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Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade

  • Abdominal Radiology
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Abstract

Purpose

The aim of this single-center retrospective study is to assess whether contrast-enhanced computed tomography (CECT) radiomics analysis is predictive of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) grade based on the 2019 World Health Organization (WHO) classification and to establish a tumor grade (G) prediction model.

Material and methods

Preoperative CECT images of 78 patients with GEP-NENs were retrospectively reviewed and divided in two groups (G1–G2 in class 0, G3-NEC in class 1). A total of 107 radiomics features were extracted from each neoplasm ROI in CT arterial and venous phases acquisitions with 3DSlicer. Mann–Whitney test and LASSO regression method were performed in R for feature selection and feature reduction, in order to build the radiomic-based predictive model. The model was developed for a training cohort (75% of the total) and validated on the independent validation cohort (25%). ROC curves and AUC values were generated on training and validation cohorts.

Results

40 and 24 features, for arterial phase and venous phase, respectively, were found to be significant in class distinction. From the LASSO regression 3 and 2 features, for arterial phase and venous phase, respectively, were identified as suitable for groups classification and used to build the tumor grade radiomic-based prediction model. The prediction of the arterial model resulted in AUC values of 0.84 (95% CI 0.72–0.97) and 0.82 (95% CI 0.62–1) for the training cohort and validation cohort, respectively, while the prediction of the venous model yielded AUC values of 0.7877 (95% CI 0.6416–0.9338) and 0.6813 (95% CI 0.3933–0.9693) for the training cohort and validation cohort, respectively.

Conclusions

CT-radiomics analysis may aid in differentiating the histological grade for GEP-NENs.

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References

  1. Bilimoria KY, Tomlinson JS, Merkow RP, Stewart AK, Ko CY, Talamonti MS et al (2007) Clinicopathologic features and treatment trends of pancreatic neuroendocrine tumors: analysis of 9,821 patients. J Gastrointest Surg 11:1460–1469. https://doi.org/10.1007/S11605-007-0263-3

    Article  PubMed  Google Scholar 

  2. Ito T, Igarashi H, Nakamura K, Sasano H, Okusaka T, Takano K et al (2015) Epidemiological trends of pancreatic and gastrointestinal neuroendocrine tumors in Japan: a nationwide survey analysis. J Gastroenterol 50:58–64. https://doi.org/10.1007/S00535-014-0934-2

    Article  PubMed  Google Scholar 

  3. Granata V, Coppola F, Grassi R, Fusco R, Tafuto S, Izzo F et al (2021) Structured reporting of computed tomography in the staging of neuroendocrine neoplasms: a Delphi consensus proposal. Front Endocrinol (Lausanne). https://doi.org/10.3389/FENDO.2021.748944

    Article  PubMed  PubMed Central  Google Scholar 

  4. Fang JM, Shi J (2019) A clinicopathologic and molecular update of pancreatic neuroendocrine neoplasms with a focus on the new World Health Organization Classification. Arch Pathol Lab Med 143:1317–1326. https://doi.org/10.5858/ARPA.2019-0338-RA

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bicci E, Cozzi D, Ferrari R, Grazzini G, Pradella S, Miele V (2020) Pancreatic neuroendocrine tumours: spectrum of imaging findings. Gland Surg. 9:2215–24. https://doi.org/10.21037/GS-20-537

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chiti G, Grazzini G, Cozzi D, Danti G, Matteuzzi B, Granata V et al (2021) Imaging of pancreatic neuroendocrine neoplasms. Int J Environ Res Public Health. https://doi.org/10.3390/IJERPH18178895

    Article  PubMed  PubMed Central  Google Scholar 

  7. Danti G, Flammia F, Matteuzzi B, Cozzi D, Berti V, Grazzini G et al (2021) Gastrointestinal neuroendocrine neoplasms (GI-NENs): hot topics in morphological, functional, and prognostic imaging. Radiol Med 126:1497–1507. https://doi.org/10.1007/S11547-021-01408-X

    Article  PubMed  PubMed Central  Google Scholar 

  8. Nagtegaal ID, Odze RD, Klimstra D, Paradis V, Rugge M, Schirmacher P et al (2020) The 2019 WHO classification of tumours of the digestive system. Histopathology 76:182–188. https://doi.org/10.1111/HIS.13975

