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^*

^*Corresponding author for this work

CSE Information & Computing Technology

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

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
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
Publication status	Published - 2023
Event	16th Asian Conference on Computer Vision , ACCV 2022 - Macao, China Duration: 4 Dec 2022 → 8 Dec 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	4/12/22 → 8/12/22

Keywords

Digital pathology
Fuzzy clustering
Graph theory
Hematolymphoid cancer

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

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)).

@inproceedings{9fbaf184959e490c9181e2c0f80ee999,

title = "Understanding Tumor Micro Environment Using Graph Theory",

abstract = "Based over the historical data statistics of about past 50 years from National Cancer Institute{\textquoteright}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.",

keywords = "Digital pathology, Fuzzy clustering, Graph theory, Hematolymphoid cancer",

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

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 16th Asian Conference on Computer Vision , ACCV 2022 ; Conference date: 04-12-2022 Through 08-12-2022",

year = "2023",

doi = "10.1007/978-3-031-27066-6_7",

language = "English",

isbn = "9783031270659",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "90--101",

editor = "Yinqiang Zheng and Kele{\c s}, {Hacer Yalim} and Piotr Koniusz",

booktitle = "Computer Vision – ACCV 2022 Workshops - 16th Asian Conference on Computer Vision, Revised Selected Papers",

address = "Germany",

}

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, 4/12/22. 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 › peer-review

TY - GEN

T1 - Understanding Tumor Micro Environment Using Graph Theory

AU - Rohail, Kinza

AU - Bashir, Saba

AU - Ali, Hazrat

AU - Alam, Tanvir

AU - Khan, Sheheryar

AU - Wu, Jia

AU - Chen, Pingjun

AU - Qureshi, Rizwan

PY - 2023

Y1 - 2023

N2 - 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.

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.

KW - Digital pathology

KW - Fuzzy clustering

KW - Graph theory

KW - Hematolymphoid cancer

UR - http://www.scopus.com/inward/record.url?scp=85151060281&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-27066-6_7

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

M3 - Conference contribution

AN - SCOPUS:85151060281

SN - 9783031270659

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 90

EP - 101

BT - Computer Vision – ACCV 2022 Workshops - 16th Asian Conference on Computer Vision, Revised Selected Papers

A2 - Zheng, Yinqiang

A2 - Keleş, Hacer Yalim

A2 - Koniusz, Piotr

PB - Springer Science and Business Media Deutschland GmbH

T2 - 16th Asian Conference on Computer Vision , ACCV 2022

Y2 - 4 December 2022 through 8 December 2022

ER -

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

Understanding Tumor Micro Environment Using Graph Theory

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this