Skip to main content

Advertisement

Log in

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential

  • Abdominal Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Background

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs) are the most common pancreatic cystic tumors and have a low risk of malignant transformation. Features able to early identify high-risk BD-IPMNs are lacking, and guidelines currently rely on the occurrence of worrisome features (WF) and high-risk stigmata (HRS).

Aim

In our study, we aimed to use a magnetic resonance imaging (MRI) radiomic model to identify features linked to a higher risk of malignant degeneration, and whether these appear before the occurrence of WF and HRS.

Methods

We retrospectively evaluated adult patients with a known BD-IPMN who had had at least two contrast-enhanced MRI studies at our center and a 24-month minimum follow-up time. MRI acquisition protocol for the two examinations included pre- and post-contrast phases and diffusion-weighted imaging (DWI)/apparent diffusion coefficient (ADC) map. Patients were divided into two groups according to the development of WF or HRS at the end of the follow-up (Group 0 = no WF or HRS; Group 1 = WF or HRS). We segmented the MRI images and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model.

Results

We included 50 patients: 31 in Group 0 and 19 in Group 1. No patients in this cohort developed HRS. At baseline, 47, 67, 38, and 68 SF were identified for pre-contrast T1-weighted (T1-W) sequence, post-contrast T1-W sequence, T2-weighted (T2- W) sequence, and ADC map, respectively. At the end of follow-up, we found 69, 78, 53, and 91 SF, respectively. The radiomic-based predictive model identified 16 SF: more particularly, 5 SF for pre-contrast T1-W sequence, 6 for post-contrast T1-W sequence, 3 for T2-W sequence, and 2 for ADC.

Conclusion

We identified radiomic features that correlate significantly with WF in patients with BD-IPMNs undergoing contrast-enhanced MRI. Our MRI-based radiomic model can predict the occurrence of WF.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Klöppel G, Basturk O, Schlitter AM, Konukiewitz B, Esposito I (2014) Intraductal neoplasms of the pancreas. Semin Diagn Pathol 31(6):452–466. https://doi.org/10.1053/j.semdp.2014.08.005

    Article  PubMed  Google Scholar 

  2. Grazzini G, Danti G, Cozzi D, Lanzetta MM, Addeo G, Falchini M, Masserelli A, Pradella S, Miele V (2019) Diagnostic imaging of gastrointestinal neuroendocrine tumours (GI-NETs): relationship between MDCT features and 2010 WHO classification. Radiol Med 124(2):94–102. https://doi.org/10.1007/s11547-018-0946-8

    Article  PubMed  Google Scholar 

  3. Danti G, Flammia F, Matteuzzi B, Cozzi D, Berti V, Grazzini G, Pradella S, Recchia L, Brunese L, Miele V (2021) Gastrointestinal neuroendocrine neoplasms (GI-NENs): hot topics in morphological, functional, and prognostic imaging. Radiol Med 126(12):1497–1507. https://doi.org/10.1007/s11547-021-01408-x

    Article  PubMed  PubMed Central  Google Scholar 

  4. Chiti G, Grazzini G, Flammia F, Matteuzzi B, Tortoli P, Bettarini S, Pasqualini E, Granata V, Busoni S, Messserini L, Pradella S, Massi D, Miele V (2022) Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade. Radiol Med 127(9):928–938. https://doi.org/10.1007/s11547-022-01529-x

    Article  PubMed  Google Scholar 

  5. Addeo G, Beccani D, Cozzi D, Ferrari R, Lanzetta MM, Paolantonio P, Pradella S, Miele V (2019) Groove pancreatitis: a challenging imaging diagnosis. Gland Surg 8(Suppl 3):S178-s187. https://doi.org/10.21037/gs.2019.04.06

    Article  PubMed  PubMed Central  Google Scholar 

  6. Moris M, Bridges MD, Pooley RA, Raimondo M, Woodward TA, Stauffer JA, Asbun HJ, Wallace MB (2016) Association between advances in high-resolution cross-section imaging technologies and increase in prevalence of pancreatic cysts from 2005 to 2014. Clin Gastroenterol Hepatol 14(4):585-593.e583. https://doi.org/10.1016/j.cgh.2015.08.038

    Article  PubMed  Google Scholar 

  7. Tanaka M, Fernández-Del Castillo C, Kamisawa T, Jang JY, Levy P, Ohtsuka T, Salvia R, Shimizu Y, Tada M, Wolfgang CL (2017) Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas. Pancreatology 17(5):738–753. https://doi.org/10.1016/j.pan.2017.07.007

    Article  PubMed  Google Scholar 

  8. Berland LL, Silverman SG, Gore RM, Mayo-Smith WW, Megibow AJ, Yee J, Brink JA, Baker ME, Federle MP, Foley WD, Francis IR, Herts BR, Israel GM, Krinsky G, Platt JF, Shuman WP, Taylor AJ (2010) Managing incidental findings on abdominal CT: white paper of the ACR incidental findings committee. J Am Coll Radiol 7(10):754–773. https://doi.org/10.1016/j.jacr.2010.06.013

