Skip to main content

Advertisement

SpringerLink
Log in

Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation

  • Hepatobiliary-Pancreas
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

In resectable pancreatic ductal adenocarcinoma (PDAC), few pre-operative prognostic biomarkers are available. Radiomics has demonstrated potential but lacks external validation. We aimed to develop and externally validate a pre-operative clinical-radiomic prognostic model.

Methods

Retrospective international, multi-center study in resectable PDAC. The training cohort included 352 patients (pre-operative CTs from five Canadian hospitals). Cox models incorporated (a) pre-operative clinical variables (clinical), (b) clinical plus CT-radiomics, and (c) post-operative TNM model, which served as the reference. Outcomes were overall (OS)/disease-free survival (DFS). Models were assessed in the validation cohort from Ireland (n = 215, CTs from 34 hospitals), using C-statistic, calibration, and decision curve analyses.

Results

The radiomic signature was predictive of OS/DFS in the validation cohort, with adjusted hazard ratios (HR) 2.87 (95% CI: 1.40–5.87, p < 0.001)/5.28 (95% CI 2.35–11.86, p < 0.001), respectively, along with age 1.02 (1.01–1.04, p = 0.01)/1.02 (1.00–1.04, p = 0.03). In the validation cohort, median OS was 22.9/37 months (p = 0.0092) and DFS 14.2/29.8 (p = 0.0023) for high-/low-risk groups and calibration was moderate (mean absolute errors 7%/13% for OS at 3/5 years). The clinical-radiomic model discrimination (C = 0.545, 95%: 0.543–0.546) was higher than the clinical model alone (C = 0.497, 95% CI 0.496–0.499, p < 0.001) or TNM (C = 0.525, 95% CI: 0.524–0.526, p < 0.001). Despite superior net benefit compared to the clinical model, the clinical-radiomic model was not clinically useful for most threshold probabilities.

Conclusion

A multi-institutional pre-operative clinical-radiomic model for resectable PDAC prognostication demonstrated superior net benefit compared to a clinical model but limited clinical utility at external validation. This reflects inherent limitations of radiomics for PDAC prognostication, when deployed in real-world settings.

Key Points

• At external validation, a pre-operative clinical-radiomics prognostic model for pancreatic ductal adenocarcinoma (PDAC) outperformed pre-operative clinical variables alone or pathological TNM staging.

• Discrimination and clinical utility of the clinical-radiomic model for treatment decisions remained low, likely due to heterogeneity of CT acquisition parameters.

• Despite small improvements, prognosis in PDAC using state-of-the-art radiomics methodology remains challenging, mostly owing to its low discriminative ability. Future research should focus on standardization of CT protocols and acquisition parameters.

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
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AI:

Artificial intelligence

AJCC:

American Joint Committee on Cancer

CI:

Confidence intervals

CPH:

Cox proportional hazard

CT:

Computed tomography

DCA:

Decision curve analysis

DICOM:

Digital Imaging and Communications in Medicine

GLCM:

Gray-level co-occurrence matrix

HR:

Hazard ratio

IBSI:

Image Biomarker Standardization Initiative

ISI:

Image-to-surgery time interval in days

LASSO:

Least absolute shrinkage and selection operator

MVA:

Multivariate analysis

OS:

Overall survival

PACS:

Picture archiving and communication system

PDAC:

Pancreatic ductal adenocarcinoma

PV:

Portal venous

QIBA:

Quantitative Imaging Biomarker Alliance

RIS:

Radiology information system

TNM:

Tumor Node Metastasis staging system

TRIPOD:

Transparent Reporting of multivariable prediction model for Individual Prognosis or Diagnosis statement

References

  1. Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  2. National Cancer Registry Ireland. Pancreas Factsheet. Available via https://www.ncri.ie/sites/ncri/files/factsheets/Factsheet%20pancreas.pdf. Accessed February 3rd 2021

  3. (2017) Canadian Cancer Statistics 2017. Special topic: pancreatic cancer. Available via https://www.cancer.ca/~/media/cancer.ca/CW/cancer%20information/cancer%20101/Canadian%20cancer%20statistics/Canadian-Cancer-Statistics-2017-EN.pdf. Accessed February 3rd 2021Accessed February 3rd 2021

  4. Sun H, Ma H, Hong G, Sun H, Wang J (2014) Survival improvement in patients with pancreatic cancer by decade: a period analysis of the SEER database, 1981–2010. Sci Rep 4:6747

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Huang L, Jansen L, Balavarca Y et al (2019) Resection of pancreatic cancer in Europe and USA: an international large-scale study highlighting large variations. Gut 68:130–139

