Bibliographic Details
| Title: |
Genopathomic profiling identifies signatures for immunotherapy response of lung adenocarcinoma via confounder-aware representation learning |
| Authors: |
Jiajun Deng, Jiancheng Yang, Likun Hou, Junqi Wu, Yi He, Mengmeng Zhao, Bingbing Ni, Donglai Wei, Hanspeter Pfister, Caicun Zhou, Tao Jiang, Yunlang She, Chunyan Wu, Chang Chen |
| Source: |
iScience, Vol 25, Iss 11, Pp 105382- (2022) |
| Publisher Information: |
Elsevier, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Science |
| Subject Terms: |
Immunology, Cancer, Artificial intelligence, Science |
| More Details: |
Summary: Immunotherapy shows durable response but only in a subset of patients, and test for predictive biomarkers requires procedures in addition to routine workflow. We proposed a confounder-aware representation learning-based system, genopathomic biomarker for immunotherapy response (PITER), that uses only diagnosis-acquired hematoxylin-eosin (H&E)-stained pathological slides by leveraging histopathological and genetic characteristics to identify candidates for immunotherapy. PITER was generated and tested with three datasets containing 1944 slides of 1239 patients. PITER was found to be a useful biomarker to identify patients of lung adenocarcinoma with both favorable progression-free and overall survival in the immunotherapy cohort (p |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2589-0042 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2589004222016546; https://doaj.org/toc/2589-0042 |
| DOI: |
10.1016/j.isci.2022.105382 |
| Access URL: |
https://doaj.org/article/0e697dab45a44642aec9804adb7a7dd5 |
| Accession Number: |
edsdoj.0e697dab45a44642aec9804adb7a7dd5 |
| Database: |
Directory of Open Access Journals |