    Article  PubMed  Google Scholar 

  9. Granata V, Grassi R, Fusco R, Setola SV, Palaia R, Belli A, Miele V, Brunese L, Petrillo A, Izzo F (2020) Assessment of ablation therapy in pancreatic cancer: the radiologist’s challenge. Front Oncol 10:560952. https://doi.org/10.3389/fonc.2020.560952

    Article  PubMed  PubMed Central  Google Scholar 

  10. Goh BK, Thng CH, Tan DM, Low AS, Wong JS, Cheow PC, Chow PK, Chung AY, Wong WK, Ooi LL (2014) Evaluation of the Sendai and 2012 international consensus guidelines based on cross-sectional imaging findings performed for the initial triage of mucinous cystic lesions of the pancreas: a single institution experience with 114 surgically treated patients. Am J Surg 208(2):202–209. https://doi.org/10.1016/j.amjsurg.2013.09.031

    Article  PubMed  Google Scholar 

  11. Nguyen AH, Toste PA, Farrell JJ, Clerkin BM, Williams J, Muthusamy VR, Watson RR, Tomlinson JS, Hines OJ, Reber HA, Donahue TR (2015) Current recommendations for surveillance and surgery of intraductal papillary mucinous neoplasms may overlook some patients with cancer. J Gastrointest Surg 19(2):258–265. https://doi.org/10.1007/s11605-014-2693-z

    Article  PubMed  Google Scholar 

  12. Grassi R, Miele V, Giovagnoni A (2019) Artificial intelligence: a challenge for third millennium radiologist. Radiol Med 124(4):241–242. https://doi.org/10.1007/s11547-019-00990-5

    Article  PubMed  Google Scholar 

  13. Hassani C, Saremi F, Varghese BA, Duddalwar V (2020) Myocardial radiomics in cardiac MRI. AJR Am J Roentgenol 214(3):536–545. https://doi.org/10.2214/ajr.19.21986

    Article  PubMed  Google Scholar 

  14. Neri E, Coppola F, Miele V, Bibbolino C, Grassi R (2020) Artificial intelligence: who is responsible for the diagnosis? Radiol Med 125(6):517–521. https://doi.org/10.1007/s11547-020-01135-9

    Article  PubMed  Google Scholar 

  15. Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E (2021) A deep look into radiomics. Radiol Med 126(10):1296–1311. https://doi.org/10.1007/s11547-021-01389-x

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cusumano D, Meijer G, Lenkowicz J, Chiloiro G, Boldrini L, Masciocchi C, Dinapoli N, Gatta R, Casà C, Damiani A, Barbaro B, Gambacorta MA, Azario L, De Spirito M, Intven M, Valentini V (2021) A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer. Radiol Med 126(3):421–429. https://doi.org/10.1007/s11547-020-01266-z

    Article  PubMed  Google Scholar 

  17. Santone A, Brunese MC, Donnarumma F, Guerriero P, Mercaldo F, Reginelli A, Miele V, Giovagnoni A, Brunese L (2021) Radiomic features for prostate cancer grade detection through formal verification. Radiol Med 126(5):688–697. https://doi.org/10.1007/s11547-020-01314-8

    Article  PubMed  Google Scholar 

  18. Chiloiro G, Cusumano D, de Franco P, Lenkowicz J, Boldrini L, Carano D, Barbaro B, Corvari B, Dinapoli N, Giraffa M, Meldolesi E, Manfredi R, Valentini V, Gambacorta MA (2022) Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development. Radiol Med 127(1):11–20. https://doi.org/10.1007/s11547-021-01421-0

    Article  PubMed  Google Scholar 

  19. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30(9):1234–1248. https://doi.org/10.1016/j.mri.2012.06.010

    Article  PubMed  PubMed Central  Google Scholar 

  20. Palatresi D, Fedeli F, Danti G, Pasqualini E, Castiglione F, Messerini L, Massi D, Bettarini S, Tortoli P, Busoni S, Pradella S, Miele V (2022) Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience. Radiol Med 127(2):117–128. https://doi.org/10.1007/s11547-021-01446-5

    Article  PubMed  Google Scholar 

  21. Gregucci F, Fiorentino A, Mazzola R, Ricchetti F, Bonaparte I, Surgo A, Figlia V, Carbonara R, Caliandro M, Ciliberti MP, Ruggieri R, Alongi F (2022) Radiomic analysis to predict local response in locally advanced pancreatic cancer treated with stereotactic body radiation therapy. Radiol Med 127(1):100–107. https://doi.org/10.1007/s11547-021-01422-z

    Article  PubMed  Google Scholar 

  22. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures they are data. Radiology 278(2):563–577. https://doi.org/10.1148/radiol.2015151169

    Article  PubMed  Google Scholar 

  23. Fusco R, Granata V, Mazzei MA, Meglio ND, Roscio DD, Moroni C, Monti R, Cappabianca C, Picone C, Neri E, Coppola F, Montanino A, Grassi R, Petrillo A, Miele V (2021) Quantitative imaging decision support (QIDS(TM)) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan. Cancer Control 28:1073274820985786. https://doi.org/10.1177/1073274820985786

