Computer-assisted quantification of CD3+ T cells in follicular lymphoma.

The advance of high resolution digital scans of pathology slides allowed development of computer based image analysis algorithms that may help pathologists in IHC stains quantification. While very promising, these methods require further refinement before they are implemented in routine clinical setting. Particularly critical is to evaluate algorithm performance in a setting similar to current clinical practice. In this article, we present a pilot study that evaluates the use of a computerized cell quantification method in the clinical estimation of CD3 positive (CD3+) T cells in follicular lymphoma (FL). Our goal is to demonstrate the degree to which computerized quantification is comparable to the practice of estimation by a panel of expert pathologists. The computerized quantification method uses entropy based histogram thresholding to separate brown (CD3+) and blue (CD3-) regions after a color space transformation. A panel of four board-certified hematopathologists evaluated a database of 20 FL images using two different reading methods: visual estimation and manual marking of each CD3+ cell in the images. These image data and the readings provided a reference standard and the range of variability among readers. Sensitivity and specificity measures of the computer's segmentation of CD3+ and CD- T cell are recorded. For all four pathologists, mean sensitivity and specificity measures are 90.97 and 88.38%, respectively. The computerized quantification method agrees more with the manual cell marking as compared to the visual estimations. Statistical comparison between the computerized quantification method and the pathologist readings demonstrated good agreement with correlation coefficient values of 0.81 and 0.96 in terms of Lin's concordance correlation and Spearman's correlation coefficient, respectively. These values are higher than most of those calculated among the pathologists. In the future, the computerized quantification method may be used to investigate the relationship between the overall architectural pattern (i.e., interfollicular vs. follicular) and outcome measures (e.g., overall survival, and time to treatment). © 2017 International Society for Advancement of Cytometry.

Robust video content analysis schemes for human action recognition.

INTRODUCTION: Action recognition is a challenging time series classification task that has received much attention in the recent past due to its importance in critical applications, such as surveillance, visual behavior study, topic discovery, security, and content retrieval. OBJECTIVES: The main objective of the research is to develop a robust and high-performance human action recognition techniques. A combination of local and holistic feature extraction methods used through analyzing the most effective features to extract to reach the objective, followed by using simple and high-performance machine learning algorithms. METHODS: This paper presents three robust action recognition techniques based on a series of image analysis methods to detect activities in different scenes. The general scheme architecture consists of shot boundary detection, shot frame rate re-sampling, and compact feature vector extraction. This process is achieved by emphasizing variations and extracting strong patterns in feature vectors before classification. RESULTS: The proposed schemes are tested on datasets with cluttered backgrounds, low- or high-resolution videos, different viewpoints, and different camera motion conditions, namely, the Hollywood-2, KTH, UCF11 (YouTube actions), and Weizmann datasets. The proposed schemes resulted in highly accurate video analysis results compared to those of other works based on four widely used datasets. The First, Second, and Third Schemes provides recognition accuracies of 57.8%, 73.6%, and 52.0% on Hollywood2, 94.5%, 97.0%, and 59.3% on KTH, 94.5%, 95.6%, and 94.2% on UCF11, and 98.9%, 97.8% and 100% on Weizmann. CONCLUSION: Each of the proposed schemes provides high recognition accuracy compared to other state-of-art methods. Especially, the Second Scheme as it gives excellent comparable results to other benchmarked approaches.

Cell Classification in ER-Stained Whole Slide Breast Cancer Images Using Convolutional Neural Network.

Hormone receptor status in breast carcinoma is determined primarily to identify patients who may benefit from hormonal therapy. Estrogen receptor (ER) is one of the hormone receptor positive factors which have been recognized as a marker for which women with breast cancer would respond to hormone treatment. We propose a system to classify cells in ER-stained whole slide breast carcinoma images according to their staining strength using convolutional neural network (CNN). The proposed CNN multiclass classifier was tested on a region of 1200 cells, and achieved very promising results, with overall accuracy of 88.8% and AUC score of 97.5%. The proposed system is useful for use in hormone receptor testing, where the outcomes are used to decide whether the cancer is likely to respond to hormonal therapy or other treatments.

Efficacy of Onalespib, a Long-Acting Second-Generation HSP90 Inhibitor, as a Single Agent and in Combination with Temozolomide against Malignant Gliomas.

Purpose: HSP90, a highly conserved molecular chaperone that regulates the function of several oncogenic client proteins, is altered in glioblastoma. However, HSP90 inhibitors currently in clinical trials are short-acting, have unacceptable toxicities, or are unable to cross the blood-brain barrier (BBB). We examined the efficacy of onalespib, a potent, long-acting novel HSP90 inhibitor as a single agent and in combination with temozolomide (TMZ) against gliomas in vitro and in vivoExperimental Design: The effect of onalespib on HSP90, its client proteins, and on the biology of glioma cell lines and patient-derived glioma-initiating cells (GSC) was determined. Brain and plasma pharmacokinetics of onalespib and its ability to inhibit HSP90 in vivo were assessed in non-tumor-bearing mice. Its efficacy as a single agent or in combination with TMZ was assessed in vitro and in vivo using zebrafish and patient-derived GSC xenograft mouse glioma models.Results: Onalespib-mediated HSP90 inhibition depleted several survival-promoting client proteins such as EGFR, EGFRvIII, and AKT, disrupted their downstream signaling, and decreased the proliferation, migration, angiogenesis, and survival of glioma cell lines and GSCs. Onalespib effectively crossed the BBB to inhibit HSP90 in vivo and extended survival as a single agent in zebrafish xenografts and in combination with TMZ in both zebrafish and GSC mouse xenografts.Conclusions: Our results demonstrate the long-acting effects of onalespib against gliomas in vitro and in vivo, which combined with its ability to cross the BBB support its development as a potential therapeutic agent in combination with TMZ against gliomas. Clin Cancer Res; 23(20); 6215-26. ©2017 AACR.