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©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Jan 28, 2026; 18(1): 115504
Published online Jan 28, 2026. doi: 10.4329/wjr.v18.i1.115504
Published online Jan 28, 2026. doi: 10.4329/wjr.v18.i1.115504
Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage
Yu-Han Yang, West China Hospital, Sichuan University, Chengdu 6100041, Sichuan Province, China
Yuan Li, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, Laboratory of Digestive Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Yang YH drafted the manuscript and performed critical revision of the manuscript; Li Y and Yang YH contributed to the study conception and design, data acquisition, and analysis and interpretation of data; and all authors read and approved the 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 requirement for written informed consent was waived by the institutional review boards.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: De-identified individual participant data that underlie the results reported in this article are available from the corresponding author upon reasonable request. Data sharing is subject to approval by the relevant institutional review boards and execution of a data-use agreement to ensure protection of patient privacy and compliance with applicable regulations. Due to institutional policies and patient privacy considerations, raw imaging data or any data containing potentially identifying information will not be publicly released.
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: https://creativecommons.org/Licenses/by-nc/4.0/
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
Received: October 20, 2025
Revised: November 17, 2025
Accepted: January 6, 2026
Published online: January 28, 2026
Processing time: 100 Days and 13.4 Hours
Revised: November 17, 2025
Accepted: January 6, 2026
Published online: January 28, 2026
Processing time: 100 Days and 13.4 Hours
Core Tip
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.