Skip to main content

Advertisement

SpringerLink
Log in

Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis

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

Abstract

Purpose

To develop a predictive model for liver metastases in patients with pancreatic ductal adenocarcinoma (PDAC) based on textural features of the primary tumor extracted by computed tomography (CT) images.

Materials and methods

Patients with a pathologically proved PDAC who underwent CT between December 2020 and January 2022 were retrospectively identified. Treatment-naïve patients were included. Sex, age, tumor size, vascular infiltration and 39 arterial and portal phase textural features were analyzed. The variables significantly correlated to tumor size according to the Pearson's product-moment correlation test were excluded from analysis; the remaining variables were compared between metastatic (M +) and non-metastatic (M−) patients using Fisher’s or Mann–Whitney test. The features with a significant difference between groups were entered into a binomial logistic regression test to develop a predictive model for liver metastases.

Results

This study included 220 patients. Eight variables (tumor size, arterial HU_MAX, arterial GLRLM_LRLGE, arterial GLZLM_SZHGE, arterial GLZLM_LZLGE, portal GLCM_CORRELATION, portal GLRLM_LRLGE, and portal GLZLM_SZHGE) were significantly different between groups. The logistic regression model was statistically significant (χ2 = 81.6, p < .001) and correctly classified 80.9% of cases. Sensitivity, specificity, positive and negative predictive values of the model were 58.6%, 91.3%, 75.9% and 82.5%, respectively. The area under the ROC curve of the model was 0.850 (95% CI, 0.793–0.907). Tumor size, arterial HU_MAX, arterial GLZLM_SZHGE and portal GLCM_CORRELATION were significant predictors of the likelihood of liver metastases, with odds ratios of 1.1, 0.9, 1, and 1.49, respectively.

Conclusions

CT texture analysis of PDAC can identify features that may predict the likelihood of liver metastases.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Rawla P, Sunkara T, Gaduputi V (2019) Epidemiology of pancreatic cancer: global trends, etiology and risk factors. World J Oncol 10:10–27

    Article  Google Scholar 

  2. Carioli G, Malvezzi M, Bertuccio P, Boffetta P, Levi F, La Vecchia C, Negri E (2021) European cancer mortality predictions for the year 2021 with focus on pancreatic and female lung cancer. Ann Oncol 32:478–487. https://doi.org/10.1016/j.annonc.2021.01.006

    Article  CAS  PubMed  Google Scholar 

  3. Associazione Italiana di Oncologia Medica (2021) Linee guida Carcinoma del pancreas esocrino. https://www.aiom.it/linee-guida-aiom-2021-carcinoma-del-pancreas-esocrino/ Accessed 7 Jul 2022

  4. Rebelo A, Büdeyri I, Heckler M, Partsakhashvili J, Ukkat J, Ronellenfitsch U, Michalski CW, Kleeff J (2020) Systematic review and meta-analysis of contemporary pancreas surgery with arterial resection. Langenbecks Arch Surg 405:903–919. https://doi.org/10.1007/s00423-020-01972-2

    Article  PubMed  PubMed Central  Google Scholar 

  5. Solaini L, de Rooij T, Marsman EM, Te Riele WW, Tanis PJ, van Gulik TM, Gouma DJ, Bhayani NH, Hackert T, Busch OR, Besselink MG (2018) Pancreatoduodenectomy with colon resection for pancreatic cancer: a systematic review. HPB 20:881–887. https://doi.org/10.1016/j.hpb.2018.03.017

    Article  PubMed  Google Scholar 

  6. Sanjeevi S, Ivanics T, Lundell L, Kartalis N, Andrén-Sandberg Å, Blomberg J, Del Chiaro M, Ansorge C (2016) Impact of delay between imaging and treatment in patients with potentially curable pancreatic cancer. Br J Surg 103:267–275. https://doi.org/10.1002/bjs.10046

    Article  CAS  PubMed  Google Scholar 

  7. Barugola G, Partelli S, Marcucci S, Sartori N, Capelli P, Bassi C, Pederzoli P, Falconi M (2009) Resectable pancreatic cancer: Who really benefits from resection? Ann Surg Oncol 16:3316–3322. https://doi.org/10.1245/s10434-009-0670-7

    Article  PubMed  Google Scholar 

  8. La Torre M, Nigri G, Lo Conte A, Mazzuca F, Tierno SM, Salaj A, Marchetti P, Ziparo V, Ramacciato G (2014) Is a preoperative assessment of the early recurrence of pancreatic cancer possible after complete surgical resection? Gut Liver 8:102–108. https://doi.org/10.5009/gnl.2014.8.1.102

