Abstract 4061: Computer-assisted Gleason grading of prostate cancer: Two novel approaches using nuclear shape and texture feature to classify pathologic Gleason grade patterns 3 and 4

Abstract

Abstract Introduction: We used two novel computer-assisted imaging applications that incorporate nuclear shape and texture analysis to differentiate Gleason grade patterns 3 and 4 of H&Ein a tissue microarray (TMA). The two approaches utilized a variational adaptive segmentation scheme (AdACM) (Rutgers) and the cardinal multiridgelet transform (CMRT) based texture analysis (University of Pittsburgh). Methods: The TMA include 0.6 mm cores from blocks that included Gleason patterns of CaP radical prostatectomy cases from 40 patients. The TMA comprised 13 Gleason patterns 6 (3+3), 8 pattern 7 (3+4), 7 pattern 8 (4+4) and 5 pattern 9 (4+5) Gleason grade (GG) patterns. The AdACM uses 200 × 200 pixel images extracted for nuclear & architectural textural features. All features were based on mathematical energy terms with a shape prior in a multi-level set formulation. The CMRT is an image transform that is translation- and rotation-invariant, and provides a means of displaying texture contents for selection of discriminative features. With a 3-scale decomposition, it gives 8 ridgelet packets and 16 statistical texture features in a 256 x 256 pixel image patch that are averaged over 9 non-overlapping patches in a given image for separation of GG patterns 3 and 4. Results: The CMRT study utilized 3 of 16 texture features extracted from images and trained a Gaussian kernel support vector machine (SVM), a leave-one-out cross-validation showed an accuracy of 94% and AUC of 0.97. For AdACM, nuclear features and architectural features were extracted. Linear Discriminant Analysis (LDA) was employed for feature selection and best features from all three classes could discriminate Gleason grade 3 vs. 4 with an accuracy of 86% via a SVM classifier. Conclusions: Unique quantitative imaging of tissue nuclear shape and texture features can accurately differentiate Gleason patterns 3 and 4. Our research will next evaluate CaP outcomes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4061. doi:1538-7445.AM2012-4061