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Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided.

Methods

Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction.

Results

Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7–0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response.

Conclusions

Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.

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References

  1. Öberg K, Knigge U, Kwekkeboom D, Perren A. Neuroendocrine gastro-entero-pancreatic tumors: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2012.

  2. Lawrence B, Gustafsson BI, Chan A, Svejda B, Kidd M, Modlin IM. The epidemiology of gastroenteropancreatic neuroendocrine tumors. Endocrinol Metab Clin N Am. 2011.

  3. Kuo EJ, Salem RR. Population-level analysis of pancreatic neuroendocrine tumors 2 cm or less in size. Ann Surg Oncol. 2013.

  4. Cheema A, Weber J, Strosberg JR. Incidental detection of pancreatic neuroendocrine tumors: an analysis of incidence and outcomes. Ann Surg Oncol. 2012.

  5. Bettini R, Partelli S, Boninsegna L, Capelli P, Crippa S, Pederzoli P, et al. Tumor size correlates with malignancy in nonfunctioning pancreatic endocrine tumor. Surgery. 2011.

  6. Lloyd R V, Osamura YR, Kloppel G, Rosai J. WHO classification of tumours of endocrine organs. WHO Press; 2017.

  7. Nagtegaal ID, Odze RD, Klimstra D, Paradis V, Rugge M, Schirmacher P, et al. WHO classification of tumours of the digestive system. Histopathology. 2019;2020.

  8. Falconi M, Bartsch DK, Eriksson B, Klöppel G, Lopes JM, O’Connor JM, et al. ENETS consensus guidelines for the management of patients with digestive neuroendocrine neoplasms of the digestive system: well-differentiated pancreatic non-functioning tumors. Neuroendocrinology. 2012.

  9. Falconi M, Partelli S. Neuroendocrine tumours in 2016: Defining rules for increasingly personalized treatments. Nat Rev Clin Oncol. 2017.

  10. Hill JS, McPhee JT, McDade TP, Zhou Z, Sullivan ME, Whalen GF, et al. Pancreatic neuroendocrine tumors: the impact of surgical resection on survival. Cancer. 2009.

  11. Falconi M, Eriksson B, Kaltsas G, Bartsch DK, Capdevila J, Caplin M, et al. ENETS consensus guidelines update for the management of patients with functional pancreatic neuroendocrine tumors and non-functional pancreatic neuroendocrine tumors. Neuroendocrinology. 2016.

  12. Zhao Z, Bian Y, Jiang H, Fang X, Li J, Cao K, et al. CT-radiomic approach to predict G1/2 nonfunctional pancreatic neuroendocrine tumor. Acad Radiol. 2020.

  13. Strobel O, Hinz U, Gluth A, Hank T, Hackert T, Bergmann F, et al. Pancreatic adenocarcinoma: number of positive nodes allows to distinguish several N categories. Ann Surg. 2015.

  14. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016.

  15. Mori M, Benedetti G, Partelli S, Sini C, Andreasi V, Broggi S, et al. Ct radiomic features of pancreatic neuroendocrine neoplasms (panNEN) are robust against delineation uncertainty. Phys Medica. 2019;57:41–6.

    Article  Google Scholar 

  16. Loi S, Mori M, Benedetti G, Partelli S, Broggi S, Cattaneo GM, et al. Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms. Phys Medica. 2020;76:125–33.

    Article  Google Scholar 

  17. Gruzdev IS, Zamyatina KA, Tikhonova VS, Kondratyev E V, Glotov A V, Karmazanovsky GG, et al. Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms. Eur J Radiol [Internet]. Elsevier Ireland Ltd; 2020;109371. Available from: https://doi.org/10.1016/j.ejrad.2020.109371.

  18. Bian Y, Jiang H, Ma C, Wang L, Zheng J, Jin G, et al. CT-based radiomics score for distinguishing between grade 1 and grade 2 nonfunctioning pancreatic neuroendocrine tumors. AJR Am J Roentgenol. 2020;215:852–63.

