Publication Name	Artificial Intelligence in Cancer
Manuscript ID	116460
Country	China

Category	Orthopedics
Manuscript Type	Retrospective Cohort Study
Article Title	Magnetic resonance imaging-based deep learning model for prediction of the neoadjuvant chemotherapy response and survival prognosis in adolescents with osteosarcoma
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	Osteosarcoma; Magnetic resonance imaging; Neoadjuvant chemotherapy; Deep learning; Convolutional neural network
Core Tip	We developed and externally validated prediction models for histopathological diagnosis of resectable bone sarcomas using radiological features derived from both deep learning and handcrafted radiomics approaches. To our knowledge, this is the first study using pre-trained convolutional neural networks via transfer learning method to identify osteosarcoma and chondrosarcoma and predict long-term survival outcomes based on T1 and T2 magnetic resonance imaging images.
Citation	Yang YH. Magnetic resonance imaging-based deep learning model for prediction of the neoadjuvant chemotherapy response and survival prognosis in adolescents with osteosarcoma. Artif Intell Cancer 2026; In press

Received	2025-11-21 08:01
Peer-Review Started	2025-11-21 08:02
First Decision by Editorial Office Director	2025-12-02 08:09
Return for Revision	2025-12-02 08:09
Revised	2025-12-03 17:24
Publication Fee Transferred
Second Decision by Editor	2026-02-02 02:45
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-02-02 14:36
Articles in Press	2026-02-02 14:36
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2644-3228 (online)
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