| Autor: |
Brattoli, Biagio, Mostafavi, Mohammad, Lee, Taebum, Jung, Wonkyung, Ryu, Jeongun, Park, Seonwook, Park, Jongchan, Pereira, Sergio, Shin, Seunghwan, Choi, Sangjoon, Kim, Hyojin, Yoo, Donggeun, Ali, Siraj M., Paeng, Kyunghyun, Ock, Chan-Young, Cho, Soo Ick, Kim, Seokhwi |
| Zdroj: |
NPJ Precision Oncology; 12/3/2024, Vol. 8 Issue 1, p1-13, 13p |
| Abstrakt: |
Immunohistochemistry (IHC) is the common companion diagnostics in targeted therapies. However, quantifying protein expressions in IHC images present a significant challenge, due to variability in manual scoring and inherent subjective interpretation. Deep learning (DL) offers a promising approach to address these issues, though current models require extensive training for each cancer and IHC type, limiting the practical application. We developed a Universal IHC (UIHC) analyzer, a DL-based tool that quantifies protein expression across different cancers and IHC types. This multi-cohort trained model outperformed conventional single-cohort models in analyzing unseen IHC images (Kappa score 0.578 vs. up to 0.509) and demonstrated consistent performance across varying positive staining cutoff values. In a discovery application, the UIHC model assigned higher tumor proportion scores to MET amplification cases, but not MET exon 14 splicing or other non-small cell lung cancer cases. This UIHC model represents a novel role for DL that further advances quantitative analysis of IHC. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Full text from SpringerLink