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With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data. However, the scarcity of available large-scale labeled US data has hindered the success of CNNs for classifying liver disease from US data. In this work, we propose a novel generative adversarial network (GAN) architecture for realistic diseased and healthy liver US image synthesis. We adopt the concept of stacking to synthesize realistic liver US data. Quantitative and qualitative evaluation is performed on 550 in-vivo B-mode liver US images collected from 55 subjects. We also show that the synthesized images, together with real in vivo data, can be used to significantly improve the performance of traditional CNN architectures for Nonalcoholic fatty liver disease (NAFLD) classification.

Computed tomography (CT) and chest X-ray (CXR) have been the two dominant imaging modalities deployed for improved management of Coronavirus disease 2019 (COVID-19). Due to faster imaging, less radiation exposure, and being cost-effective CXR is preferred over CT. However, the interpretation of CXR images, compared to CT, is more challenging due to low image resolution and COVID-19 image features being similar to regular pneumonia. Computer-aided diagnosis via deep learning has been investigated to help mitigate these problems and help clinicians during the decision-making process. The requirement for a large amount of labeled data is one of the major problems of deep learning methods when deployed in the medical domain. To provide a solution to this, in this work, we propose a semi-supervised learning (SSL) approach using minimal data for training. We integrate local-phase CXR image features into a multi-feature convolutional neural network architecture where the training of SSL method is obtained with a teacher/student paradigm. Quantitative evaluation is performed on 8,851 normal (healthy), 6,045 pneumonia, and 3,795 COVID-19 CXR scans. By only using 7.06% labeled and 16.48% unlabeled data for training, 5.53% for validation, our method achieves 93.61% mean accuracy on a large-scale (70.93%) test data. We provide comparison results against fully supervised and SSL methods. The code and dataset will be made available after acceptance.

This paper presents a novel hybrid approach for extracting cancer biomarkers from pathology reports and a prototype system, CBEx (Cancer Biomarker Extractor). It integrates a Long Short-Time Memory Neural Network that identifies biomarker-containing sentences and a dictionary-based method to extract biomarker mentions from those sentences. Preliminary results show that CBEx outperforms existing methods.

Background The molecular mechanisms underlying the association between increased adiposity and aggressive breast cancer phenotypes remain unclear, but likely involve the adipokines, leptin (LEP) and adiponectin (ADIPOQ), and their receptors (LEPR, ADIPOR1, ADIPOR2). Methods We used immunohistochemistry (IHC) to assess LEP, LEPR, ADIPOQ, ADIPOR1, and ADIPOR2 expression in breast tumor tissue microarrays among a sample of 720 women recently diagnosed with breast cancer (540 of whom self-identified as Black). We scored IHC expression quantitatively, using digital pathology analysis. We abstracted data on tumor grade, tumor size, tumor stage, lymph node status, Ki67, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) from pathology records, and used ER, PR, and HER2 expression data to classify breast cancer subtype. We used multivariable mixed effects models to estimate associations of IHC expression with tumor clinicopathology, in the overall sample and separately among Blacks. Results Larger proportions of Black than White women were overweight or obese and had more aggressive tumor features. Older age, Black race, postmenopausal status, and higher body mass index were associated with higher LEPR IHC expression. In multivariable models, lower LEPR IHC expression was associated with ER-negative status and triple-negative subtype (P Conclusions Lower LEPR IHC expression within the breast tumor microenvironment might contribute mechanistically to inter-individual variation in aggressive breast cancer clinicopathology, particularly ER-negative status and triple-negative subtype.

Summarization: This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine. Presented on: IEEE Journal of Biomedical and Health Informatics

Goal: This research aims to validate a new biomarker of breast cancer by introducing electromechanical coupling factor of breast tissue samples as a possible additional indicator of breast cancer. Since collagen fibril exhibits a structural organization that gives rise to a piezoelectric effect, the difference in collagen density between normal and cancerous tissue can be captured by identifying the corresponding electromechanical coupling factor. Methods: The design of a portable diagnostic tool and a microelectromechanical systems (MEMS)-based biochip, which is integrated with a piezoresistive sensing layer for measuring the reaction force as well as a microheater for temperature control, is introduced. To verify that electromechanical coupling factor can be used as a biomarker for breast cancer, the piezoelectric model for breast tissue is described with preliminary experimental results on five sets of normal and invasive ductal carcinoma (IDC) samples in the 25–45 $^{\circ }{\text C}$ temperature range. Conclusion: While the stiffness of breast tissues can be captured as a representative mechanical signature which allows one to discriminate among tissue types especially in the higher strain region, the electromechanical coupling factor shows more distinct differences between the normal and IDC groups over the entire strain region than the mechanical signature. From the two-sample $t$ -test, the electromechanical coupling factor under compression shows statistically significant differences ( $p\leq$ 0.0039) between the two groups. Significance: The increase in collagen density in breast tissue is an objective and reproducible characteristic of breast cancer. Although characterization of mechanical tissue property has been shown to be useful for differentiating cancerous tissue from normal tissue, using a single parameter may not be sufficient for practical usage due to inherent variation among biological samples. The portable breast cancer diagnostic tool reported in this manuscript shows the feasibility of measuring multiple parameters of breast tissue allowing for practical application.