    Article  PubMed  Google Scholar 

  9. Garcia-Carbonero R, Sorbye H, Baudin E, Raymond E, Wiedenmann B, Niederle B et al (2016) ENETS consensus guidelines for high-grade gastroenteropancreatic neuroendocrine tumors and neuroendocrine carcinomas. Neuroendocrinology 103:186–194. https://doi.org/10.1159/000443172

    Article  CAS  PubMed  Google Scholar 

  10. Busico A, Maisonneuve P, Prinzi N, Pusceddu S, Centonze G, Garzone G et al (2020) Gastroenteropancreatic high-grade neuroendocrine neoplasms: histology and molecular analysis, two sides of the same coin. Neuroendocrinology 110:616–629. https://doi.org/10.1159/000503722

    Article  CAS  PubMed  Google Scholar 

  11. Dasari A, Shen C, Halperin D, Zhao B, Zhou S, Xu Y et al (2017) Trends in the incidence, prevalence, and survival outcomes in patients with neuroendocrine tumors in the United States. JAMA Oncol 3:1335–1342. https://doi.org/10.1001/JAMAONCOL.2017.0589

    Article  PubMed  PubMed Central  Google Scholar 

  12. Grazzini G, Danti G, Cozzi D, Lanzetta MM, Addeo G, Falchini M et al (2019) Diagnostic imaging of gastrointestinal neuroendocrine tumours (GI-NETs): relationship between MDCT features and 2010 WHO classification. Radiol Med 124:94–102. https://doi.org/10.1007/S11547-018-0946-8

    Article  PubMed  Google Scholar 

  13. Sorbye H, Welin S, Langer SW, Vestermark LW, Holt N, Osterlund P et al (2013) Predictive and prognostic factors for treatment and survival in 305 patients with advanced gastrointestinal neuroendocrine carcinoma (WHO G3): the NORDIC NEC study. Ann Oncol 24:152–160. https://doi.org/10.1093/ANNONC/MDS276

    Article  CAS  PubMed  Google Scholar 

  14. Milione M, Maisonneuve P, Spada F, Pellegrinelli A, Spaggiari P, Albarello L et al (2017) The clinicopathologic heterogeneity of grade 3 gastroenteropancreatic neuroendocrine neoplasms: morphological differentiation and proliferation identify different prognostic categories. Neuroendocrinology 104:85–93. https://doi.org/10.1159/000445165

    Article  CAS  PubMed  Google Scholar 

  15. Sorbye H, Baudin E, Borbath I, Caplin M, Chen J, Cwikla JB et al (2019) Unmet needs in high-grade gastroenteropancreatic neuroendocrine neoplasms (WHO G3). Neuroendocrinology 108:54–62. https://doi.org/10.1159/000493318

    Article  CAS  PubMed  Google Scholar 

  16. Sorbye H, Kong G, Grozinsky-Glasberg S (2020) PRRT in high-grade gastroenteropancreatic neuroendocrine neoplasms (WHO G3). Endocr Relat Cancer 27:R67-77. https://doi.org/10.1530/ERC-19-0400

    Article  CAS  PubMed  Google Scholar 

  17. Grassi R, Miele V, Giovagnoni A (2019) Artificial intelligence: a challenge for third millennium radiologist. Radiol Med 124:241–242. https://doi.org/10.1007/S11547-019-00990-5

    Article  PubMed  Google Scholar 

  18. Hassani C, Saremi F, Varghese BA, Duddalwar V (2020) Myocardial radiomics in cardiac MRI. AJR Am J Roentgenol 214:536–545. https://doi.org/10.2214/AJR.19.21986

    Article  PubMed  Google Scholar 

  19. Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E (2021) A deep look into radiomics. Radiol Med 126:1296–1311. https://doi.org/10.1007/S11547-021-01389-X

    Article  PubMed  PubMed Central  Google Scholar 

  20. Coppola F, Faggioni L, Regge D, Giovagnoni A, Golfieri R, Bibbolino C et al (2021) Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey. Radiol Med 126:63–71. https://doi.org/10.1007/S11547-020-01205-Y

