Development of Prognostic Biomarkers by TMB-Guided WSI Analysis: A Two-Step Approach-Reference-Cited by-同舟云学术

Development of Prognostic Biomarkers by TMB-Guided WSI Analysis: A Two-Step Approach

Published:2023-04 Issue:4 Volume:27 Page:1780-1789
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Liu Xiangyu¹^ORCID,Liu Zhenyu²^ORCID,Yan Ye³,Wang Kai⁴,Wang Aodi⁴,Ye Xiongjun⁵,Wang Liwei⁴,Wei Wei²^ORCID,Li Bao²^ORCID,Sun Caixia⁶,He Wei³,Zhu Xuehua³,Liu Zenan³,Liu Jiangang⁶^ORCID,Lu Jian³^ORCID,Tian Jie¹^ORCID

Affiliation:

1. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China

2. CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

3. Department of Urology, Peking University Third Hospital, Peking University, Beijing, China

4. OrigiMed, Shanghai, China

5. Urology and Lithotripsy Center, Peking University People's Hospital, Peking University, Beijing, China

6. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Natural Science Basic Research Program of Shaanxi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10092240/10054000.pdf?arnumber=10054000

Reference40 articles.

1. Exploration of the relationships between tumor mutation burden with immune infiltrates in clear cell renal cell carcinoma

2. AI-based pathology predicts origins for cancers of unknown primary

3. High tumor mutation burden predicts better efficacy of immunotherapy: a pooled analysis of 103078 cancer patients

4. WSISA: Making Survival Prediction from Whole Slide Histopathological Images

5. Histopathology of Surgically Treated Renal Cell Carcinoma: Survival Differences by Subtype and Stage

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning;Cancer Medicine;2024-08

2. Development of Intelligent Revisiting Method of Making ROC Curves for CFE for Finiding Lung Cancer;2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT);2024-02-09

3. Sparse and Hierarchical Transformer for Survival Analysis on Whole Slide Images;IEEE Journal of Biomedical and Health Informatics;2024-01

4. Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis;The Innovation Medicine;2024

5. Tumor Mutation Burden–Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning;The American Journal of Pathology;2023-12