Recent advances in digital pathology technology have led to significant improvements in terms of both the quality and resolution of the resulting images which now often exceed several Gigabytes each. Today, several leading institutions across the country utilize whole-slide imaging (WSI) as part of their routine workflow. WSI’s have utility in a wide range of diagnostic and investigative pathology applications. The fact that, these images are both large in size (about 30GB when uncompressed), and are generated in non-standard proprietary formats has limited wider adoption of these technologies and makes the task of accessing, processing and analyzing them in high-throughput fashion extremely challenging. The common approach for such data analytics applications is to pre-process the large, whole-slide images into smaller size files and store them in a generic format. However this approach limits the advantages that might be realized if different scalability levels and data unit sizes could be dynamically changed based on the specifications of the task at hand and the architectural limits of the infrastructure (e.g. node memory size). Such strategies also introduce extra processing time to the workflow. To address these challenges we present, in this paper, novel scalable access methods for parallel file systems and distributed file/object storage systems. Experimental results gathered during the course of our studies show that these methods provide opportunities not realizable using traditional approaches. We demonstrate tangible, scalability and high-throughput advantages using a Lustre parallel file system and AWS S3 distributed storage system.

Summary Objectives: Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology. Methods: The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein. Results: Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician’s feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize innovation; partnership with industry to facilitate commercialization; and education in academic communities regarding the challenges involved with translation of technology from the research domain to clinical utility and the benefits of doing so. Conclusions: This article provides a survey of the role and priorities for imaging informatics to help advance quantitative imaging in the era of precision medicine. While these recommendations were drawn from oncology, they are relevant and applicable to other clinical domains where imaging aids precision medicine.

Prostate cancer is the most common non-skin related cancer affecting 1 in 7 men in the United States. Treatment of patients with prostate cancer still remains a difficult decision-making process that requires physicians to balance clinical benefits, life expectancy, comorbidities, and treatment-related side effects. Gleason score (a sum of the primary and secondary Gleason patterns) solely based on morphological prostate glandular architecture has shown as one of the best predictors of prostate cancer outcome. Significant progress has been made on molecular subtyping prostate cancer delineated through the increasing use of gene sequencing. Prostate cancer patients with Gleason score of 7 show heterogeneity in recurrence and survival outcomes. Therefore, we propose to assess the correlation between histopathology images and genomic data with disease recurrence in prostate tumors with a Gleason 7 score to identify prognostic markers. In the study, we identify image biomarkers within tissue WSIs by modeling the spatial relationship from automatically created patches as a sequence within WSI by adopting a recurrence network model, namely long short-term memory (LSTM). Our preliminary results demonstrate that integrating image biomarkers from CNN with LSTM and genomic pathway scores, is more strongly correlated with patients recurrence of disease compared to standard clinical markers and engineered image texture features. The study further demonstrates that prostate cancer patients with Gleason score of 4+3 have a higher risk of disease progression and recurrence compared to prostate cancer patients with Gleason score of 3+4.

Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain with-out requiring labeling of images at the target domain. The adaptation is achieved through adversarial training to find an invariant feature space along with the proposed Siamese architecture on the target domain to add a regularization that is appropriate for the whole-slide images. We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.

Prostate cancer is the most common nonskin-related cancer, affecting one in seven men in the United States. Gleason score, a sum of the primary and secondary Gleason patterns, is one of the best predictors of prostate cancer outcomes. Recently, significant progress has been made in molecular subtyping prostate cancer through the use of genomic sequencing. It has been established that prostate cancer patients presented with a Gleason score 7 show heterogeneity in both disease recurrence and survival. We built a unified system using publicly available whole-slide images and genomic data of histopathology specimens through deep neural networks to identify a set of computational biomarkers. Using a survival model, the experimental results on the public prostate dataset showed that the computational biomarkers extracted by our approach had hazard ratio as 5.73 and C-index as 0.74, which were higher than standard clinical prognostic factors and other engineered image texture features. Collectively, the results of this study highlight the important role of neural network analysis of prostate cancer and the potential of such approaches in other precision medicine applications.

We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. A primary contribution of our work is the development of an unsupervised detection network by using the characteristics of histopathology image patches. The pretrained nucleus detection and feature extraction modules in our CAE can be fine-tuned for supervised learning in an end-to-end fashion. We evaluate our method on four datasets and achieve state-of-the-art results. In addition, we are able to achieve comparable performance with only 5% of the fully- supervised annotation cost.

