Understanding Tumor Micro Environment Using Graph Theory

Kinza Rohail; Saba Bashir; Hazrat Ali; Tanvir Alam; Sheheryar Khan; Jia Wu; Pingjun Chen; Rizwan Qureshi

doi:10.1007/978-3-031-27066-6_7

Understanding Tumor Micro Environment Using Graph Theory

Kinza Rohail, Saba Bashir, Hazrat Ali, Tanvir Alam, Sheheryar Khan, Jia Wu, Pingjun Chen, Rizwan Qureshi

Imaging Physics

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

Abstract

Based over the historical data statistics of about past 50 years from National Cancer Institute’s Surveillance, the survival rate of patients affected with Chronic Lymphocytic Leukemia (CLL) is about 65%. Neoplastic lymphomas accelerated Chronic Lymphocytic Leukemia (aCLL) and Richter Transformation - Diffuse Large B-cell Lymphoma (RT-DLBL) are the aggressive and rare variant of this cancer that are subjected to less survival rate in patients and becomes worse with age of the patients. In this study, we developed a framework based over Graph Theory, Gaussian Mixture Modeling and Fuzzy C-mean Clustering, for learning the cell characteristics in neoplastic lymphomas along with quantitative analysis of pathological facts observed with integration of Image and Nuclei level analysis. On H &E slides of 60 hematolymphoid neoplasms, we evaluated the proposed algorithm and compared it to four cell level graph-based algorithms, including the global cell graph, cluster cell graph, hierarchical graph modeling and FLocK. The proposed method achieves better performance than the existing algorithms with mean diagnosis accuracy of 0.70833.

Original language	English (US)
Title of host publication	Computer Vision – ACCV 2022 Workshops - 16th Asian Conference on Computer Vision, Revised Selected Papers
Editors	Yinqiang Zheng, Hacer Yalim Keleş, Piotr Koniusz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	90-101
Number of pages	12
ISBN (Print)	9783031270659
DOIs	https://doi.org/10.1007/978-3-031-27066-6_7
State	Published - 2023
Event	16th Asian Conference on Computer Vision , ACCV 2022 - Macao, China Duration: Dec 4 2022 → Dec 8 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13848 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Asian Conference on Computer Vision , ACCV 2022
Country/Territory	China
City	Macao
Period	12/4/22 → 12/8/22

Keywords

Digital pathology
Fuzzy clustering
Graph theory
Hematolymphoid cancer

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-27066-6_7

Cite this

Rohail, K., Bashir, S., Ali, H., Alam, T., Khan, S., Wu, J., Chen, P., & Qureshi, R. (2023). Understanding Tumor Micro Environment Using Graph Theory. In Y. Zheng, H. Y. Keleş, & P. Koniusz (Eds.), Computer Vision – ACCV 2022 Workshops - 16th Asian Conference on Computer Vision, Revised Selected Papers (pp. 90-101). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13848 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-27066-6_7

Understanding Tumor Micro Environment Using Graph Theory. / Rohail, Kinza; Bashir, Saba; Ali, Hazrat et al.
Computer Vision – ACCV 2022 Workshops - 16th Asian Conference on Computer Vision, Revised Selected Papers. ed. / Yinqiang Zheng; Hacer Yalim Keleş; Piotr Koniusz. Springer Science and Business Media Deutschland GmbH, 2023. p. 90-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13848 LNCS).

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

Rohail, K, Bashir, S, Ali, H, Alam, T, Khan, S, Wu, J , Chen, P & Qureshi, R 2023, Understanding Tumor Micro Environment Using Graph Theory. in Y Zheng, HY Keleş & P Koniusz (eds), Computer Vision – ACCV 2022 Workshops - 16th Asian Conference on Computer Vision, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13848 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 90-101, 16th Asian Conference on Computer Vision , ACCV 2022, Macao, China, 12/4/22. https://doi.org/10.1007/978-3-031-27066-6_7

Rohail K, Bashir S, Ali H, Alam T, Khan S, Wu J et al. Understanding Tumor Micro Environment Using Graph Theory. In Zheng Y, Keleş HY, Koniusz P, editors, Computer Vision – ACCV 2022 Workshops - 16th Asian Conference on Computer Vision, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. p. 90-101. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-27066-6_7

Rohail, Kinza ; Bashir, Saba ; Ali, Hazrat et al. / Understanding Tumor Micro Environment Using Graph Theory. Computer Vision – ACCV 2022 Workshops - 16th Asian Conference on Computer Vision, Revised Selected Papers. editor / Yinqiang Zheng ; Hacer Yalim Keleş ; Piotr Koniusz. Springer Science and Business Media Deutschland GmbH, 2023. pp. 90-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AB - Based over the historical data statistics of about past 50 years from National Cancer Institute’s Surveillance, the survival rate of patients affected with Chronic Lymphocytic Leukemia (CLL) is about 65%. Neoplastic lymphomas accelerated Chronic Lymphocytic Leukemia (aCLL) and Richter Transformation - Diffuse Large B-cell Lymphoma (RT-DLBL) are the aggressive and rare variant of this cancer that are subjected to less survival rate in patients and becomes worse with age of the patients. In this study, we developed a framework based over Graph Theory, Gaussian Mixture Modeling and Fuzzy C-mean Clustering, for learning the cell characteristics in neoplastic lymphomas along with quantitative analysis of pathological facts observed with integration of Image and Nuclei level analysis. On H &E slides of 60 hematolymphoid neoplasms, we evaluated the proposed algorithm and compared it to four cell level graph-based algorithms, including the global cell graph, cluster cell graph, hierarchical graph modeling and FLocK. The proposed method achieves better performance than the existing algorithms with mean diagnosis accuracy of 0.70833.

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Understanding Tumor Micro Environment Using Graph Theory

Abstract

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Keywords

ASJC Scopus subject areas

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