Idioma:

Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates

Kong, Jun ; Cooper, Lee A D ; Wang, Fusheng ; Gao, Jingjing ; Teodoro, George ; Scarpace, Lisa ; Mikkelsen, Tom ; Schniederjan, Matthew J ; Moreno, Carlos S ; Saltz, Joel H ; Brat, Daniel J Andrade-Navarro, Miguel A

PloS one, 2013-11, Vol.8 (11), p.e81049 [Periódico revisado por pares]

United States: Public Library of Science

Texto completo disponível

Citações Citado por

Enviar para

Título:
Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates
Autor: Kong, Jun ; Cooper, Lee A D ; Wang, Fusheng ; Gao, Jingjing ; Teodoro, George ; Scarpace, Lisa ; Mikkelsen, Tom ; Schniederjan, Matthew J ; Moreno, Carlos S ; Saltz, Joel H ; Brat, Daniel J
Andrade-Navarro, Miguel A
Assuntos: Algorithms ; Brain cancer ; Brain Neoplasms - diagnosis ; Brain Neoplasms - genetics ; Brain Neoplasms - mortality ; Brain Neoplasms - pathology ; Cancer ; Cell Nucleus - pathology ; Classification ; Cluster Analysis ; Correlation analysis ; Data integration ; Data processing ; Digitization ; Evaluation ; Gene expression ; Gene Expression Profiling ; Genetic transcription ; Genomes ; Glioblastoma ; Glioblastoma - diagnosis ; Glioblastoma - genetics ; Glioblastoma - mortality ; Glioblastoma - pathology ; Glioblastoma multiforme ; Glioblastomas ; Humans ; Image analysis ; Image processing ; Image Processing, Computer-Assisted ; Informatics ; Laboratories ; Medical research ; Microscopy ; Morphology ; Nuclei ; Oligodendrocyte-myelin glycoprotein ; Oligodendroglioma ; Oligodendroglioma - genetics ; Oligodendroglioma - pathology ; Pathology ; Patient Outcome Assessment ; Platelet-derived growth factor ; Regression analysis ; Regression models ; Reproducibility ; Reproducibility of Results ; Survival analysis ; Transcription ; Transcription (Genetics) ; Tumors
É parte de: PloS one, 2013-11, Vol.8 (11), p.e81049
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: JK DJB. Performed the experiments: JK LADC FW JG GT MJS DJB. Analyzed the data: JK LADC FW JG GT CSM DJB. Contributed reagents/materials/analysis tools: JK LS TM MJS JHS DJB. Wrote the manuscript: JK DJB.
Descrição: Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human review has limitations that can result in low reproducibility and inter-observer agreement. Computerized image analysis can partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure histologic structures on a large-scale. In this paper, we present an end-to-end image analysis and data integration pipeline for large-scale morphologic analysis of pathology images and demonstrate the ability to correlate phenotypic groups with molecular data and clinical outcomes. We demonstrate our method in the context of glioblastoma (GBM), with specific focus on the degree of the oligodendroglioma component. Over 200 million nuclei in digitized pathology slides from 117 GBMs in the Cancer Genome Atlas were quantitatively analyzed, followed by multiplatform correlation of nuclear features with molecular and clinical data. For each nucleus, a Nuclear Score (NS) was calculated based on the degree of oligodendroglioma appearance, using a regression model trained from the optimal feature set. Using the frequencies of neoplastic nuclei in low and high NS intervals, we were able to cluster patients into three well-separated disease groups that contained low, medium, or high Oligodendroglioma Component (OC). We showed that machine-based classification of GBMs with high oligodendroglioma component uncovered a set of tumors with strong associations with PDGFRA amplification, proneural transcriptional class, and expression of the oligodendrocyte signature genes MBP, HOXD1, PLP1, MOBP and PDGFRA. Quantitative morphologic features within the GBMs that correlated most strongly with oligodendrocyte gene expression were high nuclear circularity and low eccentricity. These findings highlight the potential of high throughput morphologic analysis to complement and inform human-based pathologic review.
Editor: United States: Public Library of Science
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates

Kong, Jun ; Cooper, Lee A D ; Wang, Fusheng ; Gao, Jingjing ; Teodoro, George ; Scarpace, Lisa ; Mikkelsen, Tom ; Schniederjan, Matthew J ; Moreno, Carlos S ; Saltz, Joel H ; Brat, Daniel J Andrade-Navarro, Miguel A

United States: Public Library of Science

Buscando em bases de dados remotas. Favor aguardar.