Designing the structure of neural networks is considered one of the most challenging tasks in deep learning, especially when there is few prior knowledge about the task domain. In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain. Specifically, we first use primary succession to rapidly evolve a population of poorly initialized neural network structures into a more diverse population, followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession. Extinction is applied in both stages to reduce computational cost. Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find better network structures. Experimental results show that our proposed approach can achieve similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost. For example, the network discovered by our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35]., CVPR 2019

As the volume and velocity of data generated by scientific experiments increase, the analysis of those data inevitably requires HPC resources. Successful research in a growing number of scientific fields depends on the ability to analyze data rapidly. In many situations, scientists and engineers want quasi-instant feedback, so that results from one experiment can guide selection of the next or even improve the course of a single experiment. Such real-time requirements are hard to meet on current HPC systems, which are typically batch-scheduled under policies in which an arriving job is run immediately only if enough resources are available and is otherwise queued. Real-time jobs, in order to meet their requirements, should sometimes have higher priority than batch jobs that were submitted earlier. But, accommodating more real-time jobs will negatively impact the performance of batch jobs, which may have to be preempted. The overhead involved in preempting and restarting batch jobs will, in turn, negatively impact system utilization. Here we evaluate various scheduling schemes to support real-time jobs along with the traditional batch jobs. We perform simulation studies using trace logs of Mira, the IBM BG/Q system at Argonne National Laboratory, to quantify the impact of real-time jobs on batch job performance for various percentages of real-time jobs in the workload. We present new insights gained from grouping the jobs into different categories and studying the performance of each category. Our results show that real-time jobs in all categories can achieve an average slowdown less than 1.5 and that most categories achieve an average slowdown close to 1 with at most 20% increase in average slowdown for some categories of batch jobs with 20% or fewer real-time jobs.

Atomic force microscopy (AFM) and other forms of scanning probe microscopy have been successfully used to assess biomechanical and bioelectrical characteristics of individual cells. When extending such approaches to heterogeneous tissue, there exists the added challenge of traversing the tissue while directing the probe to the exact location of the targeted biological components under study. Such maneuvers are extremely challenging owing to the relatively small field of view, limited availability of reliable visual cues, and lack of context. In this study we designed a system that leverages the visual topology of the serial tissue sections of interest to help guide robotic control of the AFM stage to provide the requisite navigational support. The process begins by mapping the whole-slide image of a stained specimen with a well-matched, consecutive section of unstained section of tissue in a piecewise fashion. The morphological characteristics and localization of any biomarkers in the stained section can be used to position the AFM probe in the unstained tissue at regions of interest where the AFM measurements are acquired. This general approach can be utilized in various forms of microscopy for navigation assistance in tissue specimens.

Breast cancer is the largest detected cancer amongst women in the US. In this work, our team reports on the development of piezoresistive microcantilevers (PMCs) to investigate their potential use in the accurate detection and characterization of benign and diseased breast tissues by performing indentations on the micro-scale tissue specimens. The PMCs used in these experiments have been fabricated using laboratory-made silicon-on-insulator (SOI) substrate, which significantly reduces the fabrication costs. The PMCs are 260 μm long, 35 μm wide and 2 μm thick with resistivity of order 1.316 X 10−3 Ω-cm obtained by using boron diffusion technique. For indenting the tissue, we utilized 8 μm thick cylindrical SU-8 tip. The PMC was calibrated against a known AFM probe. Breast tissue cores from seven different specimens were indented using PMC to identify benign and cancerous tissue cores. Furthermore, field emission scanning electron microscopy (FE-SEM) of benign and cancerous specimens showed marked differences in the tissue morphology, which further validates our observed experimental data with the PMCs. While these patient aspecific feasibility studies clearly demonstrate the ability to discriminate between benign and cancerous breast tissues, further investigation is necessary to perform automated mechano-phenotyping (classification) of breast cancer: from onset to disease progression.

Automatic image aesthetics rating has received a growing interest with the recent breakthrough in deep learning. Although many studies exist for learning a generic or universal aesthetics model, investigation of aesthetics models incorporating individual user’s preference is quite limited. We address this personalized aesthetics problem by showing that individual’s aesthetic preferences exhibit strong correlations with content and aesthetic attributes, and hence the deviation of individual’s perception from generic image aesthetics is predictable. To accommodate our study, we first collect two distinct datasets, a large image dataset from Flickr and annotated by Amazon Mechanical Turk, and a small dataset of real personal albums rated by owners. We then propose a new approach to personalized aesthetics learning that can be trained even with a small set of annotated images from a user. The approach is based on a residual-based model adaptation scheme which learns an offset to compensate for the generic aesthetics score. Finally, we introduce an active learning algorithm to optimize personalized aesthetics prediction for real-world application scenarios. Experiments demonstrate that our approach can effectively learn personalized aesthetics preferences, and outperforms existing methods on quantitative comparisons.

The mechanical properties of tissue change significantly during the progression from healthy to malignant. Quantifying the mechanical properties of breast tissue within the tumor microenvironment can help to delineate benign from cancerous stages. In this work, we study high-grade invasive ductal carcinoma in comparison with their matched tumor adjacent areas, which exhibit benign morphology. Such paired tissue cores obtained from eight patients were indented using a MEMS-based piezoresistive microcantilever, which was positioned within pre-designated epithelial and stromal areas of the specimen. Field emission scanning electron microscopy studies on breast tissue cores were performed to understand the microstructural changes from benign to malignant. The normal epithelial tissues appeared compact and organized. The appearance of cancer regions, in comparison, not only revealed increased cellularity but also showed disorganization and increased fenestration. Using this technique, reliable discrimination between epithelial and stromal regions throughout both benign and cancerous breast tissue cores was obtained. The mechanical profiling generated using this method has the potential to be an objective, reproducible, and quantitative indicator for detecting and characterizing breast cancer.

