Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading

Clara Mosquera Lopez, Sos Agaian, Isaac Sanchez, Ali Almuntashri, Osman Zinalabdin, Amar Al Rikabi, Ian Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Prostate cancer automatic grading has attracted a lot of attention during the last years [1]. Many research efforts have been fixated on the development of computerized recognition and classification systems to automatically grade Gleason patterns. Automatic computerized Gleason grading methods can be classified into two basic classes: image textural-based class and tissue structural-based (nuclear architecture, gland morphology) class. To the best of our knowledge, tissue structural classification based on three-class classification results including Gleason grade 3, 4 and 5 carcinoma were not reported. The goal of this article is to: (1) develop computerized assessment support systems to automatically grade Gleason patterns 3, 4 and 5 by integrating gland morphology and architectural features; (2) improve classification accuracy especially between intermediate Gleason grades 3 and 4. Computer simulations show an average correct classification accuracy of 97.63%, 96.57% and 87.30% when distinguishing Gleason 3 vs. Gleason 4, Gleason 3 vs. Gleason 5, and Gleason 4 vs. Gleason 5 respectively. These results lead the way towards providing an effective and promising software tool in automatic prostate cancer histological Gleason grading.

Original languageEnglish (US)
Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages2849-2854
Number of pages6
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: Oct 14 2012Oct 17 2012

Other

Other2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
CountryKorea, Republic of
CitySeoul
Period10/14/1210/17/12

Fingerprint

Tissue
Computer simulation

Keywords

  • gland morphology
  • Gleason grading
  • image analysis
  • Prostate cancer
  • SVM classification
  • tissue structures

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Cite this

Lopez, C. M., Agaian, S., Sanchez, I., Almuntashri, A., Zinalabdin, O., Rikabi, A. A., & Thompson, I. (2012). Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 2849-2854). [6378181] https://doi.org/10.1109/ICSMC.2012.6378181

Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading. / Lopez, Clara Mosquera; Agaian, Sos; Sanchez, Isaac; Almuntashri, Ali; Zinalabdin, Osman; Rikabi, Amar Al; Thompson, Ian.

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2012. p. 2849-2854 6378181.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lopez, CM, Agaian, S, Sanchez, I, Almuntashri, A, Zinalabdin, O, Rikabi, AA & Thompson, I 2012, Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading. in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics., 6378181, pp. 2849-2854, 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012, Seoul, Korea, Republic of, 10/14/12. https://doi.org/10.1109/ICSMC.2012.6378181
Lopez CM, Agaian S, Sanchez I, Almuntashri A, Zinalabdin O, Rikabi AA et al. Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2012. p. 2849-2854. 6378181 https://doi.org/10.1109/ICSMC.2012.6378181
Lopez, Clara Mosquera ; Agaian, Sos ; Sanchez, Isaac ; Almuntashri, Ali ; Zinalabdin, Osman ; Rikabi, Amar Al ; Thompson, Ian. / Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2012. pp. 2849-2854
@inproceedings{6fb83a5b109a4cb186b039a752ed2cb6,
title = "Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading",
abstract = "Prostate cancer automatic grading has attracted a lot of attention during the last years [1]. Many research efforts have been fixated on the development of computerized recognition and classification systems to automatically grade Gleason patterns. Automatic computerized Gleason grading methods can be classified into two basic classes: image textural-based class and tissue structural-based (nuclear architecture, gland morphology) class. To the best of our knowledge, tissue structural classification based on three-class classification results including Gleason grade 3, 4 and 5 carcinoma were not reported. The goal of this article is to: (1) develop computerized assessment support systems to automatically grade Gleason patterns 3, 4 and 5 by integrating gland morphology and architectural features; (2) improve classification accuracy especially between intermediate Gleason grades 3 and 4. Computer simulations show an average correct classification accuracy of 97.63{\%}, 96.57{\%} and 87.30{\%} when distinguishing Gleason 3 vs. Gleason 4, Gleason 3 vs. Gleason 5, and Gleason 4 vs. Gleason 5 respectively. These results lead the way towards providing an effective and promising software tool in automatic prostate cancer histological Gleason grading.",
keywords = "gland morphology, Gleason grading, image analysis, Prostate cancer, SVM classification, tissue structures",
author = "Lopez, {Clara Mosquera} and Sos Agaian and Isaac Sanchez and Ali Almuntashri and Osman Zinalabdin and Rikabi, {Amar Al} and Ian Thompson",
year = "2012",
doi = "10.1109/ICSMC.2012.6378181",
language = "English (US)",
isbn = "9781467317146",
pages = "2849--2854",
booktitle = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

}

TY - GEN

T1 - Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading

AU - Lopez, Clara Mosquera

AU - Agaian, Sos

AU - Sanchez, Isaac

AU - Almuntashri, Ali

AU - Zinalabdin, Osman

AU - Rikabi, Amar Al

AU - Thompson, Ian

PY - 2012

Y1 - 2012

N2 - Prostate cancer automatic grading has attracted a lot of attention during the last years [1]. Many research efforts have been fixated on the development of computerized recognition and classification systems to automatically grade Gleason patterns. Automatic computerized Gleason grading methods can be classified into two basic classes: image textural-based class and tissue structural-based (nuclear architecture, gland morphology) class. To the best of our knowledge, tissue structural classification based on three-class classification results including Gleason grade 3, 4 and 5 carcinoma were not reported. The goal of this article is to: (1) develop computerized assessment support systems to automatically grade Gleason patterns 3, 4 and 5 by integrating gland morphology and architectural features; (2) improve classification accuracy especially between intermediate Gleason grades 3 and 4. Computer simulations show an average correct classification accuracy of 97.63%, 96.57% and 87.30% when distinguishing Gleason 3 vs. Gleason 4, Gleason 3 vs. Gleason 5, and Gleason 4 vs. Gleason 5 respectively. These results lead the way towards providing an effective and promising software tool in automatic prostate cancer histological Gleason grading.

AB - Prostate cancer automatic grading has attracted a lot of attention during the last years [1]. Many research efforts have been fixated on the development of computerized recognition and classification systems to automatically grade Gleason patterns. Automatic computerized Gleason grading methods can be classified into two basic classes: image textural-based class and tissue structural-based (nuclear architecture, gland morphology) class. To the best of our knowledge, tissue structural classification based on three-class classification results including Gleason grade 3, 4 and 5 carcinoma were not reported. The goal of this article is to: (1) develop computerized assessment support systems to automatically grade Gleason patterns 3, 4 and 5 by integrating gland morphology and architectural features; (2) improve classification accuracy especially between intermediate Gleason grades 3 and 4. Computer simulations show an average correct classification accuracy of 97.63%, 96.57% and 87.30% when distinguishing Gleason 3 vs. Gleason 4, Gleason 3 vs. Gleason 5, and Gleason 4 vs. Gleason 5 respectively. These results lead the way towards providing an effective and promising software tool in automatic prostate cancer histological Gleason grading.

KW - gland morphology

KW - Gleason grading

KW - image analysis

KW - Prostate cancer

KW - SVM classification

KW - tissue structures

UR - http://www.scopus.com/inward/record.url?scp=84872415640&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872415640&partnerID=8YFLogxK

U2 - 10.1109/ICSMC.2012.6378181

DO - 10.1109/ICSMC.2012.6378181

M3 - Conference contribution

SN - 9781467317146

SP - 2849

EP - 2854

BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

ER -