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Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of resection margin status and high-risk features

  • Hepatobiliary-Pancreas
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Abstract

Objectives

To determine if CT texture analysis features are associated with hypovascular pancreas head adenocarcinoma (PHA) postoperative margin status, nodal status, grade, lymphovascular invasion (LVI), and perineural invasion (PNI).

Methods

This Research Ethics Board–approved retrospective cohort study included 131 consecutive patients with resected PHA. Tumors were segmented on preoperative contrast-enhanced CT. Tumor diameter and texture analysis features including mean, minimum and maximum Hounsfield units, standard deviation, skewness, kurtosis, and entropy and gray-level co-occurrence matrix (GLCM) features correlation and dissimilarity were extracted. Two-sample t test and logistic regression were used to compare parameters for prediction of margin status, nodal status, grade, LVI, and PNI. Diagnostic accuracy was assessed using receiver operating characteristic curves and Youden method was used to establish cutpoints.

Results

Margin status was associated with GLCM correlation (p = 0.012) and dissimilarity (p = 0.003); nodal status was associated with standard deviation (p = 0.026) and entropy (p = 0.031); grade was associated with kurtosis (p = 0.031); LVI was associated with standard deviation (p = 0.047), entropy (p = 0.026), and GLCM correlation (p = 0.033) and dissimilarity (p = 0.011). No associations were found for PNI (p > 0.05). Logistic regression yielded an area under the curve of 0.70 for nodal disease, 0.70 for LVI, 0.68 for grade, and 0.65 for margin status. Optimal sensitivity/specificity was as follows: nodal disease 73%/72%, LVI 72%/65%, grade 55%/83%, and margin status 63%/66%.

Conclusions

CT texture analysis features demonstrate fair diagnostic accuracy for assessment of hypovascular PHA nodal disease, LVI, grade, and postoperative margin status. Additional research is rapidly needed to identify these high-risk features with better accuracy.

Key Points

• CT texture analysis features are associated with pancreas head adenocarcinoma postoperative margin status which may help inform treatment decisions as a negative resection margin is required for cure.

• CT texture analysis features are associated with pancreas head adenocarcinoma nodal disease, a poor prognostic feature.

• Indicators of more aggressive pancreas head adenocarcinoma biology including tumor grade and LVI can be diagnosed using CT texture analysis with fair accuracy.

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Abbreviations

AUC:

Area under the curve

GLCM:

Gray-level co-occurrence matrix

HU:

Hounsfield unit

IHC:

Immunohistochemistry

LVI:

Lymphovascular invasion

PACS:

Picture archiving and communication system

PHA:

Pancreas head adenocarcinoma

PNI:

Perineural invasion

ROC:

Receiver operating characteristic

References

  1. The American Cancer Society (2018) Survival rates for exocrine pancreatic cancer. Available via https://www.cancer.org/cancer/pancreatic-cancer/detection-diagnosis-staging/survival-rates.html2018. Accessed 10 Jul 2019

  2. Wagner M, Redaelli C, Lietz M, Seiler CA, Friess H, Buchler MW (2004) Curative resection is the single most important factor determining outcome in patients with pancreatic adenocarcinoma. Br J Surg 91:586–594

    Article  CAS  Google Scholar 

  3. Greenlee RT, Murray T, Bolden S, Wingo PA (2000) Cancer statistics, 2000. CA Cancer J Clin 50:7–33

    Article  CAS  Google Scholar 

  4. Wolfgang CL, Corl F, Johnson PT et al (2011) Pancreatic surgery for the radiologist, 2011: an illustrated review of classic and newer surgical techniques for pancreatic tumor resection. AJR Am J Roentgenol 197:1343–1350

    Article  Google Scholar 

  5. Demir IE, Jager C, Schlitter AM et al (2018) R0 versus R1 resection matters after pancreaticoduodenectomy, and less after distal or total pancreatectomy for pancreatic cancer. Ann Surg 268:1058–1068

    Article  Google Scholar 

  6. van Roessel S, Kasumova GG, Tabatabaie O et al (2018) Pathological margin clearance and survival after pancreaticoduodenectomy in a US and European pancreatic center. Ann Surg Oncol 25:1760–1767

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M (2018) CT texture analysis of pancreatic cancer. Eur Radiol. https://doi.org/10.1007/s00330-018-5662-1

  9. 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

    Article  Google Scholar 

  10. 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

    Article  Google Scholar 

  11. Canellas R, Burk KS, Parakh A, Sahani DV (2018) Prediction of pancreatic neuroendocrine tumor grade based on CT features and texture analysis. AJR Am J Roentgenol 210:341–346