Micro-Electro-Mechanical-Systems (MEMS) are desirable for use within medical diagnostics because of their capacity to manipulate and analyze biological materials at the microscale. Biosensors can be incorporated into portable lab-on-a-chip devices to quickly and reliably perform diagnostics procedure on laboratory and clinical samples. In this paper, electrical impedance-based measurements were used to distinguish between benign and cancerous breast tissues using microchips in a real-time and label-free manner. Two different microchips having inter-digited electrodes (10 µm width with 10 µm spacing and 10 µm width with 30 µm spacing) were used for measuring the impedance of breast tissues. The system employs Agilent E4980A precision impedance analyzer. The impedance magnitude and phase were collected over a frequency range of 100 Hz to 2 MHz. The benign group and cancer group showed clearly distinguishable impedance properties. At 200 kHz, the difference in impedance of benign and cancerous breast tissue was significantly higher (3110 Ω) in the case of microchips having 10 µm spacing compared to microchip having 30 µm spacing (568 Ω).

In the present study, we investigated the role of the transcription factor RUNX2 in melanomagenesis. We demonstrated that the expression of transcriptionally active RUNX2 was increased in melanoma cell lines as compared with human melanocytes. Using a melanoma tissue microarray, we showed that RUNX2 levels were higher in melanoma cells as compared with nevic melanocytes. RUNX2 knockdown in melanoma cell lines significantly decreased Focal Adhesion Kinase expression, and inhibited their cell growth, migration and invasion ability. Finally, the pro-hormone cholecalciferol reduced RUNX2 transcriptional activity and decreased migration of melanoma cells, further suggesting a role of RUNX2 in melanoma cell migration.

Contact mode Atomic Force Microscopy (CM-AFM) is popularly used by the biophysics community to study mechanical properties of cells cultured in petri dishes, or tissue sections fixed on microscope slides. While cells are fairly easy to locate, sampling in spatially heterogeneous tissue specimens is laborious and time-consuming at higher magnifications. Furthermore, tissue registration across multiple magnifications for AFM-based experiments is a challenging problem, suggesting the need to automate the process of AFM indentation on tissue. In this work, we have developed an image-guided micropositioning system to align the AFM probe and human breast-tissue cores in an automated manner across multiple magnifications. Our setup improves efficiency of the AFM indentation experiments considerably.

To study the frequency with which targeted tumor sequencing results will lead to implemented change in care, this study assessed tumors from 100 patients for utility, feasibility, and limitations of genomic sequencing for genomically guided therapy or other clinical purpose in the setting of a multidisciplinary molecular tumor board. Comprehensive profiling led to implementable clinical action in 35% of tumors with genomic alterations., Background. The frequency with which targeted tumor sequencing results will lead to implemented change in care is unclear. Prospective assessment of the feasibility and limitations of using genomic sequencing is critically important. Methods. A prospective clinical study was conducted on 100 patients with diverse-histology, rare, or poor-prognosis cancers to evaluate the clinical actionability of a Clinical Laboratory Improvement Amendments (CLIA)-certified, comprehensive genomic profiling assay (FoundationOne), using formalin-fixed, paraffin-embedded tumors. The primary objectives were to assess utility, feasibility, and limitations of genomic sequencing for genomically guided therapy or other clinical purpose in the setting of a multidisciplinary molecular tumor board. Results. Of the tumors from the 92 patients with sufficient tissue, 88 (96%) had at least one genomic alteration (average 3.6, range 0–10). Commonly altered pathways included p53 (46%), RAS/RAF/MAPK (rat sarcoma; rapidly accelerated fibrosarcoma; mitogen-activated protein kinase) (45%), receptor tyrosine kinases/ligand (44%), PI3K/AKT/mTOR (phosphatidylinositol-4,5-bisphosphate 3-kinase; protein kinase B; mammalian target of rapamycin) (35%), transcription factors/regulators (31%), and cell cycle regulators (30%). Many low frequency but potentially actionable alterations were identified in diverse histologies. Use of comprehensive profiling led to implementable clinical action in 35% of tumors with genomic alterations, including genomically guided therapy, diagnostic modification, and trigger for germline genetic testing. Conclusion. Use of targeted next-generation sequencing in the setting of an institutional molecular tumor board led to implementable clinical action in more than one third of patients with rare and poor-prognosis cancers. Major barriers to implementation of genomically guided therapy were clinical status of the patient and drug access. Early and serial sequencing in the clinical course and expanded access to genomically guided early-phase clinical trials and targeted agents may increase actionability. Implications for Practice: Identification of key factors that facilitate use of genomic tumor testing results and implementation of genomically guided therapy may lead to enhanced benefit for patients with rare or difficult to treat cancers. Clinical use of a targeted next-generation sequencing assay in the setting of an institutional molecular tumor board led to implementable clinical action in over one third of patients with rare and poor prognosis cancers. The major barriers to implementation of genomically guided therapy were clinical status of the patient and drug access both on trial and off label. Approaches to increase actionability include early and serial sequencing in the clinical course and expanded access to genomically guided early phase clinical trials and targeted agents.