    PubMed  Google Scholar 

  6. Kim JR, Kim H, Kwon W, Jang JY, Kim SW (2021) Pattern of local recurrence after curative resection in pancreatic ductal adenocarcinoma according to the initial location of the tumor. J Hepatobiliary Pancreat Sci 28:105–114

    PubMed  Google Scholar 

  7. Gaskill CE, Maxwell J, Ikoma N et al (2021) History of preoperative therapy for pancreatic cancer and the MD Anderson experience. J Surg Oncol 123:1414–1422

    PubMed  Google Scholar 

  8. Schneider M, Neoptolemos JP, Büchler MW (2020) Commentary: Neoadjuvant treatment of resectable pancreatic cancer: lack of level III evidence. Surgery 168:1015–1016

    PubMed  Google Scholar 

  9. Versteijne E, Suker M, Groothuis K et al (2020) Preoperative chemoradiotherapy versus immediate surgery for resectable and borderline resectable pancreatic cancer: results of the Dutch Randomized Phase III PREOPANC Trial. J Clin Oncol 38:1763–1773

    CAS  PubMed  PubMed Central  Google Scholar 

  10. O’Reilly EM, Ferrone C (2020) Neoadjuvant or adjuvant therapy for resectable or borderline resectable pancreatic cancer: which is preferred? J Clin Oncol 38:1757–1759

    PubMed  PubMed Central  Google Scholar 

  11. Haab BB, Huang Y, Balasenthil S et al (2015) Definitive characterization of CA 19–9 in resectable pancreatic cancer using a reference set of serum and plasma specimens. PLoS One 10:e0139049

    PubMed  PubMed Central  Google Scholar 

  12. Xie T, Wang X, Li M, Tong T, Yu X, Zhou Z (2020) Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection. Eur Radiol 30:2513–2524

    PubMed  Google Scholar 

  13. Cassinotto C, Chong J, Zogopoulos G et al (2017) Resectable pancreatic adenocarcinoma: role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes. Eur J Radiol 90:152–158

    PubMed  Google Scholar 

  14. Park JK, Paik WH, Ryu JK et al (2013) Clinical significance and revisiting the meaning of CA 19–9 blood level before and after the treatment of pancreatic ductal adenocarcinoma: analysis of 1,446 patients from the pancreatic cancer cohort in a single institution. PLoS One 8:e78977

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Xu D, Zhang K, Li M et al (2020) Prognostic nomogram for resected pancreatic adenocarcinoma: a TRIPOD-compliant retrospective long-term survival analysis. World J Surg 44:1260–1269

    PubMed  Google Scholar 

  16. Brennan MF, Kattan MW, Klimstra D, Conlon K (2004) Prognostic nomogram for patients undergoing resection for adenocarcinoma of the pancreas. Ann Surg 240:293–298

    PubMed  PubMed Central  Google Scholar 

  17. Grossmann P, Stringfield O, El-Hachem N et al (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife 6:e23421. https://doi.org/10.7554/eLife.23421

  18. Li H, Zhu Y, Burnside ES et al (2016) Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer 2:16012

    PubMed  PubMed Central  Google Scholar 

  19. Zwanenburg A, Vallières M, Abdalah MA et al (2020) The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338

    PubMed  Google Scholar 

  20. Abunahel BM, Pontre B, Kumar H, Petrov MS (2020) Pancreas image mining: a systematic review of radiomics. Eur Radiol. https://doi.org/10.1007/s00330-020-07376-6

    Article  PubMed  Google Scholar 

  21. Li K, Xiao J, Yang J et al (2019) Association of radiomic imaging features and gene expression profile as prognostic factors in pancreatic ductal adenocarcinoma. Am J Transl Res 11:4491–4499

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Shi H, Wei Y, Cheng S et al (2021) Survival prediction after upfront surgery in patients with pancreatic ductal adenocarcinoma: radiomic, clinic-pathologic and body composition analysis. Pancreatology. https://doi.org/10.1016/j.pan.2021.02.009

    Article  PubMed  Google Scholar 

  23. Attiyeh MA, Chakraborty J, Doussot A et al (2018) Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis. Ann Surg Oncol 25:1034–1042

    PubMed  PubMed Central  Google Scholar 

  24. Eilaghi A, Baig S, Zhang Y et al (2017) CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis. BMC Med Imaging 17:38

    PubMed  PubMed Central  Google Scholar 

  25. Yun G, Kim YH, Lee YJ, Kim B, Hwang JH, Choi DJ (2018) Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection. Sci Rep 8:7226

