Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 31, Issue 30

This Article

Academic Content and Language Evaluation 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

Number of Hits and Downloads for This Article

All Articles published online

Item

Count

PDF

HTML

287

Figures (1-6)

Tables (1-4)

Sum=386

Publishing Process of This Article

Item

Count

Browse

Download

Sum=3

Aug 14, 2025 (publication date) through Aug 12, 2025

Times Cited 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 Study

World J Gastroenterol. Aug 14, 2025; 31(30): 109186
Published online Aug 14, 2025. doi: 10.3748/wjg.v31.i30.109186

Table 1 Scanning parameters of different computed tomography devices

HU: Hounsfield units; ROI: Region of interest.

Citation: Cen YY, Nong HY, Huang XX, Lu XX, Pu CH, Huang LH, Zheng XJ, Pan ZL, Huang Y, Ding K, Huang DY. Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma. World J Gastroenterol 2025; 31(30): 109186
URL: https://www.wjgnet.com/1007-9327/full/v31/i30/109186.htm
DOI: https://dx.doi.org/10.3748/wjg.v31.i30.109186