Goal: The objective of this study is to design and develop a portable tool consisting of a disposable biochip for measuring electrothermomechanical (ETM) properties of breast tissue. Methods: A biochip integrated with a microheater, force sensors, and electrical sensors is fabricated using microtechnology. The sensor covers the area of 2 mm and the biochip is 10 mm in diameter. A portable tool capable of holding tissue and biochip is fabricated using 3-D printing. Invasive ductal carcinoma and normal tissue blocks are selected from cancer tissue bank in Biospecimen Repository Service at Rutgers Cancer Institute of New Jersey. The ETM properties of the normal and cancerous breast tissues (3-mm thickness and 2-mm diameter) are measured by indenting the tissue placed on the biochip integrated inside the 3-D printed tool. Results: Integrating microengineered biochip and 3-D printing, we have developed a portable cancer diagnosis device. Using this device, we have shown a statistically significant difference between cancerous and normal breast tissues in mechanical stiffness, electrical resistivity, and thermal conductivity. Conclusion: The developed cancer diagnosis device is capable of simultaneous ETM measurements of breast tissue specimens and can be a potential candidate for delineating normal and cancerous breast tissue cores. Significance: The portable cancer diagnosis tool could potentially provide a deterministic and quantitative information about the breast tissue characteristics, as well as the onset and disease progression of the tissues. The tool can be potentially used for other tissue-related cancers.

Clinically, prostate adenocarcinoma is diagnosed by recognizing certain morphology on histology. While the Gleason grading system has been shown to be the strongest prognostic factor for men with prostrate adenocarcinoma, there is a significant intra and interobserver variability between pathologists in assigning this grading system. In this study, we present a new method for prostate gland segmentation from which we then utilize to develop a computer aided Gleason grading. The novelty of our method is a region-based nuclei segmentation to get individual gland without using lumen as prior information. Because each gland region is surrounded by nuclei, individual gland can be segmented by using the structure features and Delaunay Triangulation. The precision, recal and F1 of this approach are 0.94±0.11, 0.60±0.23 and 0.70±0.19 respectively. Our method achieves a high accuracy for prostate gland segmentation with less computation time.

Lung cancer is the leading cause of cancer death in the United States and worldwide. Most patients are diagnosed at an advanced stage, usually stage III or IV. Identification of lung cancer patients at an early stage might enable oncologists to surgically remove the tumors. Currently, low dose CT scans are used to identify the malignant nodules in high risk patients. However, screening CT scans yield a high rate of false-positive results. A prediction model was developed for improved discrimination of malignant nodules from benign nodules in patients who underwent lung screening CT. CT images and clinical outcomes of 39 patients were obtained from the National Lung Screening Trial (NLST), National Cancer Institute, National Institute of Health. Images were analyzed to extract computational features relevant to malignancy prediction. A Support Vector Machine (SVM) based model was developed to predict the malignancy of nodules. During pilot studies, our model achieved the following prediction performance: accuracy of 0.74, sensitivity of 0.85, and specificity of 0.61.

The aim of this study was to explore the use of texture features generated from liver computed tomographic (CT) datasets as potential image-based indicators of patient response to radioembolization (RE) with yttrium-90 ((90)Y) resin microspheres, an emerging locoregional therapy for advanced-stage liver cancer.Overall posttherapy survival and percent change in serologic tumor marker at 3 months posttherapy represent the primary clinical outcomes in this study. Thirty advanced-stage liver cancer cases (primary and metastatic) treated with RE over a 3-year period were included. Texture signatures for tumor regions, which were delineated to reveal boundaries with normal regions, were computed from pretreatment contrast-enhanced liver CT studies and evaluated for their ability to classify patient serologic response and survival.A series of systematic leave-one-out cross-validation studies using soft-margin support vector machine (SVM) classifiers showed hepatic tumor texton and local binary pattern (LBP) signatures both achieve high accuracy (96%) in discriminating subjects in terms of their serologic response. The image-based indicators were also accurate in classifying subjects by survival status (80% and 93% accuracy for texton and LBP signatures, respectively).Hepatic texture signatures generated from tumor regions on pretreatment triphasic CT studies were highly accurate in differentiating among subjects in terms of serologic response and survival. These image-based computational markers show promise as potential predictive tools in candidate evaluation for locoregional therapy such as RE.