    PubMed  PubMed Central  Google Scholar 

  26. 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

    PubMed  PubMed Central  Google Scholar 

  27. Zhang H, Dullerud N, Seyyed-Kalantari L, Morris Q, Joshi S, Ghassemi M (2021) An empirical framework for domain generalization in clinical settings. Proceedings of the Conference on Health, Inference, and Learning, pp 279–290. https://doi.org/10.1145/3450439.3451878

  28. Khalvati F, Zhang Y, Baig S et al (2019) Prognostic value of CT radiomic features in resectable pancreatic ductal adenocarcinoma. Sci Rep 9:5449

    PubMed  PubMed Central  Google Scholar 

  29. Zhang Y, Lobo-Mueller EM, Karanicolas P, Gallinger S, Haider MA, Khalvati F (2020) CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. BMC Med Imaging 20:11

    PubMed  PubMed Central  Google Scholar 

  30. Zhang Y, Lobo-Mueller EM, Karanicolas P, Gallinger S, Haider MA, Khalvati F (2021) Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images. Sci Rep 11:1378

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    PubMed  Google Scholar 

  32. Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg 102:148–158

    CAS  PubMed  Google Scholar 

  33. Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A, editors. (2010) AJCC Cancer Staging Manual (7th ed). Springer, New York, NY

  34. Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341

    PubMed  PubMed Central  Google Scholar 

  35. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    PubMed  PubMed Central  Google Scholar 

  36. The image biomarker standardisation initiative. Available via https://ibsi.readthedocs.io/en/latest/index.html

  37. Fornacon-Wood I, Mistry H, Ackermann CJ et al (2020) Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. Eur Radiol. https://doi.org/10.1007/s00330-020-06957-9

    Article  PubMed  PubMed Central  Google Scholar 

  38. Mackin D, Fave X, Zhang L et al (2017) Harmonizing the pixel size in retrospective computed tomography radiomics studies. PLoS One 12:e0178524

    PubMed  PubMed Central  Google Scholar 

  39. Shafiq-Ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E (2018) Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep 8:10545

    PubMed  PubMed Central  Google Scholar 

  40. Ligero M, Jordi-Ollero O, Bernatowicz K et al (2021) Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. Eur Radiol 31:1460–1470

    PubMed  Google Scholar 

  41. Berenguer R, Pastor-Juan MDR, Canales-Vázquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:407–415

    PubMed  Google Scholar 

  42. Fortin JP, Parker D, Tunç B et al (2017) Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161:149–170

    PubMed  Google Scholar 

  43. Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I (2019) Validation of A method to compensate multicenter effects affecting CT radiomics. Radiology 291:53–59

    PubMed  Google Scholar 

  44. Little R (1988) Missing-data adjustments in large surveys. J Bus Econ Stat 6:287–296

    Google Scholar 

  45. Steyerberg E (2009) Clinical prediction models: a practical approach to development, validation and updating, 2nd edn. Springer

    Google Scholar 

  46. Eid M, Ostřížková L, Kunovský L et al (2021) Current view of neoadjuvant chemotherapy in primarily resectable pancreatic adenocarcinoma. Neoplasma 68:1–9

    PubMed  Google Scholar 

  47. Tempero MA, Malafa MP, Al-Hawary M et al (2021) Pancreatic Adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 19:439-457

  48. Roalsø M, Aunan JR, Søreide K (2020) Refined TNM-staging for pancreatic adenocarcinoma – real progress or much ado about nothing? Eur J Surg Oncol 46:1554–1557

    PubMed  Google Scholar 

  49. Chang JC, Kundranda M (2017) Novel Diagnostic and Predictive Biomarkers in Pancreatic Adenocarcinoma. Int J Mol Sci 18:667

  50. Guo SW, Shen J, Gao JH et al (2020) A preoperative risk model for early recurrence after radical resection may facilitate initial treatment decisions concerning the use of neoadjuvant therapy for patients with pancreatic ductal adenocarcinoma. Surgery 168:1003–1014

    PubMed  Google Scholar 

  51. Vickers AJ, Cronin AM (2010) Everything you always wanted to know about evaluating prediction models (but were too afraid to ask). Urology 76:1298–1301

    PubMed  Google Scholar 

  52. He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z (2016) Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep 6:34921

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Chu LC, Solmaz B, Park S et al (2020) Diagnostic performance of commercially available vs. in-house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls. Abdom Radiol (NY) 45:2469–2475

