In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response

Jun Kong; Lee Cooper; Carlos Moreno; Fusheng Wang; Tahsin Kurc; Joel Saltz; Daniel Brat

doi:10.1109/IEMBS.2011.6089903

In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response

Jun Kong
, Lee Cooper
, Carlos Moreno
, Fusheng Wang
, Tahsin Kurc
, Joel Saltz
, Daniel Brat

Biomedical Informatics

Stony Brook University

Emory University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

In this paper, we present a complete and novel workflow for quantitative nuclear feature analysis of glioblas-toma using high-throughput whole-slide microscopy image processing as it relates to treatment response and patient survival. With a complete suite of computer algorithms, large numbers of micro-anatomical structures, in this case nuclei, are analyzed and represented efficiently from whole-slide digitized images with numerical features. With regard to endpoints of treatment response, the computerized analysis presents a better discrimination than traditional neuropathologic review. As a result, this analysis method shows potential to facilitate a better understanding of disease progression and patients' response to therapy for glioblastoma.

Original language	English
Title of host publication	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages	87-90
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2011.6089903
State	Published - 2011
Event	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States Duration: Aug 30 2011 → Sep 3 2011

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Conference

Conference	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Country/Territory	United States
City	Boston, MA
Period	08/30/11 → 09/3/11

Access to Document

10.1109/IEMBS.2011.6089903

Cite this

Kong, J., Cooper, L., Moreno, C., Wang, F., Kurc, T., Saltz, J., & Brat, D. (2011). In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 (pp. 87-90). Article 6089903 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/IEMBS.2011.6089903

Kong, Jun ; Cooper, Lee ; Moreno, Carlos et al. / In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response. 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011. 2011. pp. 87-90 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{3eccdbcfb0474aefb0b2d30dcf9d9933,

title = "In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response",

abstract = "In this paper, we present a complete and novel workflow for quantitative nuclear feature analysis of glioblas-toma using high-throughput whole-slide microscopy image processing as it relates to treatment response and patient survival. With a complete suite of computer algorithms, large numbers of micro-anatomical structures, in this case nuclei, are analyzed and represented efficiently from whole-slide digitized images with numerical features. With regard to endpoints of treatment response, the computerized analysis presents a better discrimination than traditional neuropathologic review. As a result, this analysis method shows potential to facilitate a better understanding of disease progression and patients' response to therapy for glioblastoma.",

author = "Jun Kong and Lee Cooper and Carlos Moreno and Fusheng Wang and Tahsin Kurc and Joel Saltz and Daniel Brat",

year = "2011",

doi = "10.1109/IEMBS.2011.6089903",

language = "English",

isbn = "9781424441211",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

pages = "87--90",

booktitle = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011",

note = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 ; Conference date: 30-08-2011 Through 03-09-2011",

}

Kong, J, Cooper, L, Moreno, C, Wang, F , Kurc, T , Saltz, J & Brat, D 2011, In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response. in 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011., 6089903, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 87-90, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011, Boston, MA, United States, 08/30/11. https://doi.org/10.1109/IEMBS.2011.6089903

In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response. / Kong, Jun; Cooper, Lee; Moreno, Carlos et al.
33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011. 2011. p. 87-90 6089903 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response

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AU - Kurc, Tahsin

AU - Saltz, Joel

AU - Brat, Daniel

PY - 2011

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AB - In this paper, we present a complete and novel workflow for quantitative nuclear feature analysis of glioblas-toma using high-throughput whole-slide microscopy image processing as it relates to treatment response and patient survival. With a complete suite of computer algorithms, large numbers of micro-anatomical structures, in this case nuclei, are analyzed and represented efficiently from whole-slide digitized images with numerical features. With regard to endpoints of treatment response, the computerized analysis presents a better discrimination than traditional neuropathologic review. As a result, this analysis method shows potential to facilitate a better understanding of disease progression and patients' response to therapy for glioblastoma.

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DO - 10.1109/IEMBS.2011.6089903

M3 - Conference contribution

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SN - 9781424441211

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

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T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011

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ER -

Kong J, Cooper L, Moreno C, Wang F , Kurc T , Saltz J et al. In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011. 2011. p. 87-90. 6089903. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/IEMBS.2011.6089903

In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response

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