2021
DOI: 10.1016/j.tranon.2021.101016
|View full text |Cite
|
Sign up to set email alerts
|

Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
19
0
1

Year Published

2021
2021
2023
2023

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 24 publications
(20 citation statements)
references
References 31 publications
0
19
0
1
Order By: Relevance
“…Several retrospective studies have attempted to assess the prediction accuracy of these tests performed on biopsy specimens from patients with CUP. Using correlation with clinicopathological features, the IHC profile or the identification of a latent primary as prediction comparator, these molecular based tissue of origin classifiers yield prediction accuracy from 60% to 92% [15,[21][22][23][24][25][26][27][28][29][30]. This is corroborated by a prospective study demonstrating an 84% agreement of molecular profile with clinicopathological diagnosis [31].…”
Section: Tissue Of Origin Classifier Assaysmentioning
confidence: 67%
See 1 more Smart Citation
“…Several retrospective studies have attempted to assess the prediction accuracy of these tests performed on biopsy specimens from patients with CUP. Using correlation with clinicopathological features, the IHC profile or the identification of a latent primary as prediction comparator, these molecular based tissue of origin classifiers yield prediction accuracy from 60% to 92% [15,[21][22][23][24][25][26][27][28][29][30]. This is corroborated by a prospective study demonstrating an 84% agreement of molecular profile with clinicopathological diagnosis [31].…”
Section: Tissue Of Origin Classifier Assaysmentioning
confidence: 67%
“…Even with such sophisticated tools, this yields in 71.7% of prediction in CUPs. This approach also have the originality to provide, at the same time, results about potential targetable genomic alteration [30].…”
Section: T Olivier Et Almentioning
confidence: 99%
“…Although more research is indeed necessary to further elucidate VHL's role in the development of such tumors, our case suggests that molecular characterization of tumors provides information not obtainable by histological examination. Molecular signatures such as GPSai TM scoring contribute to a greater understanding of tumor genesis, as such tools have been found to have an accuracy of 94% in identifying tumor type when matching with a high probability [ 15 ]. Furthermore, commercially available NGS and GPS are tools available to clinicians and can provide significant utility in establishing diagnoses and assessing responses to treatments.…”
Section: Discussionmentioning
confidence: 99%
“…Also, CDH1 mutation, which is frequently reported in plasmacytoid UC, was not identified [ 9 ]. Furthermore, we applied a Genomic Prevalence Score (MI GPSai™) using whole-exome sequencing and whole transcriptome (RNA) analysis coupled with machine learning to confirm tumor origin [ 14 ]. MI GPSai predicts the tumor type with an accuracy of 94% by matching tumor molecular signature across 21 cancer types.…”
Section: Molecular Studiesmentioning
confidence: 99%