubMed
57.
Onner H, Abdulrezzak U, Tutus A (2020) Could the skewness and kurtosis texture parameters of lesions obtained from pretreatment Ga-68 DOTA-TATE PET/CT images predict receptor radionuclide therapy response in patients with gastroenteropancreatic neuroendocrine tumors? Nuclear Med Commun 41:1034–1039
58.
Liang P, Xu C, Tan F et al (2020) Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis. Cancer Med 10.2: 595-604
59.
Guo C, Zhuge X, Wang Q et al (2018) The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis. Cancer Imaging 18:37
60.
Wang R, Liu H, Liang P, Zhao H, Li L, Gao J (2021) Radiomics analysis of CT imaging for differentiating gastric neuroendocrine carcinomas from gastric adenocarcinomas. Eur J Radiol 138:109662PubMed
61.
Chetan MR, Gleeson FV (2021) Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Rad 31:1049–1058
62.
63.
Jamal-Hanjani M, Quezada SA, Larkin J, Swanton C (2015) Translational implications of tumor heterogeneity. Clin Cancer Res: 21:1258–1266
64.
Morris LG, Riaz N, Desrichard A et al (2016) Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 7:10051–10063PubMedPubMedCentral
65.
Fléjou JF (2011) WHO Classification of digestive tumors: the fourth edition. Ann Pathol 31:S27–S31PubMed
66.
Lloyd RV, Osamura RY, Kloppel G, Rosai J (2017) WHO classification of tumours of endocrine organs. 4th Edition, Volume 10
67.
Nagtegaal ID, Odze RD, Klimstra D et al (2020) The 2019 WHO classification of tumours of the digestive system. Histopathology 76:182-188
68.
Zwanenburg A, Leger S, Vallières M, Löck S (2016) Image biomarker standardisation initiative. arXiv:1612.07003
69.
Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardised quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338
70.
Song J, Yin Y, Wang H, Chang Z, Liu Z, Cui L (2020) A review of original articles published in the emerging field of radiomics. Eur J Radiol 127:108991PubMed
Metadata
Title
GEP-NET radiomics: a systematic review and radiomics quality score assessment
Authors
Femke C. R. Staal
Else A. Aalbersberg
Daphne van der Velden
Erica A. Wilthagen
Margot E. T. Tesselaar
Regina G. H. Beets-Tan
Monique Maas
Publication date
26-07-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 10/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08996-w

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