    Article  PubMed  Google Scholar 

  21. Shiradkar R, Ghose S, Jambor I, Taimen P, Ettala O, Purysko AS et al (2018) Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings. J Magn Reson Imaging 48:1626–1636. https://doi.org/10.1002/JMRI.26178

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248. https://doi.org/10.1016/J.MRI.2012.06.010

    Article  PubMed  PubMed Central  Google Scholar 

  23. Canellas R, Burk KS, Parakh A, Sahani DV (2018) Prediction of pancreatic neuroendocrine tumor grade based on CT features and texture analysis. Am Roentgenol Ray Soc. 210:341–6. https://doi.org/10.2214/AJR.17.18417

    Article  Google Scholar 

  24. Cusumano D, Meijer G, Lenkowicz J, Chiloiro G, Boldrini L, Masciocchi C et al (2021) A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer. Radiol Med 126:421–429. https://doi.org/10.1007/S11547-020-01266-Z

    Article  PubMed  Google Scholar 

  25. Santone A, Brunese MC, Donnarumma F, Guerriero P, Mercaldo F, Reginelli A et al (2021) Radiomic features for prostate cancer grade detection through formal verification. Radiol Med 126:688–697. https://doi.org/10.1007/S11547-020-01314-8

    Article  PubMed  Google Scholar 

  26. Chiloiro G, Cusumano D, de Franco P, Lenkowicz J, Boldrini L, Carano D et al (2022) Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development. Radiol Med 127:11–20. https://doi.org/10.1007/S11547-021-01421-0

    Article  PubMed  Google Scholar 

  27. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446. https://doi.org/10.1016/J.EJCA.2011.11.036

    Article  PubMed  PubMed Central  Google Scholar 

  28. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/RADIOL.2015151169

    Article  PubMed  Google Scholar 

  29. Palatresi D, Fedeli F, Danti G, Pasqualini E, Castiglione F, Messerini L et al (2022) Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience. Radiol Med 127:117–128. https://doi.org/10.1007/S11547-021-01446-5

    Article  PubMed  Google Scholar 

  30. Karmazanovsky G, Gruzdev I, Tikhonova V, Kondratyev E, Revishvili A (2021) Computed tomography-based radiomics approach in pancreatic tumors characterization. Radiol Med 126:1388–1395. https://doi.org/10.1007/S11547-021-01405-0

    Article  Google Scholar 

  31. Sahani DV, Bonaffini PA, Fernández Del Castillo C, Blake MA (2013) Gastroenteropancreatic neuroendocrine tumors: role of imaging in diagnosis and management. Radiology. 266:38–61. https://doi.org/10.1148/RADIOL.12112512

    Article  PubMed  Google Scholar 

  32. Gregucci F, Fiorentino A, Mazzola R, Ricchetti F, Bonaparte I, Surgo A et al (2022) Radiomic analysis to predict local response in locally advanced pancreatic cancer treated with stereotactic body radiation therapy. Radiol Med 127:100–107. https://doi.org/10.1007/S11547-021-01422-Z

    Article  PubMed  Google Scholar 

  33. Neri E, Coppola F, Miele V, Bibbolino C, Grassi R (2020) Artificial intelligence: who is responsible for the diagnosis? Radiol Med 125:517–521. https://doi.org/10.1007/S11547-020-01135-9

    Article  PubMed  Google Scholar 

  34. Chen PT, Chang D, Yen H, Liu KL, Huang SY, Roth H et al (2021) Radiomic features at CT can distinguish pancreatic cancer from noncancerous pancreas. Radiol Imaging Cancer. https://doi.org/10.1148/RYCAN.2021210010

    Article  PubMed  PubMed Central  Google Scholar 

  35. del Re M, Cucchiara F, Rofi E, Fontanelli L, Petrini I, Gri N et al (2021) A multiparametric approach to improve the prediction of response to immunotherapy in patients with metastatic NSCLC. Cancer Immunol Immunother 70:1667–1678. https://doi.org/10.1007/S00262-020-02810-6

    Article  PubMed  Google Scholar 

  36. Asselin MC, O’Connor JPB, Boellaard R, Thacker NA, Jackson A (2012) Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer 48:447–455. https://doi.org/10.1016/J.EJCA.2011.12.025

