Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
Artif Intell Cancer. Sep 8, 2026; 7(1): 116460
Published online Sep 8, 2026. doi: 10.35713/aic.v7.i1.116460
Published online Sep 8, 2026. doi: 10.35713/aic.v7.i1.116460
Magnetic resonance imaging-based deep learning model for prediction of the neoadjuvant chemotherapy response and survival prognosis in adolescents with osteosarcoma
Yu-Han Yang, West China School of Medicine, Sichuan University, Chengdu 6100041, Sichuan Province, China
Author contributions: Yang YH contributed to study conception and design, data acquisition, analysis and interpretation, draft and critical revise the manuscript.
Institutional review board statement: This retrospective study involving human participants was reviewed and approved by the Institutional Review Boards of the participating institutions. All procedures were conducted in accordance with the ethical standards of the institutional and/or national research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: The author reports 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.
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
Received: November 21, 2025
Revised: December 3, 2025
Accepted: February 2, 2026
Published online: September 8, 2026
Processing time: 294 Days and 18.8 Hours
Revised: December 3, 2025
Accepted: February 2, 2026
Published online: September 8, 2026
Processing time: 294 Days and 18.8 Hours
Core Tip
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.