CD44 is a multifunctional cell receptor that conveys a cancer phenotype, regulates macrophage inflammatory gene expression and vascular gene activation in proatherogenic environments, and is also a marker of many cancer stem cells. CD44 undergoes sequential proteolytic cleavages that produce an intracytoplasmic domain called CD44-ICD. However, the role of CD44-ICD in cell function is unknown. We take a major step toward the elucidation of the CD44-ICD function by using a CD44-ICD-specific antibody, a modification of a ChIP assay to detect small molecules, and extensive computational analysis. We show that CD44-ICD translocates into the nucleus, where it then binds to a novel DNA consensus sequence in the promoter region of the MMP-9 gene to regulate its expression. We also show that the expression of many other genes that contain this novel response element in their promoters is up- or down-regulated by CD44-ICD. Furthermore, hypoxia-inducible factor-1α (Hif1α)-responsive genes also have the CD44-ICD consensus sequence and respond to CD44-ICD induction under normoxic conditions and therefore independent of Hif1α expression. Additionally, CD44-ICD early responsive genes encode for critical enzymes in the glycolytic pathway, revealing how CD44 could be a gatekeeper of the Warburg effect (aerobic glycolysis) in cancer cells and possibly cancer stem cells. The link of CD44 to metabolism is novel and opens a new area of research not previously considered, particularly in the study of obesity and cancer. In summary, our results finally give a function to the CD44-ICD and will accelerate the study of the regulation of many CD44-dependent genes.

Automated image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of breast cancer. Automated segmentation of the cells comprising imaged tissue microarrays (TMA) is a prerequisite for any subsequent quantitative analysis. Unfortunately, crowding and overlapping of cells present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel algorithm which can reliably separate touching cells in hematoxylin stained breast TMA specimens which have been acquired using a standard RGB camera. The algorithm is composed of two steps. It begins with a fast, reliable object center localization approach which utilizes single-path voting followed by mean-shift clustering. Next, the contour of each cell is obtained using a level set algorithm based on an interactive model. We compared the experimental results with those reported in the most current literature. Finally, performance was evaluated by comparing the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm. The method was systematically tested on 234 image patches exhibiting dense overlap and containing more than 2200 cells. It was also tested on whole slide images including blood smears and tissue microarrays containing thousands of cells. Since the voting step of the seed detection algorithm is well suited for parallelization, a parallel version of the algorithm was implemented using graphic processing units (GPU) which resulted in significant speed-up over the C/C++ implementation.

SummaryBackground: Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms.Objectives: In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging.Methods: It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts’ annotation and those generated using the automatic segmentation algorithm.Results: The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation.Conclusion: The proposed segmentation algorithm can accurately detect and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation.

Background We describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands. Results The proposed tools and methods take advantage of state-of-the-art parallel machines and efficient content-based image searching strategies. The content based image retrieval (CBIR) algorithms can quickly detect and retrieve image patches similar to a query patch using a hierarchical analysis approach. The analysis component based on high performance computing can carry out consensus clustering on 500,000 data points using a large shared memory system. Conclusions Our work demonstrates efficient CBIR algorithms and high performance computing can be leveraged for efficient analysis of large microscopy images to meet the challenges of clinically salient applications in pathology. These technologies enable researchers and clinical investigators to make more effective use of the rich informational content contained within digitized microscopy specimens.

Summary Melanoma cells are resistant to transforming growth factor-β (TGFβ)-induced cell-cycle arrest. In this study, we investigated a mechanism of resistance involving a regulatory domain, called linker region, in Smad2 and Smad3, main downstream effectors of TGFβ. Melanoma cells in culture and tumor samples exhibited constitutive Smad2 and Smad3 linker phosphorylation. Treatment of melanoma cells with the MEK1/2 inhibitor, U0126, or the two pan-CDK and GSK3 inhibitors, Flavopiridol and R547, resulted in decreased linker phosphorylation of Smad2 and Smad3. Overexpression of the linker phosphorylation-resistant Smad3 EPSM mutant in melanoma cells resulted in an increase in expression of p15INK4B and p21WAF1, as compared with cells transfected with wild-type (WT) Smad3. In addition, the cell numbers of EPSM Smad3-expressing melanoma cells were significantly reduced compared with WT Smad3-expressing cells. These results suggest that the linker phosphorylation of Smad3 contributes to the resistance of melanoma cells to TGFβ-mediated growth inhibition.

BACKGROUND Autophagy is a starvation induced cellular process of self-digestion that allows cells to degrade cytoplasmic contents. The understanding of autophagy, as either a mechanism of resistance to therapies that induce metabolic stress, or as a means to cell death, is rapidly expanding and supportive of a new paradigm of therapeutic starvation. METHODS To determine the effect of therapeutic starvation in prostate cancer, we studied the effect of the prototypical inhibitor of metabolism, 2-deoxy-D-glucose (2DG), in multiple cellular models including a transfected pEGFP-LC3 autophagy reporter construct in PC-3 and LNCaP cells. RESULTS We found that 2DG induced cytotoxicity in PC-3 and LNCaP cells in a dose dependent fashion. We also found that 2DG modulated checkpoint proteins cdk4, and cdk6. Using the transfected pEGFP-LC3 autophagy reporter construct, we found that 2DG induced LC3 membrane translocation, characteristic of autophagy. Furthermore, knockdown of beclin1, an essential regulator of autophagy, abrogated 2DG induced autophagy. Using Western analysis for LC3 protein, we also found increased LC3-II expression in 2DG treated cells, again consistent with autophagy. In an effort to develop markers that may be predictive of autophagy, for assessment in clinical trials, we stained human prostate tumors for Beclin1 by immunohistochemistry (IHC). Additionally, we used a digitized imaging algorithm to quantify Beclin1 staining assessment. These data demonstrate the induction of autophagy in prostate cancer by therapeutic starvation with 2DG, and support the feasibility of assessment of markers predictive of autophagy such as Beclin1 that can be utilized in clinical trials. Prostate 68: 1743-1752 (c) 2008 Wiley-Liss, Inc. These data demonstrate the induction of autophagy in prostate cancer by therapeutic starvation with 2DG, and support the feasibility of assessment of markers predictive of autophagy such as Beclin1 that can be utilized in clinical trials.

