European Radiology

, Volume 29, Issue 1, pp 362–372 | Cite as

CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis

  • Bo Ram Kim
  • Jung Hoon KimEmail author
  • Su Joa Ahn
  • Ijin Joo
  • Seo-Youn Choi
  • Sang Joon Park
  • Joon Koo Han



To assess utility of CT findings and texture analysis for predicting the resectability and prognosis in patients after neoadjuvant therapy for pancreatic ductal adenocarcinoma (PDAC).

Materials and methods

Among 308 patients, 45 with PDAC underwent neoadjuvant therapy (concurrent-chemoradiation-therapy, CCRT, n = 27 and chemotherapy, ChoT, n = 18) before surgery were included. All underwent baseline and preoperative CT. Two reviewers assessed CT findings and resectability. We analyzed relationship between CT resectability and residual tumor. CT texture values obtained by subtracting preoperative from baseline CT were analyzed using multivariate Cox/logistic regression analysis to identify significant parameters predicting resectability and prognosis.


There were 30 patients without residual tumor (CCRT, n = 20; ChoT, n = 10) and 15 with residual tumor (CCRT, n = 7; ChoT, n = 8). Considering borderline as resectable was more accurate for R0 resectability than considering borderline as unresectable (68.9% vs 55.6% and 51.1%, p < 0.001). Particularly, neoadjuvant CCRT provided better accuracy than that in (p < 0.001). In CT texture analysis, higher subtracted entropy (cut-off: 0.03, HR 0.159, p = 0.005) and lower subtracted GLCM entropy (cut-off: –0.35, HR 10.235, p = 0.036) are important parameters for prediction of longer overall survival.


CT findings with texture analysis can be useful for predicting a patient’s outcome, including resectability and prognosis, after neoadjuvant therapy for PDAC.

Key Points

• Considering borderline resectable tumor as resectable have better accuracy for resectability.

• Considering borderline as resectable, CCRT-patients have better resectability accuracy than chemotherapy-patients.

• Higher subtracted entropy and lower subtracted GLCM entropy are predictors of favorable outcome.


Pancreatic neoplasm Neoadjuvant therapy Pancreatectomy Prognosis Diagnosis 



Angular second moment


Celiac axis


Concurrent chemoradiation therapy


Grey level co-occurrence matrices


Hazard ratio


Inverse difference moment


Overall survival


Portal vein


Superior mesenteric vein



We also thank Bonnie Hami, M.A. (USA) for her editorial assistance in the preparation of this manuscript.


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

Compliance with ethical standards


The scientific guarantor of this publication is Joon Koo Han, M.D.

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

Seo-Youn Choi MD has significant statistical expertise and no complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Study subjects or cohorts overlap

Among 45 patients who were enrolled in our study, 13 patients have been previously reported in our previous paper (AJR Am J Roentgenol 2018, 210(5):1059–1065). However, the study purposes of these two studies were different. The previously published paper was a comparison of the diagnostic performance of CT in assessing tumor resectability pancreatic cancers after receiving neoadjuvant chemoradiation in comparison with those undergoing up-front surgery. The purpose of this study was to assess the utility of CT findings and texture analysis for predicting the resectability and prognosis in patients after neoadjuvant therapy for pancreatic cancer.


• Retrospective

• Diagnostic or prognostic study

• Performed at one institution


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologySeoul National University HospitalSeoulRepublic of Korea
  2. 2.Institute of Radiation MedicineSeoul National University College of MedicineSeoulKorea
  3. 3.Department of RadiologySoonchunhyang University Bucheon HospitalBucheonKorea

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