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There will be an anticipated 29,480 prostate cancer (PCa) deaths in 2014 in the US. Nearly 40% of PCa patients will undergo radical prostatectomy (RP), which reduces the risk of death from PCa. Roughly ∼15% of these PCa patients tend to recur and of these a portion will metastasize (∼40%). Hence, a more accurate and objective computer-assisted image analysis (CAIA) automated PCa grading system should be considered. The current standard for Gleason grading involves an expert pathologist visually scoring Gleason grade patterns based upon H&E histologic glandular architecture (size, shape and organization). Using the same tissue microarray (TMA) containing 80 radical prostatectomy (RP) cases stratified by Gleason scores, the authors selected a subset of up to fifty 20X Aperio whole slide scanned H&E images representing GG 3 and GG4 cases. Three separate approaches to measure tissue texture and nuclear structure were used: 1) adaptive active contour scheme (AdACM) segmentation which focuses on nuclear shape and texture features 2) Curvelet transform based histologic tissue texture method and 3) MediaCybernetics ImagePro 9.1 software that includes ∼22 nuclear structure features. Data analysis utilized area under the curve (AUC) for the receiver operating characteristics (ROC) with assessment of accuracy, sensitivity and specificity. Ali and Madabhushi at Case Western Reserve University utilized AdACM segmentation approach and three nuclear features to obtain an AUC = 0.88 yielding an accuracy of 86.1% with sensitivity = 85.2% and specificity = 87.1% applying a quadratic discriminant analysis (QDA) classifier to separate GG 3 from GG4. Next, the University of Pittsburgh's EEE group (Wen-Chyi Lin and C. C. Li) applied a curvelet based tissue feature extraction method and a tree-structured classifier consisting of three Gaussian-kernel support vector machines each with an embedded voting mechanism and discriminated Gleason grade (GG) 3 vs GG 4 generating a AUC = 0.98 yielding an accuracy = 95.7% with sensitivity = 94.5% and specificity = 96.9%. Finally, the Veltri laboratory differentiated GG3 and GG4 and obtained an AUC = 0.96 and yielding an accuracy = 85%, sensitivity = 82%, specificity = 90% to separate GG pattern 3 from 4. Therefore using CAIA with three different laboratories we were able to use PCa glandular histologic and cell nuclear quantitative measurements to accurately discriminate GG patterns 3 and 4. More work is required to apply these automated tools to confirm the optimal method and also to predict PCa outcomes of recurrence, metastasis and survival. Citation Format: Robert W. Veltri, Sahirzeeshan Ali, Wen-Chyi Lin, Guangjing Zhu, Jonathan I. Epstein, Ching-Chung Li, Anant Madabhushi. Cancer histologic and cell nucleus architecture differentiate prostate cancer Gleason patterns 3 from 4. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4349. doi:10.1158/1538-7445.AM2015-4349

This article presents the design of a smart pet care system based on the slow intelligence framework for providing pets with suitable living conditions that closely mirror their natural habitat. By integrating heterogeneous information from various sensing data, the smart environment-aware pet care system can adaptively adjust the setting of temperature and humidity that best fits for the pet through iterative slow intelligence computation. Simulations of two case studies were provided to illustrate the application of the proposed system for pets such as snakes and dogs. The simulation results demonstrate the feasibility of the proposed approach to the design of smart pet care systems.