Receptor tyrosine kinases-based autocrine loops largely contribute to activate the MAPK and PI3K/AKT pathways in melanoma. However, the molecular mechanisms involved in generating these autocrine loops are still largely unknown. In the present study, we examine the role of the transcription factor RUNX2 in the regulation of receptor tyrosine kinase (RTK) expression in melanoma. We have demonstrated that RUNX2-deficient melanoma cells display a significant decrease in three receptor tyrosine kinases, EGFR, IGF-1R and PDGFRβ. In addition, we found co-expression of RUNX2 and another RTK, AXL, in both melanoma cells and melanoma patient samples. We observed a decrease in phosphoAKT2 (S474) and phosphoAKT (T308) levels when RUNX2 knock down resulted in significant RTK down regulation. Finally, we showed a dramatic up regulation of RUNX2 expression with concomitant up-regulation of EGFR, IGF-1R and AXL in melanoma cells resistant to the BRAF V600E inhibitor PLX4720. Taken together, our results strongly suggest that RUNX2 might be a key player in RTK-based autocrine loops and a mediator of resistance to BRAF V600E inhibitors involving RTK up regulation in melanoma.

As the most common neoplasm arising from the kidney, renal cell carcinoma (RCC) continues to have a significant impact on global health. Conventional cross-sectional imaging has always served an important role in the staging of RCC. However, with recent advances in imaging techniques and postprocessing analysis, magnetic resonance imaging (MRI) now has the capability to function as a diagnostic, therapeutic, and prognostic biomarker for RCC. For this narrative literature review, a PubMed search was conducted to collect the most relevant and impactful studies from our perspectives as urologic oncologists, radiologists, and computational imaging specialists. We seek to cover advanced MR imaging and image analysis techniques that may improve the management of patients with small renal mass or metastatic renal cell carcinoma.

Breast cancer is one of the leading cancers worldwide. Precision medicine is a new trend that systematically examines molecular and functional genomic information within each patient's cancer to identify the patterns that may affect treatment decisions and potential outcomes. As a part of precision medicine, computer-aided diagnosis enables joint analysis of functional genomic information and image from pathological images. In this paper we propose an integrated framework for breast cancer staging using image-omics and functional genomic information. The entire biomedical imaging informatics framework consists of image-omics extraction, feature combination, and classification. First, a robust automatic nuclei detection and segmentation is presented to identify tumor regions, delineate nuclei boundaries and calculate a set of image-based morphological features; next, the low dimensional image-omics is obtained through principal component analysis and is concatenated with the functional genomic features identified by a linear model. A support vector machine for differentiating stage I breast cancer from other stages are learned. We experimentally demonstrate that compared with a single type of representation (image-omics), the combination of image-omics and functional genomic feature can improve the classification accuracy by 3%.

Gleason-grading of prostate cancer pathology specimens reveal the malignancy of the cancer tissues, thus provides critical guidance for prostate cancer diagnoses and treatment. Computer-aided automatic grading methods have been providing efficient and result-consistent alternative to traditional manually slide reading approach, through statistical and structural feature analysis of the digitized pathology slides. In this paper, we propose a novel automatic Gleason grading algorithm through local structure model learning and classification. We use attributed graph to represent the tissue glandular structures in histopathology images; representative sub-graphs features were learned as bags-of-words features from labeled samples of each grades. Then structural similarity between sub-graphs in the unlabeled images and the representative sub-graphs were obtained using the learned codebook. Gleason grade was given based on an overall similarity score. We validated the proposed algorithm on 300 prostate histopathology images from the TCGA dataset, and the algorithm achieved average grading accuracy of 91.25%, 76.36% and 64.75% on images with Gleason grade 3, 4 and 5 respectively.

Carcinomas are the most commonly diagnosed cancers originating in the skin, lungs, breasts, pancreas, and other organs and glands. In most of the cases, the microenvironment within the tissue changes with the progression of disease. A key challenge is to develop a device capable of providing quantitative indicators in diagnosing cancer by measuring alteration in electrical and mechanical property of the tissues from the onset of malignancy. We demonstrate micro-electro-mechanical-systems (MEMS) based flexible polymer microsensor array capable of simultaneously measuring electro-mechanical properties of the breast tissues cores (1 mm in diameter and 10 μm in thickness) from onset through progression of the cancer. The electrical and mechanical signatures obtained from the tissue cores shows the capability of the device to clearly demarcate the specific stages of cancer in epithelial and stromal regions providing quantitative indicators facilitating the diagnosis of breast cancer. The present study shows that electro-mechanical properties of the breast tissue core at the micro-level are different than those at the macro-level.

