Development and validation of a predictive model for portal-systemic venous invasion grading in borderline resectable pancreatic cancer

doi:10.3748/wjg.v31.i42.112354

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 31, Issue 42

This Article

(69)

(41)

(0)

(13)

(1)

(692)

(2)

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Supplementary Materials of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Journal Information of This Article

Publication Name

World Journal of Gastroenterology

ISSN

1007-9327

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Cohort Study

World J Gastroenterol. Nov 14, 2025; 31(42): 112354
Published online Nov 14, 2025. doi: 10.3748/wjg.v31.i42.112354

Full Size Figure Download Figure

Figure 3 Nomogram for predicting pathological portal-systemic venous invasion grade in borderline resectable pancreatic cancer. This nomogram incorporates 3 independent predictors identified by multivariable analysis: Preoperative serum carbohydrate antigen 19-9 (U/mL), circumferential involvement (°), and luminal compromise (%). CA19-9: Carbohydrate antigen 19-9.

Citation: Wang FF, Dai XD, Zhao X, He Q, Lyu SC. Development and validation of a predictive model for portal-systemic venous invasion grading in borderline resectable pancreatic cancer. World J Gastroenterol 2025; 31(42): 112354
URL: https://www.wjgnet.com/1007-9327/full/v31/i42/112354.htm
DOI: https://dx.doi.org/10.3748/wjg.v31.i42.112354

Wang FF, Dai XD, Zhao X, He Q, Lyu SC. Development and validation of a predictive model for portal-systemic venous invasion grading in borderline resectable pancreatic cancer. World J Gastroenterol 2025; 31(42): 112354 [PMID: 41278166 DOI: 10.3748/wjg.v31.i42.112354]

All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.