Performance of CT-based radiomics in diagnosis of superior mesenteric vein resection margin in patients with pancreatic head cancer

  • Yun Bian
  • Hui Jiang
  • Chao Ma
  • Kai Cao
  • Xu Fang
  • Jing Li
  • Li Wang
  • Jianming Zheng
  • Jianping LuEmail author



To accurately identify the relationship between a portal radiomics score (rad-score) and pathologic superior mesenteric vein (SMV) resection margin and to evaluate the diagnostic performance in patients with pancreatic head cancer.

Materials and methods

A total of 181 patients with postoperatively and pathologically confirmed pancreatic head cancer who underwent multislice computed tomography within one month of resection between January 2016 and December 2018 were retrospectively investigated. For each patient, 1029 radiomics features of the portal phase were extracted, which were reduced using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. Multivariate logistic regression models were used to analyze the association between the portal rad-score and SMV resection margin.


Patients with negative (R0) and positive (R1) margins accounted for 70.17% (127) and 29.83% (54) of the cohort, respectively. The rad-score was significantly associated with the SMV resection margin status (p < 0.05). Multivariate analyses confirmed a significant and independent association between the portal rad-score and SMV resection margin (OR 4.62; 95% CI 2.19–9.76; p < 0.0001). The portal rad-score had high accuracy (area under the curve = 0.750). The best cut point based on maximizing the sum of sensitivity and specificity was − 0.741 (sensitivity = 64.8%; specificity = 74.0%; accuracy = 71.3%). Decision curve analysis indicated the clinical usefulness of radiomics score.


The portal rad-score is significantly associated with the pathologic SMV resection margin, and it can accurately and noninvasively predict the SMV resection margin in patients with pancreatic cancer.


Pancreatic cancer Resection margin CT Radiomics 



Huiying Medical Technology (Beijing) Co., Ltd, Beijing, China.


This work was supported in part by the National Science Foundation for Scientists of China (81871352), National Science Foundation for Young Scientists of China (81701689, 81601468), 63-class General Financial Grant from the China Postdoctoral Science Foundation (2018M633714), Key Junior College of National Clinical of China, and Shanghai Technology Innovation Project 2017 on Clinical Medicine (17411952200).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

261_2019_2401_MOESM1_ESM.docx (66 kb)
Supplementary material 1 (DOCX 66 kb)


