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
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
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 - Gleason grading
KW - Prostate cancer
KW - SVM classification
KW - gland morphology
KW - image analysis
KW - tissue structures
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U2 - 10.1109/ICSMC.2012.6378181
DO - 10.1109/ICSMC.2012.6378181
M3 - Conference contribution
AN - SCOPUS:84872415640
SN - 9781467317146
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2849
EP - 2854
BT - Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
T2 - 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Y2 - 14 October 2012 through 17 October 2012
ER -