Publication Name	World Journal of Radiology
Manuscript ID	118969
Country	China

Category	Radiology, Nuclear Medicine & Medical Imaging
Manuscript Type	Correspondence
Article Title	Letter to the Editor: Traditional medical image interpretation and deep learning-based image analysis in predicting risk in patients with spontaneous intracerebral hemorrhage
Manuscript Source	Unsolicited Manuscript
All Author List	Qiang Wang and Jian-She Yang
Funding Agency and Grant Number
Corresponding Author	Jian-She Yang, Department of Nuclear Medicine and Oncology Research, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yanchang Road, Shanghai 200072, China. 2305499@tongji.edu.cn
Key Words	Spontaneous intracerebral hemorrhage; Hematoma enlargement; Risk of death; Computed tomography; Magnetic resonance imaging; Deep learning model
Core Tip	In this Letter, we discuss the interpretation of medical images for predicting early hematoma enlargement in spontaneous intracerebral hemorrhage based on radiological features extracted through deep learning and traditional manual interpretation. We argue that artificial intelligence-based computer-aided diagnostic methods used to predict hematoma enlargement in spontaneous intracerebral hemorrhage on computed tomography images can help clinicians identify patients who would benefit from positive surgical intervention soon after admission.
Citation	Wang Q, Yang JS. Letter to the Editor: Traditional medical image interpretation and deep learning-based image analysis in predicting risk in patients with spontaneous intracerebral hemorrhage. World J Radiol 2026; In press

Received	2026-01-16 07:39
Peer-Review Started	2026-01-16 07:41
First Decision by Editorial Office Director	2026-02-12 08:24
Return for Revision	2026-02-12 08:24
Revised	2026-02-22 03:13
Publication Fee Transferred
Second Decision by Editor	2026-03-31 02:35
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-03-31 10:21
Articles in Press	2026-03-31 10:21
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1949-8470 (online)
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