GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma
    Springer Science and Business Media LLC ; 2021
    In:  British Journal of Cancer Vol. 125, No. 3 ( 2021-08-03), p. 337-350
    Details
    In: British Journal of Cancer, Springer Science and Business Media LLC, Vol. 125, No. 3 ( 2021-08-03), p. 337-350
    Abstract: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. Methods We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. Results We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. Conclusions This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .
    Type of Medium: Online Resource
    ISSN: 0007-0920 , 1532-1827
    URL: Article
    DOI: 10.1038/s41416-021-01394-x
    RVK:
    XA 33500
    RVK:
    XA 10000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2002452-6
    detail.hit.zdb_id: 80075-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Abstract PO-004: A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma
    American Association for Cancer Research (AACR) ; 2021
    In:  Clinical Cancer Research Vol. 27, No. 5_Supplement ( 2021-03-01), p. PO-004-PO-004
    Details
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 27, No. 5_Supplement ( 2021-03-01), p. PO-004-PO-004
    Abstract: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. Here, we have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading-edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Analysis of segmentation results from tumour images together with matched RNA expression data identified genetic signatures that are specific to these different tumour regions. We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Moreover, in silico cell ontology analysis and single-cell RNA sequencing data from resected glioblastoma tissue samples, showed that these tumour regions had different gene signatures, suggesting they are driven by different cell types in the tumour microenvironment. This points to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. Overall, this work identifies key histopathological features that are indicative of patient survival and detected spatially associated genetic signatures that mediate tumour-stroma interactions that should be investigated as new targets in glioblastoma. Citation Format: Amin Zadeh Shirazi, Mark D. McDonnell, Eric Fornaciari, Narjes Sadat Bagherian, Kaitlin G. Scheer, Michael S. Samuel, Mahdi Yaghoobi, Rebecca J. Ormsby, Santosh Poonnoose, Damon Tumes, Guillermo A. Gomez. A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma [abstract] . In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-004.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    URL: Article
    DOI: 10.1158/1557-3265.ADI21-PO-004
    RVK:
    XA 36791
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
    detail.hit.zdb_id: 1225457-5
    detail.hit.zdb_id: 2036787-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images
    Springer Science and Business Media LLC ; 2020
    In:  Medical & Biological Engineering & Computing Vol. 58, No. 5 ( 2020-05), p. 1031-1045
    Details
    In: Medical & Biological Engineering & Computing, Springer Science and Business Media LLC, Vol. 58, No. 5 ( 2020-05), p. 1031-1045
    Abstract: Histopathological whole slide images of haematoxylin and eosin (H & E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients’ survival, which can help in scheduling patients therapeutic treatment and allocate time for preclinical studies to guide personalized treatments. We now propose a new classifier, namely, DeepSurvNet powered by deep convolutional neural networks, to accurately classify in 4 classes brain cancer patients’ survival rate based on histopathological images (class I, 0–6 months; class II, 6–12 months; class III, 12–24 months; and class IV, 〉 24 months survival after diagnosis). After training and testing of DeepSurvNet model on a public brain cancer dataset, The Cancer Genome Atlas, we have generalized it using independent testing on unseen samples. Using DeepSurvNet, we obtained precisions of 0.99 and 0.8 in the testing phases on the mentioned datasets, respectively, which shows DeepSurvNet is a reliable classifier for brain cancer patients’ survival rate classification based on histopathological images. Finally, analysis of the frequency of mutations revealed differences in terms of frequency and type of genes associated to each class, supporting the idea of a different genetic fingerprint associated to patient survival. We conclude that DeepSurvNet constitutes a new artificial intelligence tool to assess the survival rate in brain cancer.
    Type of Medium: Online Resource
    ISSN: 0140-0118 , 1741-0444
    URL: Article
    DOI: 10.1007/s11517-020-02147-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 282327-5
    detail.hit.zdb_id: 2052667-2
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...