    Article  PubMed  Google Scholar 

  9. Nishio K, Kimura K, Amano R, Yamazoe S, Ohrira G, Nakata B, Hirakawa K, Ohira M (2017) Preoperative predictors for early recurrence of resectable pancreatic cancer. World J Surg Oncol 10:16. https://doi.org/10.1186/s12957-016-1078-z

    Article  Google Scholar 

  10. Liu X, Fu Y, Chen O, Wu J, Gao W, Jiang K, Miao Y, Wei J (2018) Predictors of distant metastasis on exploration in patients with potentially resectable pancreatic cancer. BMC Gastroenterol 18:168. https://doi.org/10.1186/s12876-018-0891-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503. https://doi.org/10.1148/rg.2017170056

    Article  PubMed  Google Scholar 

  12. Nardone V, Reginelli A, Grassi R, Boldrini L, Vacca G, D’Ippolito E, Annunziata S, Farchione A, Belfiore MP, Desideri I, Cappabianca S (2021) Delta radiomics: a systematic review. Radiol Med 126:1571–1583. https://doi.org/10.1007/s11547-021-01436-7

    Article  PubMed  Google Scholar 

  13. Karmazanovsky G, Gruzdev I, Tikhonova V, Kondratyev E, Revishvili A (2021) Computed tomography-based radiomics approach in pancreatic tumors characterization. Radiol Med. https://doi.org/10.1007/s11547-021-01405-0

    Article  PubMed  Google Scholar 

  14. Awe AM, Rendell VR, Lubner MG, Winslow ER (2020) Texture analysis: an emerging clinical tool for pancreatic lesions. Pancreas 49(3):301–312. https://doi.org/10.1097/MPA.0000000000001495

    Article  PubMed  PubMed Central  Google Scholar 

  15. Agostini A, Borgheresi A, Bruno F, Natella R, Floridi C, Carotti M, Giovagnoni A (2020) New advances in CT imaging of pancreas diseases: a narrative review. Gland Surg 9(6):2283–2294. https://doi.org/10.21037/gs-20-551

    Article  PubMed  PubMed Central  Google Scholar 

  16. Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, Pellot-Barakat C, Soussan M, Frouin F, Buvat I (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789. https://doi.org/10.1158/0008-5472.CAN-18-0125

    Article  CAS  PubMed  Google Scholar 

  17. Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & Sons Inc, Hoboken, New Jersey, USA

    Book  Google Scholar 

  18. Granata V, Grassi R, Fusco R, Galdiero R, Setola SV, Palaia R, Belli A, Silvestro L, Cozzi D, Brunese L, Petrillo A, Izzo F (2021) Pancreatic cancer detection and characterization: state of the art and radiomics. Eur Rev Med Pharmacol Sci 25:3684–3699

    CAS  PubMed  Google Scholar 

  19. Qiu W, Duan N, Chen X, Ren S, Zhang Y, Wang Z, Chen R (2019) Pancreatic ductal adenocarcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of histopathological grade. Cancer Manag Res 11:9253–9264. https://doi.org/10.2147/CMAR.S218414

    Article  PubMed  PubMed Central  Google Scholar 

  20. Cassinotto C, Chong J, Zogopoulos G, Reinhold C, Chiche L, Lafourcade JP, Cuggia A, Terrebonne E, Dohan A, Gallix B (2017) Resectable pancreatic adenocarcinoma: role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes. Eur J Radiol 90:152–158. https://doi.org/10.1016/j.ejrad.2017.02.033

    Article  PubMed  Google Scholar 

  21. Fang WH, Li XD, Zhu H, Miao F, Qian XH, Pan ZL, Lin XZ (2020) Resectable pancreatic ductal adenocarcinoma: association between preoperative CT texture features and metastatic nodal involvement. Cancer Imaging 20:17. https://doi.org/10.1186/s40644-020-0296-3

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kulkarni A, Carrion-Martinez I, Jiang NN, Puttagunta S, Ruo L, Meyers BM, Aziz T, van der Pol CB (2020) Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of resection margin status and high-risk features. Eur Radiol 30:2853–2860. https://doi.org/10.1007/s00330-019-06583-0