    Google Scholar 

  54. Koay EJ, Truty MJ, Cristini V et al (2014) Transport properties of pancreatic cancer describe gemcitabine delivery and response. J Clin Invest 124:1525–1536

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Fukukura Y, Takumi K, Higashi M et al (2014) Contrast-enhanced CT and diffusion-weighted MR imaging: performance as a prognostic factor in patients with pancreatic ductal adenocarcinoma. Eur J Radiol 83:612–619

    PubMed  Google Scholar 

  56. Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802

    PubMed  Google Scholar 

  57. Larue R, Klaassen R, Jochems A et al (2018) Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer. Acta Oncol 57:1475–1481

    CAS  PubMed  Google Scholar 

  58. Shim KY, Chung SW, Jeong JH et al (2021) Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI. Sci Rep 11:9974–9974

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Kulkarni A, Carrion-Martinez I, Dhindsa K, Alaref AA, Rozenberg R, van der Pol CB (2020) Pancreas adenocarcinoma CT texture analysis: comparison of 3D and 2D tumor segmentation techniques. Abdom Radiol (NY). https://doi.org/10.1007/s00261-020-02759-1

    Article  Google Scholar 

  60. Ng F, Kozarski R, Ganeshan B, Goh V (2013) Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 82:342–348

    PubMed  Google Scholar 

  61. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503

    PubMed  Google Scholar 

  62. Yamashita R, Perrin T, Chakraborty J et al (2020) Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. Eur Radiol 30:195–205

    PubMed  Google Scholar 

  63. Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387

    PubMed  Google Scholar 

Download references

Funding

M.A.H receives funding from the Ontario Institute for Cancer Research (OICR) Translational Research Initiative in Pancreatic Cancer and the Clinical Investigator Program. GMH is a Clinical Research Fellow who is funded by a research grant from the Faculty of Radiologists, Royal College of Surgeons in Ireland. DD is a research fellow who is funded by a DFG (Deutsche Forschungsgemeinschaft) Fellowship DE 3207/1–1 (DD).

Author information

Authors and Affiliations

  1. Joint Department of Medical Imaging, University Health Network, Sinai Health System and Women’s College Hospital, University of Toronto, Toronto, ON, Canada

    Gerard M. Healy & Masoom A. Haider

  2. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

    Gerard M. Healy & Masoom A. Haider

  3. Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada

    Gerard M. Healy, Emmanuel Salinas-Miranda, Xin Dong, Dominik Deniffel, Steven Gallinger & Masoom A. Haider

  4. Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada

    Rahi Jain

  5. Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany

    Dominik Deniffel

  6. Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada

    Ayelet Borgida & Robert C. Grant

  7. Radiation Medicine Program, Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada

    Ali Hosni

  8. Department of Radiology, St Vincent’s University Hospital, Dublin, Ireland

    David T. Ryan, Jonathan D. Dodd & Edmund Ronan Ryan

  9. National Surgical Centre for Pancreatic Cancer, St. Vincent’s University Hospital, Dublin, Ireland

    Nwabundo Njeze, Anne McGuire, Kevin C. Conlon & Edmund Ronan Ryan

  10. School of Medicine, Trinity College Dublin, Dublin, Ireland

    Kevin C. Conlon

  11. School of Medicine, University College Dublin, Dublin, Ireland

    Jonathan D. Dodd & Edmund Ronan Ryan

  12. PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada

    Robert C. Grant, Steven Gallinger & Masoom A. Haider

  13. Surgical Oncology Program, Hepatobiliary Pancreatic, University Health Network, Toronto, ON, Canada

    Steven Gallinger

Authors
  1. Gerard M. Healy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Salinas-Miranda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rahi Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dominik Deniffel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ayelet Borgida
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ali Hosni
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. David T. Ryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nwabundo Njeze
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Anne McGuire
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Kevin C. Conlon
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jonathan D. Dodd
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Edmund Ronan Ryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Robert C. Grant
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Steven Gallinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Masoom A. Haider
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masoom A. Haider.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr Masoom Haider

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Xin Dong and Rahi Jain (both co-authors) kindly provided statistical advice for this manuscript.

Informed consent

The need for informed consent was waived by research ethics boards.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Fifty-five patients within the North-American cohort have been previously used in a PDAC Radiomics prognostication study which generated three publications.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at multiple institutions

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 683 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Healy, G.M., Salinas-Miranda, E., Jain, R. et al. Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation. Eur Radiol 32, 2492–2505 (2022). https://doi.org/10.1007/s00330-021-08314-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-08314-w

Keywords

Search

Navigation