A Need for Multi-Institutional Collaboration for Deep Learning-Driven Assessment of Osteosarcoma Treatment Response.
作者信息
Ho David Joon, Agaram Narasimhan P, Healey John H, Hameed Meera R
机构信息
Department of Cancer AI and Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Republic of Korea.
Departments of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
出版信息
Am J Pathol. 2025 Jun;195(6):1036-1039. doi: 10.1016/j.ajpath.2025.02.002. Epub 2025 Mar 7.
Abstract
摘要
相似文献
1
A Need for Multi-Institutional Collaboration for Deep Learning-Driven Assessment of Osteosarcoma Treatment Response.骨肉瘤治疗反应的深度学习驱动评估需要多机构合作。
Am J Pathol. 2025 Jun;195(6):1036-1039. doi: 10.1016/j.ajpath.2025.02.002. Epub 2025 Mar 7.
2
FedSynthCT-Brain: A federated learning framework for multi-institutional brain MRI-to-CT synthesis.FedSynthCT-脑：一种用于多机构脑磁共振成像到计算机断层扫描合成的联邦学习框架。
Comput Biol Med. 2025 Jun;192(Pt A):110160. doi: 10.1016/j.compbiomed.2025.110160. Epub 2025 Apr 22.
3
Do Patients of Different Levels of Affluence Receive Different Care for Pediatric Osteosarcomas? One Institution's Experience.不同富裕程度的患者在小儿骨肉瘤治疗上是否得到不同的护理？一家机构的经验。
Clin Orthop Relat Res. 2025 Apr 1;483(4):748-758. doi: 10.1097/CORR.0000000000003299. Epub 2024 Oct 30.
4
Integrating multi-source data for skin burn classification using deep learning.利用深度学习整合多源数据进行皮肤烧伤分类
Comput Biol Med. 2025 Sep;195:110556. doi: 10.1016/j.compbiomed.2025.110556. Epub 2025 Jun 24.
5
The measurement of collaboration within healthcare settings: a systematic review of measurement properties of instruments.医疗机构内协作的测量：对测量工具属性的系统评价
JBI Database System Rev Implement Rep. 2016 Apr;14(4):138-97. doi: 10.11124/JBISRIR-2016-2159.
6
The safety and efficiency of photodynamic therapy for the treatment of osteosarcoma: A systematic review of in vitro experiment and animal model reports.光动力疗法治疗骨肉瘤的安全性和有效性：体外实验和动物模型报告的系统评价
Photodiagnosis Photodyn Ther. 2022 Dec;40:103093. doi: 10.1016/j.pdpdt.2022.103093. Epub 2022 Aug 27.
7
Effectiveness of mifamurtide in addition to standard chemotherapy for high-grade osteosarcoma: a systematic review.米法木肽联合标准化疗治疗高级别骨肉瘤的有效性：一项系统评价
JBI Database System Rev Implement Rep. 2017 Aug;15(8):2113-2152. doi: 10.11124/JBISRIR-2016-003105.
8
Recent advances in deep learning for protein-protein interaction: a review.深度学习在蛋白质-蛋白质相互作用研究中的最新进展：综述
BioData Min. 2025 Jun 16;18(1):43. doi: 10.1186/s13040-025-00457-6.
9
A deep learning model for predicting systemic lupus erythematosus-associated epitopes.一种用于预测系统性红斑狼疮相关表位的深度学习模型。
BMC Med Inform Decis Mak. 2025 Jul 1;25(1):230. doi: 10.1186/s12911-025-03056-x.
10
Radiomics-based machine learning in prediction of response to neoadjuvant chemotherapy in osteosarcoma: A systematic review and meta-analysis.基于影像组学的机器学习在骨肉瘤新辅助化疗反应预测中的应用：一项系统综述和荟萃分析
Clin Imaging. 2025 Jul;123:110494. doi: 10.1016/j.clinimag.2025.110494. Epub 2025 May 8.
本文引用的文献
1
Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms.下一代肺癌病理学：诊断和预后算法的开发和验证。
Cell Rep Med. 2024 Sep 17;5(9):101697. doi: 10.1016/j.xcrm.2024.101697. Epub 2024 Aug 22.
2
Viable tumor cell density after neoadjuvant chemotherapy assessed using deep learning model reflects the prognosis of osteosarcoma.使用深度学习模型评估新辅助化疗后的 viable 肿瘤细胞密度可反映骨肉瘤的预后。 （注：这里“viable”常见释义为“可行的；能存活的” ，结合语境这里可能更偏向“能存活的” ，但不确定原文中该词是否有特定含义，如果有更准确的专业释义，可根据实际情况调整。）
NPJ Precis Oncol. 2024 Jan 22;8(1):16. doi: 10.1038/s41698-024-00515-y.
3
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer.一种用于提高浸润性乳腺癌预后的人群水平数字组织学生物标志物。
Nat Med. 2024 Jan;30(1):85-97. doi: 10.1038/s41591-023-02643-7. Epub 2023 Nov 27.
4
Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction.基于深度学习的骨肉瘤化疗反应评估和预后预测的客观和可重现性。
Am J Pathol. 2023 Mar;193(3):341-349. doi: 10.1016/j.ajpath.2022.12.004. Epub 2022 Dec 21.
5
Federated learning enables big data for rare cancer boundary detection.联邦学习为罕见癌症边界检测提供大数据支持。
Nat Commun. 2022 Dec 5;13(1):7346. doi: 10.1038/s41467-022-33407-5.
6
Deep learning in histopathology: the path to the clinic.深度学习在组织病理学中的应用：通往临床的道路。
Nat Med. 2021 May;27(5):775-784. doi: 10.1038/s41591-021-01343-4. Epub 2021 May 14.
7
Deep Multi-Magnification Networks for multi-class breast cancer image segmentation.用于多类乳腺癌图像分割的深度多重放大网络。
Comput Med Imaging Graph. 2021 Mar;88:101866. doi: 10.1016/j.compmedimag.2021.101866. Epub 2021 Jan 12.
8
The future of digital health with federated learning.联合学习助力数字健康的未来。
NPJ Digit Med. 2020 Sep 14;3:119. doi: 10.1038/s41746-020-00323-1. eCollection 2020.
9
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.Hover-Net：多组织组织学图像中细胞核的同时分割和分类。
Med Image Anal. 2019 Dec;58:101563. doi: 10.1016/j.media.2019.101563. Epub 2019 Sep 18.
10
Perioperative chemotherapy in the treatment of osteosarcoma: a 26-year single institution review.骨肉瘤治疗中的围手术期化疗：一项长达26年的单机构回顾性研究
Clin Sarcoma Res. 2015 Jul 14;5:17. doi: 10.1186/s13569-015-0032-0. eCollection 2015.
Zotero 插件