PMID- 37717080
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231122
IS  - 2397-768X (Print)
IS  - 2397-768X (Electronic)
IS  - 2397-768X (Linking)
VI  - 7
IP  - 1
DP  - 2023 Sep 16
TI  - Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion 
      status using weakly-supervised learning.
PG  - 94
LID - 10.1038/s41698-023-00450-4 [doi]
LID - 94
AB  - Accurate identification of molecular alterations in gliomas is crucial for their 
      diagnosis and treatment. Although, fluorescence in situ hybridization (FISH) 
      allows for the observation of diverse and heterogeneous alterations, it is 
      inherently time-consuming and challenging due to the limitations of the molecular 
      method. Here, we report the development of 1p/19qNET, an advanced deep-learning 
      network designed to predict fold change values of 1p and 19q chromosomes and 
      classify isocitrate dehydrogenase (IDH)-mutant gliomas from whole-slide images. 
      We trained 1p/19qNET on next-generation sequencing data from a discovery set (DS) 
      of 288 patients and utilized a weakly-supervised approach with slide-level labels 
      to reduce bias and workload. We then performed validation on an independent 
      validation set (IVS) comprising 385 samples from The Cancer Genome Atlas, a 
      comprehensive cancer genomics resource. 1p/19qNET outperformed traditional FISH, 
      achieving R(2) values of 0.589 and 0.547 for the 1p and 19q arms, respectively. 
      As an IDH-mutant glioma classifier, 1p/19qNET attained AUCs of 0.930 and 0.837 in 
      the DS and IVS, respectively. The weakly-supervised nature of 1p/19qNET provides 
      explainable heatmaps for the results. This study demonstrates the successful use 
      of deep learning for precise determination of 1p/19q codeletion status and 
      classification of IDH-mutant gliomas as astrocytoma or oligodendroglioma. 
      1p/19qNET offers comparable results to FISH and provides informative spatial 
      information. This approach has broader applications in tumor classification.
CI  - (c) 2023. Nature Publishing Group UK.
FAU - Kim, Gi Jeong
AU  - Kim GJ
AUID- ORCID: 0000-0002-8843-588X
AD  - Department of Pathology, Severance Hospital, Yonsei University College of 
      Medicine, Seoul, Republic of Korea.
AD  - Department of Medicine, Yonsei University Graduate School, Seoul, Republic of 
      Korea.
FAU - Lee, Tonghyun
AU  - Lee T
AD  - Department of Artificial Intelligence, Yonsei University College of Computing, 
      Seoul, Republic of Korea.
FAU - Ahn, Sangjeong
AU  - Ahn S
AD  - Department of Pathology, Korea University Anam Hospital, Korea University College 
      of Medicine, Seoul, Republic of Korea.
FAU - Uh, Youngjung
AU  - Uh Y
AD  - Department of Artificial Intelligence, Yonsei University College of Computing, 
      Seoul, Republic of Korea. yj.uh@yonsei.ac.kr.
FAU - Kim, Se Hoon
AU  - Kim SH
AD  - Department of Pathology, Severance Hospital, Yonsei University College of 
      Medicine, Seoul, Republic of Korea. paxco@yuhs.ac.
LA  - eng
GR  - 6-2022-0041/Yonsei University | Yonsei University College of Medicine (YUCM)/
PT  - Journal Article
DEP - 20230916
PL  - England
TA  - NPJ Precis Oncol
JT  - NPJ precision oncology
JID - 101708166
PMC - PMC10505231
COIS- The authors declare no competing interests.
EDAT- 2023/09/17 00:41
MHDA- 2023/09/17 00:42
PMCR- 2023/09/16
CRDT- 2023/09/16 23:30
PHST- 2023/05/24 00:00 [received]
PHST- 2023/09/05 00:00 [accepted]
PHST- 2023/09/17 00:42 [medline]
PHST- 2023/09/17 00:41 [pubmed]
PHST- 2023/09/16 23:30 [entrez]
PHST- 2023/09/16 00:00 [pmc-release]
AID - 10.1038/s41698-023-00450-4 [pii]
AID - 450 [pii]
AID - 10.1038/s41698-023-00450-4 [doi]
PST - epublish
SO  - NPJ Precis Oncol. 2023 Sep 16;7(1):94. doi: 10.1038/s41698-023-00450-4.