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kuwahara T, Hara K, Mizuno N, Okuno N, Matsumoto S, Obata M, Kurita Y, Koda H, Toriyama K, Onishi S, Ishihara M, Tanaka T, Tajika M, Niwa Y (2019) Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary mucinous neoplasms of the pancreas. Clin Transl Gastroenterol 10(5):1–8. https://doi.org/10.14309/ctg.0000000000000045

    Article  CAS  PubMed  Google Scholar 

  25. Cui S, Tang T, Su Q, Wang Y, Shu Z, Yang W, Gong X (2021) Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study. Cancer Imaging 21(1):26. https://doi.org/10.1186/s40644-021-00395-6

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hanania AN, Bantis LE, Feng Z, Wang H, Tamm EP, Katz MH, Maitra A, Koay EJ (2016) Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget 7(52):85776–85784. https://doi.org/10.18632/oncotarget.11769

    Article  PubMed  PubMed Central  Google Scholar 

  27. Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, Bellomi M (2018) Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2(1):36. https://doi.org/10.1186/s41747-018-0068-z

    Article  PubMed  PubMed Central  Google Scholar 

  28. Sahora K, Mino-Kenudson M, Brugge W, Thayer SP, Ferrone CR, Sahani D, Pitman MB, Warshaw AL, Lillemoe KD, Fernandez-del Castillo CF (2013) Branch duct intraductal papillary mucinous neoplasms: does cyst size change the tip of the scale? A critical analysis of the revised international consensus guidelines in a large single-institutional series. Ann Surg 258(3):466–475. https://doi.org/10.1097/SLA.0b013e3182a18f48

    Article  PubMed  Google Scholar 

  29. Innocenti T, Danti G, Lynch EN, Dragoni G, Gottin M, Fedeli F, Palatresi D, Biagini MR, Milani S, Miele V, Galli A (2022) Higher volume growth rate is associated with development of worrisome features in patients with branch duct-intraductal papillary mucinous neoplasms. World J Clin Cases 10(17):5667–5679. https://doi.org/10.12998/wjcc.v10.i17.5667

    Article  PubMed  PubMed Central  Google Scholar 

  30. Attiyeh MA, Fernández-Del Castillo C, Al Efishat M, Eaton AA, Gönen M, Batts R, Pergolini I, Rezaee N, Lillemoe KD, Ferrone CR, Mino-Kenudson M, Weiss MJ, Cameron JL, Hruban RH, D’Angelica MI, DeMatteo RP, Kingham TP, Jarnagin WR, Wolfgang CL, Allen PJ (2018) Development and validation of a multi-institutional preoperative nomogram for predicting grade of dysplasia in intraductal papillary mucinous neoplasms (IPMNs) of the pancreas: a report from the pancreatic surgery consortium. Ann Surg 267(1):157–163. https://doi.org/10.1097/sla.0000000000002015

    Article  PubMed  Google Scholar 

  31. Hoffman DH, Ream JM, Hajdu CH, Rosenkrantz AB (2017) Utility of whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs). Abdom Radiol (NY) 42(4):1222–1228. https://doi.org/10.1007/s00261-016-1001-7

    Article  PubMed  Google Scholar 

  32. Tang TY, Li X, Zhang Q, Guo CX, Zhang XZ, Lao MY, Shen YN, Xiao WB, Ying SH, Sun K, Yu RS, Gao SL, Que RS, Chen W, Huang DB, Pang PP, Bai XL, Liang TB (2020) Development of a novel multiparametric mri radiomic nomogram for preoperative evaluation of early recurrence in resectable pancreatic Cancer. In: J Magn Reson Imaging, vol 52. vol 1. © 2019 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine., United States, pp 231–245. doi:https://doi.org/10.1002/jmri.27024

  33. Polk SL, Choi JW, McGettigan MJ, Rose T, Ahmed A, Kim J, Jiang K, Balagurunathan Y, Qi J, Farah PT, Rathi A, Permuth JB, Jeong D (2020) Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy. World J Gastroenterol 26(24):3458–3471. https://doi.org/10.3748/wjg.v26.i24.3458

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Correa-Gallego C, Do R, Lafemina J, Gonen M, D’Angelica MI, DeMatteo RP, Fong Y, Kingham TP, Brennan MF, Jarnagin WR, Allen PJ (2013) Predicting dysplasia and invasive carcinoma in intraductal papillary mucinous neoplasms of the pancreas: development of a preoperative nomogram. Ann Surg Oncol 20(13):4348–4355. https://doi.org/10.1245/s10434-013-3207-z

    Article  PubMed  Google Scholar 

Download references

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ginevra Danti.

Ethics declarations

Conflict of interest

No conflict of interest.

Ethical approval

The research did not involve human and/or animal participants. Patients signed informed consent form. The study was approved by the Biomedical Research Ethics Committee of our institution (protocol number: 20256_oss) and conformed to the criteria of the Declaration of Helsinki on Ethical Principles and Good Clinical Practice.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 527 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Flammia, F., Innocenti, T., Galluzzo, A. et al. Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential. Radiol med 128, 383–392 (2023). https://doi.org/10.1007/s11547-023-01609-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11547-023-01609-6

Keywords

Navigation