Cell Orientation Entropy (COrE): Predicting biochemical recurrence from prostate cancer tissue microarrays

George Lee, Sahirzeeshan Ali, Robert Veltri, Jonathan I. Epstein, Christhunesa Christudass, Anant Madabhushi

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

Abstract

We introduce a novel feature descriptor to describe cancer cells called Cell Orientation Entropy (COrE). The main objective of this work is to employ COrE to quantitatively model disorder of cell/nuclear orientation within local neighborhoods and evaluate whether these measurements of directional disorder are correlated with biochemical recurrence (BCR) in prostate cancer (CaP) patients. COrE has a number of novel attributes that are unique to digital pathology image analysis. Firstly, it is the first rigorous attempt to quantitatively model cell/nuclear orientation. Secondly, it provides for modeling of local cell networks via construction of subgraphs. Thirdly, it allows for quantifying the disorder in local cell orientation via second order statistical features. We evaluated the ability of 39 COrE features to capture the characteristics of cell orientation in CaP tissue microarray (TMA) images in order to predict 10 year BCR in men with CaP following radical prostatectomy. Randomized 3-fold cross-validation via a random forest classifier evaluated on a combination of COrE and other nuclear features achieved an accuracy of 82.7 ± 3.1% on a dataset of 19 BCR and 20 non-recurrence patients. Our results suggest that COrE features could be extended to characterize disease states in other histological cancer images in addition to prostate cancer.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages396-403
Number of pages8
Volume8151 LNCS
EditionPART 3
DOIs
StatePublished - 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 26 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8151 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/26/13

Fingerprint

Prostate Cancer
Microarrays
Microarray
Recurrence
Entropy
Tissue
Cell
Disorder
Pathology
Image analysis
Cancer
Classifiers
Cells
Random Forest
Digital Image
Image Analysis
Cross-validation
Descriptors
Subgraph
Fold

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Lee, G., Ali, S., Veltri, R., Epstein, J. I., Christudass, C., & Madabhushi, A. (2013). Cell Orientation Entropy (COrE): Predicting biochemical recurrence from prostate cancer tissue microarrays. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 8151 LNCS, pp. 396-403). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-40760-4_50

Cell Orientation Entropy (COrE) : Predicting biochemical recurrence from prostate cancer tissue microarrays. / Lee, George; Ali, Sahirzeeshan; Veltri, Robert; Epstein, Jonathan I.; Christudass, Christhunesa; Madabhushi, Anant.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8151 LNCS PART 3. ed. 2013. p. 396-403 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3).

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

Lee, G, Ali, S, Veltri, R, Epstein, JI, Christudass, C & Madabhushi, A 2013, Cell Orientation Entropy (COrE): Predicting biochemical recurrence from prostate cancer tissue microarrays. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 8151 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8151 LNCS, pp. 396-403, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 9/22/13. https://doi.org/10.1007/978-3-642-40760-4_50
Lee G, Ali S, Veltri R, Epstein JI, Christudass C, Madabhushi A. Cell Orientation Entropy (COrE): Predicting biochemical recurrence from prostate cancer tissue microarrays. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 8151 LNCS. 2013. p. 396-403. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-40760-4_50
Lee, George ; Ali, Sahirzeeshan ; Veltri, Robert ; Epstein, Jonathan I. ; Christudass, Christhunesa ; Madabhushi, Anant. / Cell Orientation Entropy (COrE) : Predicting biochemical recurrence from prostate cancer tissue microarrays. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8151 LNCS PART 3. ed. 2013. pp. 396-403 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
@inproceedings{ff705f5e6fcd475d839fba9f8e0ac1a7,
title = "Cell Orientation Entropy (COrE): Predicting biochemical recurrence from prostate cancer tissue microarrays",
abstract = "We introduce a novel feature descriptor to describe cancer cells called Cell Orientation Entropy (COrE). The main objective of this work is to employ COrE to quantitatively model disorder of cell/nuclear orientation within local neighborhoods and evaluate whether these measurements of directional disorder are correlated with biochemical recurrence (BCR) in prostate cancer (CaP) patients. COrE has a number of novel attributes that are unique to digital pathology image analysis. Firstly, it is the first rigorous attempt to quantitatively model cell/nuclear orientation. Secondly, it provides for modeling of local cell networks via construction of subgraphs. Thirdly, it allows for quantifying the disorder in local cell orientation via second order statistical features. We evaluated the ability of 39 COrE features to capture the characteristics of cell orientation in CaP tissue microarray (TMA) images in order to predict 10 year BCR in men with CaP following radical prostatectomy. Randomized 3-fold cross-validation via a random forest classifier evaluated on a combination of COrE and other nuclear features achieved an accuracy of 82.7 ± 3.1{\%} on a dataset of 19 BCR and 20 non-recurrence patients. Our results suggest that COrE features could be extended to characterize disease states in other histological cancer images in addition to prostate cancer.",
author = "George Lee and Sahirzeeshan Ali and Robert Veltri and Epstein, {Jonathan I.} and Christhunesa Christudass and Anant Madabhushi",
year = "2013",
doi = "10.1007/978-3-642-40760-4_50",
language = "English (US)",
isbn = "9783642407598",
volume = "8151 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 3",
pages = "396--403",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 3",