    Article  Google Scholar 

  19. Gu D, Hu Y, Ding H, Wei J, Chen K, Liu H, et al. CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol. 2019;29:6880–90.

    Article  Google Scholar 

  20. Bian Y, Zhao Z, Jiang H, Fang X, Li J, Cao K, et al. Noncontrast radiomics approach for predicting grades of nonfunctional pancreatic neuroendocrine tumors. J Magn Reson Imaging. 2020;52:1124–36.

    Article  Google Scholar 

  21. Canellas R, Burk KS, Parakh A, Sahani DV. Prediction of pancreatic neuroendocrine tumor grade based on CT features and texture analysis. Am J Roentgenol. 2018.

  22. Guo C, Zhuge X, Wang Z, Wang Q, Sun K, Feng Z, et al. Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade. Abdom Radiol. 2019.

  23. Bian Y, Li J, Cao K, Fang X, Jiang H, Ma C, et al. Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs. Abdom Radiol [Internet]. Springer US; 2020; Available from: https://doi.org/10.1007/s00261-020-02706-0

  24. Choi TW, Kim JH, Yu MH, Park SJ, Han JK. Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol. 2018.

  25. Liang W, Yang P, Huang R, Xu L, Wang J, Liu W, et al. A combined nomogram model to preoperatively predict histologic grade in pancreatic neuroendocrine tumors. Clin Cancer Res. 2019.

  26. Gao X, Wang X. 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. 2019.

  27. Guo CG, Ren S, Chen X, Wang QD, Xiao WB, Zhang JF, et al. Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-t magnetic resonance. Cancer Manag Res. 2019;11:1933–44.

    Article  Google Scholar 

  28. Li J, Lu J, Liang P, Li A, Hu Y, Shen Y, et al. Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: using whole-tumor CT texture analysis as quantitative biomarkers. Cancer Med. 2018.

  29. Yu H, Huang Z, Li M, Wei Y, Zhang L, Yang C, et al. Differential diagnosis of nonhypervascular pancreatic neuroendocrine neoplasms from pancreatic ductal adenocarcinomas, based on computed tomography radiological features and texture analysis. Acad Radiol [internet]. Elsevier Inc.; 2020;27:332–41. Available from: https://doi.org/10.1016/j.acra.2019.06.012.

  30. van der Pol CB, Lee S, Tsai S, Larocque N, Alayed A, Williams P, et al. Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features. Abdom Radiol [Internet]. Springer US; 2019;44:992–9. Available from: https://doi.org/10.1007/s00261-018-01889-x.

  31. Shi YJ, Zhu HT, Liu YL, Wei YY, Qin XB, Zhang XY, et al. Radiomics analysis based on diffusion kurtosis imaging and T2 weighted imaging for differentiation of pancreatic neuroendocrine tumors from solid pseudopapillary tumors. Front Oncol. 2020;10:1–9.

    Article  Google Scholar 

  32. He M, Liu Z, Lin Y, Wan J, Li J, Xu K, et al. Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics. Eur J Radiol [internet]. Elsevier; 2019;117:102–11. Available from: https://doi.org/10.1016/j.ejrad.2019.05.024.

  33. Li X, Zhu H, Qian X, Chen N, Lin X. MRI texture analysis for differentiating nonfunctional pancreatic neuroendocrine neoplasms from solid pseudopapillary neoplasms of the pancreas. Acad Radiol. 2019.

  34. Guo C, Zhuge X, Wang Q, Xiao W, Wang Z, Wang Z, et al. The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis. Cancer Imaging. 2018;18:1–6.

    Article  CAS  Google Scholar 

  35. Reinert CP, Baumgartner K, Hepp T, Bitzer M, Horger M. 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 [Internet]. Springer US; 2020;45:750–8. Available from: https://doi.org/10.1007/s00261-020-02406-9.

  36. Lin X, Xu L, Wu A, Guo C, Chen X, Wang Z. Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography. Acta Radiol. 2018;60:553–60.