    Article  PubMed  Google Scholar 

  37. Hu H, Shan Q, Chen S, Li B, Feng S, Xu E et al (2020) CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners. Radiol Med. 125:697–705. https://doi.org/10.1007/S11547-020-01174-2

    Article  PubMed  Google Scholar 

  38. Srisajjakul S, Prapaisilp P, Bangchokdee S (2020) CT and MR features that can help to differentiate between focal chronic pancreatitis and pancreatic cancer. Radiol Med 125:356–364. https://doi.org/10.1007/S11547-019-01132-7

    Article  PubMed  Google Scholar 

  39. Liang W, Yang P, Huang R, Xu L, Wang J, Liu W et al (2019) A combined nomogram model to preoperatively predict histologic grade in pancreatic neuroendocrine tumors. Clin Cancer Res 25:584–594. https://doi.org/10.1158/1078-0432.CCR-18-1305

    Article  PubMed  Google Scholar 

  40. Bian Y, Jiang H, Ma C, Wang L, Zheng J, Jin G et al (2020) CT-based radiomics score for distinguishing between grade 1 and grade 2 nonfunctioning pancreatic neuroendocrine tumors. AJR Am J Roentgenol 215:852–863. https://doi.org/10.2214/AJR.19.22123

    Article  PubMed  Google Scholar 

  41. Gu D, Hu Y, Ding H, Wei J, Chen K, Liu H et al (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol 29:6880–6890. https://doi.org/10.1007/S00330-019-06176-X

    Article  PubMed  Google Scholar 

  42. Zhao Z, Bian Y, Jiang H, Fang X, Li J, Cao K et al (2020) CT-radiomic approach to predict G1/2 nonfunctional pancreatic neuroendocrine tumor. Acad Radiol 27:e272–e281. https://doi.org/10.1016/J.ACRA.2020.01.002

    Article  PubMed  Google Scholar 

  43. Zilli A, Arcidiacono PG, Conte D, Massironi S (2018) Clinical impact of endoscopic ultrasonography on the management of neuroendocrine tumors: lights and shadows. Dig Liver Dis 50:6–14. https://doi.org/10.1016/J.DLD.2017.10.007

    Article  PubMed  Google Scholar 

  44. Benedetti G, Mori M, Panzeri MM, Barbera M, Palumbo D, Sini C et al (2021) CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors. Radiol Med 126:745–760. https://doi.org/10.1007/S11547-021-01333-Z

    Article  PubMed  Google Scholar 

  45. IARC Publications Website—WHO Classification of Tumours of the Digestive System [Internet]

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134, Florence, Italy

    Giuditta Chiti, Giulia Grazzini, Federica Flammia, Benedetta Matteuzzi, Silvia Pradella & Vittorio Miele

  2. Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy

    Giulia Grazzini & Silvia Pradella

  3. Medical Physics Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy

    Paolo Tortoli, Silvia Bettarini & Simone Busoni

  4. Department of Health and Sciences, Section of Pathological Anatomy, University of Florence, 50139, Florence, Italy

    Elisa Pasqualini & Daniela Massi

  5. Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS Di Napoli”, Via Mariano Semmola, 80131, Naples, Italy

    Vincenza Granata

  6. Department of Experimental and Clinical Medicine, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy

    Luca Messserini

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  1. Giuditta Chiti
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Contributions

Conceptualization was performed by GG and BM. Formal analysis was performed by GC, FF, PT and SB. Investigation was performed by GC and FF. Resources were collected by SB. Data curation was performed by GC, FF, EP and LM. GC, FF and GG were involved in writing—original draft preparation. GG was involved in writing—review and editing and project administration. Visualization was performed by GC, PT and SB. BM, VG, SP, DM and VM performed supervision. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Giulia Grazzini.

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Conflict of interest

The authors declare that they have no conflict of interest related to the publication of this article.

Ethical standards

Institutional Review Board Statement number 13261_oss date of approval February 02, 2021. The study was conducted according to the guidelines of the Declaration of Helsinki.

Informed consent

Informed consent was obtained from all subjects involved in the study. All patients gave their informed consent for the CT examination with intravenous administration. Patients’ records were anonymized and de-identified prior to analysis.

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Chiti, G., Grazzini, G., Flammia, F. et al. Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade. Radiol med 127, 928–938 (2022). https://doi.org/10.1007/s11547-022-01529-x

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