    Article  PubMed  Google Scholar 

  23. Rigiroli F, Hoye J, Lerebours R, Lafata KJ, Li C, Meyer M, Lyu P, Ding Y, Schwartz FR, Mettu NB, Zani S Jr, Luo S, Morgan DE, Samei E, Marin D (2021) CT radiomic features of superior mesenteric artery involvement in pancreatic ductal adenocarcinoma: a pilot study. Radiology 301:610–622. https://doi.org/10.1148/radiol.2021210699

    Article  PubMed  Google Scholar 

  24. Cheng SH, Cheng YJ, Jin ZY, Xue HD (2019) Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy. Eur J Radiol 113:188–197. https://doi.org/10.1016/j.ejrad.2019.02.009

    Article  PubMed  Google Scholar 

  25. Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M (2019) CT texture analysis of pancreatic cancer. Eur Radiol 29:1067–1073. https://doi.org/10.1007/s00330-018-5662-1

    Article  PubMed  Google Scholar 

  26. Eilaghi A, Baig S, Zhang Y, Zhang J, Karanicolas P, Gallinger S, Khalvati F, Haider MA (2017) CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis. BMC Med Imaging 17:38. https://doi.org/10.1186/s12880-017-0209-5

    Article  PubMed  PubMed Central  Google Scholar 

  27. Attiyeh MA, Chakraborty J, Doussot A, Langdon-Embry L, Mainarich S, Gönen M, Balachandran VP, D’Angelica MI, DeMatteo RP, Jarnagin WR, Kingham TP, Allen PJ, Simpson AL, Do RK (2018) Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis. Ann Surg Oncol 25:1034–1042. https://doi.org/10.1245/s10434-017-6323-3

    Article  PubMed  PubMed Central  Google Scholar 

  28. Kim HS, Kim YJ, Kim KG, Park JS (2019) Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer. Sci Rep 9:17389. https://doi.org/10.1038/s41598-019-53831-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ciaravino V, Cardobi N, De Robertis R, Capelli P, Melisi D, Simionato F, Marchegiani G, Salvia R, D’onofrio M (2018) CT texture analysis of ductal adenocarcinoma downstaged after chemotherapy. Anticancer Res 38(8):4889–4895. https://doi.org/10.21873/anticanres.12803

    Article  PubMed  Google Scholar 

  30. Borhani AA, Dewan R, Furlan A, Seiser N, Zureikat AH, Singhi AD, Boone B, Bahary N, Hogg ME, Lotze M, Zeh HJ III, Tublin ME (2020) Assessment of response to neoadjuvant therapy using ct texture analysis in patients with resectable and borderline resectable pancreatic ductal adenocarcinoma. AJR Am J Roentgenol 214:362–369. https://doi.org/10.2214/AJR.19.21152

    Article  PubMed  Google Scholar 

  31. D’Onofrio M, De Robertis R, Aluffi G, Cadore C, Beleù A, Cardobi N, Malleo G, Manfrin E, Bassi C (2021) CT simplified radiomic approach to assess the metastatic ductal adenocarcinoma of the pancreas. Cancers 13:1843. https://doi.org/10.3390/cancers13081843

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

No funds, grants, or other support was received.

Author information

Authors and Affiliations

  1. Department of Radiology – Ospedale G.B. Rossi AOUI Verona, Department of Diagnostics and Public Health, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy

    Riccardo De Robertis, Luca Geraci, Luisa Tomaiuolo, Luca Bortoli & Mirko D’Onofrio

  2. Department of Radiology, Ospedale Ca’ Foncello, Piazzale Ospedale 1, 31100, Treviso, Italy

    Alessandro Beleù

  3. Department of Pancreatic Surgery – Ospedale G.B. Rossi AOUI Verona, Department of Surgery, Dentistry, Pediatrics and Gynaecology, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy

    Giuseppe Malleo

Authors
  1. Riccardo De Robertis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luca Geraci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luisa Tomaiuolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luca Bortoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alessandro Beleù
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Giuseppe Malleo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mirko D’Onofrio
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by RDR, LG and LB. The first draft of the manuscript was written by RDR and LG, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Riccardo De Robertis.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

Ethical approval was waived by the local Ethics Committee of University of Verona in view of the retrospective nature of the study and all the procedures being performed were part of the routine case.

Informed consent

Written informed consent was obtained from all individual participants included in the study.

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 (PDF 183 KB)

Rights and permissions

Springer Nature or its licensor 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

De Robertis, R., Geraci, L., Tomaiuolo, L. et al. Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis. Radiol med 127, 1079–1084 (2022). https://doi.org/10.1007/s11547-022-01548-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11547-022-01548-8

Keywords

Search

Navigation