    Article  Google Scholar 

  37. Wang Z, Chen X, Wang J, Cui W, Ren S, Wang Z. Differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma based on CT texture analysis. Acta Radiol. 2019;61:595–604.

    Article  Google Scholar 

  38. Mapelli P, Partelli S, Salgarello M, Doraku J, Pasetto S, Rancoita PMV, et al. 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. 2020.

  39. Yano M, Misra S, Carpenter DH, Salter A, Hildebolt CF. Pancreatic neuroendocrine tumors: computed tomography enhancement, but not histological grade, correlates with tumor aggression. Pancreas. 2017.

  40. De Robertis R, Maris B, Cardobi N, Martini PT, Gobbo S, Capelli P, et al. Can histogram analysis of mr images predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol. 2018.

  41. Önner H, Abdülrezzak Ü, Tutuş A. 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? Nucl Med Commun. 2020:1034–9.

  42. Werner RA, Ilhan H, Lehner S, Papp L, Zsótér N, Schatka I, et al. Pre-therapy somatostatin receptor-based heterogeneity predicts overall survival in pancreatic neuroendocrine tumor patients undergoing peptide receptor radionuclide therapy. Mol Imaging Biol. 2019;21:582–90.

    Article  CAS  Google Scholar 

  43. Weber M, Kessler L, Schaarschmidt B, Fendler WP, Lahner H, Antoch G, et al. Treatment-related changes in neuroendocrine tumors as assessed by textural features derived from 68Ga-DOTATOC PET/MRI with simultaneous acquisition of apparent diffusion coefficient. BMC Cancer BMC Cancer. 2020;20:1–12.

    Article  Google Scholar 

  44. McGovern JM, Singhi AD, Borhani AA, Furlan A, McGrath KM, Zeh HJ, et al. CT radiogenomic characterization of the alternative lengthening of telomeres phenotype in pancreatic neuroendocrine tumors. Am J Roentgenol. 2018;211:1020–5.

    Article  Google Scholar 

  45. Afshar P, Mohammadi A, Plataniotis KN, Oikonomou A, Benali H. From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities. IEEE Signal Process Mag. 2019;36:132–60.

    Article  Google Scholar 

  46. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020.

  47. Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, Guido Gerig, (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage 31 (3):1116-1128

  48. Cook GJR, Azad G, Owczarczyk K, Siddique M, Goh V. Challenges and promises of PET radiomics. Int J Radiat Oncol Biol Phys. 2018.

  49. Larghi A, Capurso G, Carnuccio A, Ricci R, Alfieri S, Galasso D, et al. Ki-67 grading of nonfunctioning pancreatic neuroendocrine tumors on histologic samples obtained by EUS-guided fine-needle tissue acquisition: a prospective study. Gastrointest Endosc. 2012.

  50. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative adversarial networks: an overview. IEEE Signal Process Mag. 2018.

  51. Zuhir Bodalal, Stefano Trebeschi, Thi Dan Linh Nguyen-Kim, Winnie Schats, Regina Beets-Tan, (2019) Radiogenomics: bridging imaging and genomics. Abdominal Radiology 44 (6):1960-1984

  52. Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, et al. Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer. 2018.

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Author notes

  1. Bezzi C. and Mapelli P. equally contributed to the paper and therefore they are joint first authors.

Authors and Affiliations

  1. Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy

    C. Bezzi, P. Mapelli, S. Partelli, M. Falconi & M. Picchio

  2. Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy

    P. Mapelli, L. Presotto, I. Neri, P. Scifo, A. Savi, V. Bettinardi, L. Gianolli & M. Picchio

  3. Pancreatic Surgery Unit, Pancreas Translational & Clinical Research Centre, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy

    S. Partelli & M. Falconi

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Prof. Massimo Falconi is one of the Italian Advisory Board members of Advanced Accelerator Application (AAA).

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Bezzi, C., Mapelli, P., Presotto, L. et al. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging 48, 4002–4015 (2021). https://doi.org/10.1007/s00259-021-05338-8

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  • DOI: https://doi.org/10.1007/s00259-021-05338-8

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