Abstract 425: Nuclear morphometry predicts prostate cancer progression
Authors: Zhu Guangjing , Gajdhar Aniq ur rehman , Epstein Jonathan I. , Carleton Neil , Davis Christine , Kagohara Luciane Tsukamoto , Veltri Robert W. 
Published: July 2016

Abstract
Introduction:
The progression of prostate cancer (PCa) involves tissue morphometric changes, which laid the base for current pathological diagnosis of prostate cancer with the Gleason Scoring system. Nuclear morphometry (NM) changes contribute significantly to these tissue morphometric changes, but accurate and autonomous quantification of NM changes are challenging. Here we created a macro to quantify the changes of size, shape, DNA content, etc., and used these parameters to predict PCa progression in 80 men that underwent radical prostatectomy (RP).
Materials & Methods:
Two tissue microarrays (TMAs) with 80 RP PCa cases, stratified by Gleason scores (GS) were used for this study. The two continuous sections of TMAs were stained with H&E and Feulgen reagents and the H&E slides were used for pathological diagnosis of PCa. Both kinds of slides were scanned with Aperio scanner and image of each core were separated using Aperio ImageScope software. The H&E or Feulgen stained nuclei of cancer core were quantified using Smart Segmentation of ImagePro® Premier 9.1 software. For each core, data of all ROIs were pooled and the covariance of each parameter were generated. Data were first analyzed alone and then in combination using multivariate logistic regression (MLR) to predict the aggressive RP cases or biochemical recurrence (BCR). Decision curve analysis (DCA) was used for the MLR models to evaluate their power and effectiveness for clinical decision making. For all analysis, a p&lt;0.05 is considered as statistically significant.
Results:
Using multivariate logistic regression (MLR), to differentiate aggressive PCa (Gleason score 4+3 & &gt; = 8) from less aggressive PCa (Gleason score 3+3 & 3+4) on the TMAs of RP cases, our Feulgen NM model generated a receiver operating characteristic curve-area under the curve (ROC-AUC) of 0.90 with a sensitivity of 75.51% and specificity of 86.21% while our H&E NM model yield an ROC-AUC of 0.96 with a sensitivity of 82.00% and specificity of 90.00%. DCA analysis both showed better decision using both models compared with considering the patients as all or none of them as aggressive. Both models also show strength in differentiating two Gleason score 7 (3+4 vs 4+3). Feulgen NM model showed moderate power in the prediction of BCR based on the MLR (ROC-AUC = 0.79) and DCA analysis, while H&E MLR model performed better with a ROC-AUC of 0.86. Further Kaplan-Meier analysis of BCR with GS and NM (H&E and Feulgen) showed that the combination of GS with either Feulgen or H&E NM showed significant power in the differentiation patients with BCR survival time (HR = 3.14 & 3.89, respectively).
Conclusions:
Our accurate quantification of tissue morphometry demonstrated its translational clinical relevance since it can predict PCa aggressiveness in men that have undergone RP. Future application of this tool in active surveillance biopsies may provide an early diagnosis and prognosis prediction of PCa patients effectively.
Citation Format: Guangjing Zhu, Aniq ur rehman Gajdhar, Jonathan I. Epstein, Neil Carleton, Christine Davis, Luciane Tsukamoto Kagohara, Robert W. Veltri. Nuclear morphometry predicts prostate cancer progression. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 425.