}

TY - GEN

T1 - Cell Orientation Entropy (COrE)

T2 - Predicting biochemical recurrence from prostate cancer tissue microarrays

AU - Lee, George

AU - Ali, Sahirzeeshan

AU - Veltri, Robert

AU - Epstein, Jonathan I.

AU - Christudass, Christhunesa

AU - Madabhushi, Anant

PY - 2013

Y1 - 2013

N2 - We introduce a novel feature descriptor to describe cancer cells called Cell Orientation Entropy (COrE). The main objective of this work is to employ COrE to quantitatively model disorder of cell/nuclear orientation within local neighborhoods and evaluate whether these measurements of directional disorder are correlated with biochemical recurrence (BCR) in prostate cancer (CaP) patients. COrE has a number of novel attributes that are unique to digital pathology image analysis. Firstly, it is the first rigorous attempt to quantitatively model cell/nuclear orientation. Secondly, it provides for modeling of local cell networks via construction of subgraphs. Thirdly, it allows for quantifying the disorder in local cell orientation via second order statistical features. We evaluated the ability of 39 COrE features to capture the characteristics of cell orientation in CaP tissue microarray (TMA) images in order to predict 10 year BCR in men with CaP following radical prostatectomy. Randomized 3-fold cross-validation via a random forest classifier evaluated on a combination of COrE and other nuclear features achieved an accuracy of 82.7 ± 3.1% on a dataset of 19 BCR and 20 non-recurrence patients. Our results suggest that COrE features could be extended to characterize disease states in other histological cancer images in addition to prostate cancer.

AB - We introduce a novel feature descriptor to describe cancer cells called Cell Orientation Entropy (COrE). The main objective of this work is to employ COrE to quantitatively model disorder of cell/nuclear orientation within local neighborhoods and evaluate whether these measurements of directional disorder are correlated with biochemical recurrence (BCR) in prostate cancer (CaP) patients. COrE has a number of novel attributes that are unique to digital pathology image analysis. Firstly, it is the first rigorous attempt to quantitatively model cell/nuclear orientation. Secondly, it provides for modeling of local cell networks via construction of subgraphs. Thirdly, it allows for quantifying the disorder in local cell orientation via second order statistical features. We evaluated the ability of 39 COrE features to capture the characteristics of cell orientation in CaP tissue microarray (TMA) images in order to predict 10 year BCR in men with CaP following radical prostatectomy. Randomized 3-fold cross-validation via a random forest classifier evaluated on a combination of COrE and other nuclear features achieved an accuracy of 82.7 ± 3.1% on a dataset of 19 BCR and 20 non-recurrence patients. Our results suggest that COrE features could be extended to characterize disease states in other histological cancer images in addition to prostate cancer.

UR - http://www.scopus.com/inward/record.url?scp=84885907155&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885907155&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-40760-4_50

DO - 10.1007/978-3-642-40760-4_50

M3 - Conference contribution

SN - 9783642407598

VL - 8151 LNCS

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

SP - 396

EP - 403

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

ER -