  1. 1.
    Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68:7-30. CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Kamisawa T, Wood LD, Itoi T, et al. (2016) Pancreatic cancer. Lancet 388:73-85. CrossRefPubMedGoogle Scholar
  3. 3.
    De La Cruz MS, Young AP, Ruffin MT (2014) Diagnosis and management of pancreatic cancer. Am Fam Physician 89:626-632.Google Scholar
  4. 4.
    Konstantinidis IT, Warshaw AL, Allen JN, et al. (2013) Pancreatic ductal adenocarcinoma: is there a survival difference for R1 resections versus locally advanced unresectable tumors? What is a “true” R0 resection? Ann Surg 257:731-736. CrossRefPubMedGoogle Scholar
  5. 5.
    Rau BM, Moritz K, Schuschan S, et al. (2012) R1 resection in pancreatic cancer has significant impact on long-term outcome in standardized pathology modified for routine use. Surgery 152:S103-111. CrossRefPubMedGoogle Scholar
  6. 6.
    Tempero MA, Malafa MP, Chiorean EG, et al. (2019) Pancreatic Adenocarcinoma, Version 1.2019. J Natl Compr Canc Netw 17:202-210. CrossRefPubMedGoogle Scholar
  7. 7.
    Tempero MA (2019) NCCN Guidelines Updates: Pancreatic Cancer. J Natl Compr Canc Netw 17:603-605. CrossRefPubMedGoogle Scholar
  8. 8.
    Blazer M, Wu C, Goldberg RM, et al. (2015) Neoadjuvant modified (m) FOLFIRINOX for locally advanced unresectable (LAPC) and borderline resectable (BRPC) adenocarcinoma of the pancreas. Ann Surg Oncol 22:1153-1159. CrossRefPubMedGoogle Scholar
  9. 9.
    Howard TJ, Krug JE, Yu J, et al. (2006) A margin-negative R0 resection accomplished with minimal postoperative complications is the surgeon’s contribution to long-term survival in pancreatic cancer. J Gastrointest Surg 10:1338-1345; discussion 1345-1336. CrossRefGoogle Scholar
  10. 10.
    Noda Y, Goshima S, Kawada H, et al. (2018) Modified National Comprehensive Cancer Network Criteria for Assessing Resectability of Pancreatic Ductal Adenocarcinoma. AJR Am J Roentgenol 210:1252-1258. CrossRefPubMedGoogle Scholar
  11. 11.
    Fang CH, Zhu W, Wang H, et al. (2012) A new approach for evaluating the resectability of pancreatic and periampullary neoplasms. Pancreatology 12:364-371. CrossRefPubMedGoogle Scholar
  12. 12.
    Garces-Descovich A, Beker K, Jaramillo-Cardoso A, et al. (2018) Applicability of current NCCN Guidelines for pancreatic adenocarcinoma resectability: analysis and pitfalls. Abdom Radiol 43:314-322. CrossRefGoogle Scholar
  13. 13.
    Lambin P, Rios-Velazquez E, Leijenaar R, et al. (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441-446. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Kumar V, Gu Y, Basu S, et al. (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234-1248. CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Bian Y, Guo S, Jiang H, et al. (2019) Relationship Between Radiomics and Risk of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma. Pancreas 48:1195-1203. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Campbell F, Verbeke CS (2013) Pathology of the Pancreas: A Practical Approach. Springer, LondonCrossRefGoogle Scholar
  17. 17.
    Amin MB, Edge SB, Greene FL, et al. (2017) AJCC Cancer Staging manual, 8 edn. Springer, New YorkCrossRefGoogle Scholar
  18. 18.
    Watanabe H, Okada M, Kaji Y, et al. (2009) New response evaluation criteria in solid tumours-revised RECIST guideline (version 1.1). Gan To Kagaku Ryoho 36:2495-2501PubMedGoogle Scholar
  19. 19.
    Kitagawa H, Ohta T, Makino I, et al. (2008) Carcinomas of the ventral and dorsal pancreas exhibit different patterns of lymphatic spread. Front Biosci 13:2728-2735CrossRefGoogle Scholar
  20. 20.
    Makino I, Kitagawa H, Ohta T, et al. (2008) Nerve plexus invasion in pancreatic cancer: spread patterns on histopathologic and embryological analyses. Pancreas 37:358-365CrossRefGoogle Scholar
  21. 21.
    van Griethuysen JJM, Fedorov A, Parmar C, et al. (2017) Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res 77:e104-e107. CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Hong SB, Lee SS, Kim JH, et al. (2018) Pancreatic Cancer CT: Prediction of Resectability according to NCCN Criteria. Radiology 289:710-718. CrossRefPubMedGoogle Scholar
  23. 23.
    Gadducci A, Cavazzana A, Cosio S, et al. (2009) Lymph-vascular space involvement and outer one-third myometrial invasion are strong predictors of distant haematogeneous failures in patients with stage I-II endometrioid-type endometrial cancer. Anticancer Res 29:1715-1720.Google Scholar
  24. 24.
    Dekker TJ, van de Velde CJ, van Bruggen D, et al. (2013) Quantitative assessment of lymph vascular space invasion (LVSI) provides important prognostic information in node-negative breast cancer. Ann Oncol 24:2994-2998. CrossRefPubMedGoogle Scholar
  25. 25.
    Briet JM, Hollema H, Reesink N, et al. (2005) Lymphvascular space involvement: an independent prognostic factor in endometrial cancer. Gynecol Oncol 96:799-804. CrossRefPubMedGoogle Scholar
  26. 26.
    Garces-Descovich A, Beker K, Jaramillo-Cardoso A, et al. (2018) Applicability of current NCCN Guidelines for pancreatic adenocarcinoma resectability: analysis and pitfalls. Abdom Radiol (NY) 43:314-322. CrossRefGoogle Scholar
  27. 27.
    Liang W, Yang P, Huang R, et al. (2019) A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors. Clin Cancer Res 25:584-594. CrossRefPubMedGoogle Scholar
  28. 28.
    Yang J, Guo X, Ou X, et al. (2019) Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning. Frontiers in oncology 9:494. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Gu D, Hu Y, Ding H, et al. (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol. CrossRefPubMedGoogle Scholar
  30. 30.
    Kim BR, Kim JH, Ahn SJ, et al. (2019) CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis. Eur Radiol 29:362-372. CrossRefPubMedGoogle Scholar
  31. 31.
    Collins GS, Reitsma JB, Altman DG, et al. (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BJOG 122:434-443. CrossRefPubMedGoogle Scholar
  32. 32.
    Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 26:565-574. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of RadiologyChanghai HospitalShanghaiChina
  2. 2.Department of PathologyChanghai HospitalShanghaiChina

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