Publication Name	World Journal of Radiology
Manuscript ID	116462
Country	China

Category	Surgery
Manuscript Type	Retrospective Study
Article Title	Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma
Manuscript Source	Invited Manuscript
All Author List	Yu-Han Yang
Funding Agency and Grant Number
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
Key Words	Soft-tissue sarcoma; Random survival forest; Systemic immune-inflammatory index; Controlling nutritional score; Malnutrition universal screening tool; Radiomic score; Nomogram; Prognosis
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.
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; In press

Received	2025-11-12 02:43
Peer-Review Started	2025-11-12 02:46
First Decision by Editorial Office Director	2025-12-09 08:39
Return for Revision	2025-12-09 08:39
Revised	2025-12-10 19:21
Publication Fee Transferred
Second Decision by Editor	2026-01-09 02:40
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-09 08:13
Articles in Press	2026-01-09 08:13
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1949-8470 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Copyright	© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com