Publication Name	World Journal of Radiology
Manuscript ID	115504
Country	China

Category	Computer Science, Artificial Intelligence
Manuscript Type	Retrospective Cohort Study
Article Title	Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage
Manuscript Source	Invited Manuscript
All Author List	Yu-Han Yang and Yuan Li
Funding Agency and Grant Number
Corresponding Author	Yu-Han Yang, MD, West China Hospital, Sichuan University, No. 17 People’s South Road, Chengdu 6100041, Sichuan Province, China. yyh_1023@163.com
Key Words	Intracerebral hemorrhage; Enlargement; Computed tomography; Radiomics; Deep learning; Hospital mortality
Core Tip	In this work, we developed quantitative and easy-to-reach prediction tools for early hematoma enlargement in spontaneous intracerebral hemorrhage based on the radiological features from deep learning or handcrafted radiomics methods, and validated the predictive models in an independent cohort to assure their discriminative capacities. The artificial intelligence based computer aided diagnosis methods we used to predict hematoma enlargement in spontaneous intracerebral hemorrhage on computed tomography images would assist making decisions about whether clinicians should implement positive surgical intervention or not at early stage once admission.
Citation	Yang YH, Li Y. Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage. World J Radiol 2026; In press

Received	2025-10-20 02:08
Peer-Review Started	2025-10-20 02:08
First Decision by Editorial Office Director	2025-11-11 08:49
Return for Revision	2025-11-11 08:49
Revised	2025-11-17 17:51
Publication Fee Transferred
Second Decision by Editor	2026-01-06 02:48
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-06 08:10
Articles in Press	2026-01-06 08:10
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/
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