Cell orientation entropy (COrE): predicting biochemical recurrence from prostate cancer tissue microarrays.

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

Research output: Chapter in Book/Report/Conference proceedingChapter

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 publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages396-403
Number of pages8
Volume16
EditionPt 3
StatePublished - 2013

Fingerprint

Entropy
Prostatic Neoplasms
Recurrence
Prostatectomy
Neoplasms
Pathology

ASJC Scopus subject areas

  • Medicine(all)

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 Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 16, pp. 396-403)

Cell orientation entropy (COrE) : predicting biochemical recurrence from prostate cancer tissue microarrays. / Lee, George; Ali, Sahirzeeshan; Veltri, Robert; Epstein, Jonathan Ira; Christudass, Christhunesa; Madabhushi, Anant.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 3. ed. 2013. p. 396-403.

Research output: Chapter in Book/Report/Conference proceedingChapter

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 Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 edn, vol. 16, pp. 396-403.
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 Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 ed. Vol. 16. 2013. p. 396-403
Lee, George ; Ali, Sahirzeeshan ; Veltri, Robert ; Epstein, Jonathan Ira ; Christudass, Christhunesa ; Madabhushi, Anant. / Cell orientation entropy (COrE) : predicting biochemical recurrence from prostate cancer tissue microarrays. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 3. ed. 2013. pp. 396-403
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