©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
Published online Feb 28, 2026. doi: 10.4329/wjr.v18.i2.116462
Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma
Yu-Han Yang, West China School of Medicine, Sichuan University, Chengdu 6100041, Sichuan Province, China
Author contributions: Yang YH was responsible for conceptualization, study design and methodology, data collection and curation, computed tomography image segmentation and region of interest delineation, model development and statistical analysis, as well as writing-original draft preparation and writing-review and editing; and all authors approved the final manuscript.
Institutional review board statement: This study was approved by the Medical Ethics Committee of West China School of Medicine, Sichuan University.
Informed consent statement: The informed consent was waived by the Institutional Review Board.
Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.
Data sharing statement: Due to institutional policies and patient privacy considerations, raw imaging data or any data containing potentially identifying information will not be publicly released.
Corresponding author: Yu-Han Yang, West China School of Medicine, Sichuan University, No. 17 People’s South Road, Chengdu 6100041, Sichuan Province, China. yyh_1023@163.com
Received: November 12, 2025
Revised: December 10, 2025
Accepted: January 9, 2026
Published online: February 28, 2026
Processing time: 105 Days and 17.5 Hours
Revised: December 10, 2025
Accepted: January 9, 2026
Published online: February 28, 2026
Processing time: 105 Days and 17.5 Hours
Core Tip
Core Tip: This study explored the prognostic value of various nutritional assessment indicators for patients with soft tissue sarcomas receiving surgical resection. We constructed the prediction model for short- and long-term prognoses via multi-sided variables with potential prognostication in patients with resectable sarcomas. We applied machine learning methods to increase the prediction abilities of our model for identification of individual survival risk. The sample size was nearly 700 larger than most studies about nutritional prognostic factors especially for sarcomas.