    Article  Google Scholar 

  12. Choi TW, Kim JH, Yu MH, Park SJ, Han JK (2018) Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol 59:383–392

    Article  CAS  Google Scholar 

  13. van der Pol CB, Lee S, Tsai S et al (2019) Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features. Abdom Radiol (NY) 44:992–999

    Article  Google Scholar 

  14. Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I (2019) Validation of a method to compensate multicenter effects affecting CT radiomics. Radiology. https://doi.org/10.1148/radiol.2019182023:182023

  15. Zhang GM, Sun H, Shi B, Jin ZY, Xue HD (2017) Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol (NY) 42:561–568

    Article  Google Scholar 

  16. Adsay NV, Basturk O, Saka B et al (2014) Whipple made simple for surgical pathologists: orientation, dissection, and sampling of pancreaticoduodenectomy specimens for a more practical and accurate evaluation of pancreatic, distal common bile duct, and ampullary tumors. Am J Surg Pathol 38:480–493

    Article  Google Scholar 

  17. Kakar S, Shi C, Adsay NV et al (2017) Protocol for the examination of specimens from patients with carcinoma of the pancreas. Version 4.0.0.1. Includes pTNM requirements from the 8 Edition, AJCC Staging Manual. College of American Pathologists (CAP)/ Cancer Care Ontario (CCO). Available via https://documents.cap.org/protocols/cp-pancreas-exocrine-17protocol-4001.pdf. Accessed 10 Jul 2019

  18. R Core Team (2017) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available via https://www.R-project.org/

  19. Chakraborty J, Langdon-Embry L, Cunanan KM et al (2017) Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients. PLoS One 12:e0188022

  20. 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

    Article  Google Scholar 

  21. Chu LC, Park S, Kawamoto S et al (2019) Utility of CT radiomics features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue. AJR Am J Roentgenol. https://doi.org/10.2214/AJR.18.20901:1-9

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

    Article  Google Scholar 

  23. Dang W, Stefanski PD, Kielar AZ et al (2018) Impact of clinical history on choice of abdominal/pelvic CT protocol in the emergency department. PLoS One 13:e0201694

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Diagnostic Imaging, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, McMaster University, 711 Concession Street, Hamilton, ON, L8V 1C3, Canada

    Ameya Kulkarni, Ivan Carrion-Martinez, Nan N. Jiang, Srikanth Puttagunta & Christian B. van der Pol

  2. Department of Surgery, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, McMaster University, 711 Concession Street, Hamilton, ON, L8V 1C3, Canada

    Leyo Ruo

  3. Department of Oncology, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, McMaster University, 699 Concession Street, Hamilton, ON, L8V 1C3, Canada

    Brandon M. Meyers

  4. Department of Pathology and Molecular Medicine, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, McMaster University, 711 Concession Street, Hamilton, ON, L8V 1C3, Canada

    Tariq Aziz

Authors
  1. Ameya Kulkarni
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  2. Ivan Carrion-Martinez
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  3. Nan N. Jiang
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  4. Srikanth Puttagunta
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  5. Leyo Ruo
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  6. Brandon M. Meyers
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  7. Tariq Aziz
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  8. Christian B. van der Pol
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Corresponding author

Correspondence to Christian B. van der Pol.

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Guarantor

The scientific guarantor of this publication is Dr. Christian B van der Pol.

Conflict of interest

Dr. Meyers serves as an advisor for Amgen, Astra-Zeneca, Celgene, Eisai, Ipsen, Shire, Sentrex Health, and Taiho; Dr. Meyers provides expert testimony for and receives travel stipends from Eisai; Dr. Meyers performs research funded by Celgene and Sillajen. The remaining co-authors have no conflicts of interest to declare. The authors who analyzed the study data are Ameya Kulkarni, Ivan Carrion-Martinez, Srikanth Puttagunta, and Christian B van der Pol; none of whom is employed by or consultants for a company in the medical industry.

Statistics and biometry

Two of the authors, Dr. Nancy Jiang and Dr Christian B van der Pol, have significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Ameya Kulkarni and Ivan Carrion-Martinez are co-first authors.

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Kulkarni, A., Carrion-Martinez, I., Jiang, N.N. et al. Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of resection margin status and high-risk features. Eur Radiol 30, 2853–2860 (2020). https://doi.org/10.1007/s00330-019-06583-0

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  • DOI: https://doi.org/10.1007/s00330-019-06583-0

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