Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma

doi:10.4329/wjr.v18.i2.116462

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 18, Issue 2

This Article

(33)

(32)

(0)

(496)

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 Radiology

ISSN

1949-8470

Publisher of This Article

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

Retrospective Study

©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.

World J Radiol. Feb 28, 2026; 18(2): 116462
Published online Feb 28, 2026. doi: 10.4329/wjr.v18.i2.116462

Table 2 Prediction accuracy of nomogram

Variable	C-index				Brier score
Variable	Three-year		Five-year		Three-year		Five-year
PFS	Cox selection	RSF selection	Cox selection	RSF selection	Cox selection	RSF selection	Cox selection	RSF selection
Training cohort	0.635 (0.576-0.694)	0.659 (0.600-0.718)	0.653 (0.574-0.733)	0.695 (0.614-0.776)	23.4 (13.5-33.2)	22.9 (13.3-32.5)	22.9 (10.5-35.2)	22.2 (10.2-34.2)
Testing cohort	0.601 (0.504-0.698)	0.675 (0.583-0.767)	0.615 (0.482-0.749)	0.702 (0.586-0.817)	23.6 (17.0-30.1)	22.3 (16.0-28.6)	23.5 (14.7-32.3)	21.6 (13.6-29.7)
OS
Training cohort	0.573 (0.511-0.634)	0.581 (0.519-0.642)	0.660 (0.595-0.724)	0.657 (0.592-0.723)	22.4 (20.0-24.9)	22.3 (19.8-24.8)	18.8 (16.8-20.9)	18.7 (16.7-20.8)
Testing cohort	0.474 (0.378-0.569)	0.473 (0.377-0.568)	0.564 (0.444-0.684)	0.584 (0.466-0.703)	25.8 (22.3-29.3)	25.6 (22.1-29.0)	19.4 (16.0-22.9)	19.2 (15.8-22.6)

Prediction accuracy of nomogram using different variable selection methods for progression-free survival and overall survival in the training and testing sets of patients with soft-tissue sarcomas receiving surgical resection. OS: Overall survival; PFS: Progression-free survival; RSF: Random survival forest.

Full Size Table

Citation: Yang YH. Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma. World J Radiol 2026; 18(2): 116462
URL: https://www.wjgnet.com/1949-8470/full/v18/i2/116462.htm
DOI: https://dx.doi.org/10.4329/wjr.v18.i2.116462

Yang YH. Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma. World J Radiol 2026; 18(2): 116462 [PMID: 41809710 DOI: 10.4329/wjr.v18.i2.116462]

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.