Over the past few years, tissue microarray (TMA) technology has been established as a standard method for assessing the expression of proteins or genes across large sets of tissue specimens. It is being adopted increasingly among leading research institutions around the world and utilized in cancer research in parallel with the cDNA microarray technology. This article summarizes various aspects of cancer understanding and diagnostics in which TMA has had great impact. Although tremendous advances continue to be made to facilitate imaging and archiving of TMA specimens, automatic evaluation and quantitative analysis of TMA still remains an important challenge for modern investigators.

One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L/sub 2/E robust estimation into the traditional GVF snake. The accuracy of the new model was compared with the segmentation results using a mean-shift approach, the traditional color GVF snake, and several other commonly used segmentation strategies. The unsupervised robust color snake with L/sub 2/E robust estimation was shown to provide results which were superior to the other unsupervised approaches, and was comparable with supervised segmentation, as judged by a panel of human experts.

In many subspecialties of pathology, the intrinsic complexity of rendering accurate diagnostic decisions is compounded by a lack of definitive criteria for detecting and characterizing diseases and their corresponding histological features. In some cases, there exists a striking disparity between the diagnoses rendered by recognized authorities and those provided by non-experts. We previously reported the development of an Image Guided Decision Support (IGDS) system, which was shown to reliably discriminate among malignant lymphomas and leukemia that are sometimes confused with one another during routine microscopic evaluation. As an extension of those efforts, we report here a web-based intelligent archiving subsystem that can automatically detect, image, and index new cells into distributed ground-truth databases. Systematic experiments showed that through the use of robust texture descriptors and density estimation based fusion the reliability and performance of the governing classifications of the system were improved significantly while simultaneously reducing the dimensionality of the feature space.

Energy is transferred between the flowing blood and the vessel walls during pulsatile blood flow (a normal pulse cycle) resulting in storage and dissipation of elastic energy. This allows the elastic and muscular arteries to act as an auxiliary pump to propel the blood fluid forward during systole and maintain a basal blood pressure during diastole. The pulsatile flow pattern caused by the contraction of the left ventricle sets up a state of harmonic motion in the blood vessel walls throughout the arterial vasculature. In this paper, we report on a kinetic energy analysis of pressure and flow velocity waveforms, which characterize energy transfer between the blood and the blood vessel walls at different frequencies of pulsatile flow and pressure. Porcine carotid arteries were tested under simulated physiologic pulsatile flow and pressure conditions in a model water bath system and compared to measurements in vivo on human carotid arteries. Fluid and wall pressures were monitored on the porcine vessels in situ using an absolute differential pressure transducer (fluid) and by a low-pressure volumetric balloon transducer (wall), respectively. A continuous-wave Doppler ultrasonic transducer monitored flow velocity and measurements were made both in vitro and in vivo at frequencies of 1.2 Hz (72 cycles per minute) and 2.1 Hz (125 cycles per minute) for comparison purposes. The areas under the pressure versus time and flow velocity versus time curves were used to calculate the relative change in work and kinetic energies. The calculations of the ratios of the relaxation slopes versus the impact slopes showed that the vessel wall absorbed more energy at the frequency of 1.2 Hz than at the 2.1 Hz frequency. The results of these calculations indicate that energy of the pulse pressure at rest pulse rates is absorbed and dissipated in the vessel wall and the surrounding extracellular matrix. At higher pulse rates and pressures, the vessel wall becomes increasingly elastic, and therefore, transmits or reflects most of the energy of the pressure pulse back to the blood fluid. As the vessel wall becomes less compliant with aging or disease, the energy absorbing and dissipating properties of the arterial wall at rest pulse rate diminish.

Blood vessels are designed to transport blood to and from the various tissues of the body. To accomplish this task, they must support enough stress to prevent mechanical failure under normal physiological conditions yet must be able to remain flexible and elastic enough to aid the heart in maintaining blood flow. The endothelium, smooth muscles, fibroblasts and the extracellular matrix must act in concert to produce and maintain a material that is capable of withstanding these stresses and alter the mechanics of vascular tissue if needed. Over time, these requirements have led to the widely accepted view that the form and function of biological materials are intimately connected. The purpose of this study was to determine the morphological differences between various blood vessels and the impact of these differences, if any, on the mechanics of these tissues. The results of our work suggest that all vascular smooth muscle cells are embedded in a collagen matrix with a 1:1 volume fraction ratio. The primary differences between blood vessels of the juvenile porcine model appear to involve differences in the volume fraction and density of elastic tissue and medial thickness. In addition, cross-linking probably plays a significant role in altering mechanical properties.

Rationale and Objectives The authors performed this study to evaluate a portable, platform-independent software program that enables users from remote sites to transform raw materials (eg, text, images, video) into Web-ready, interactive tutorials and examinations. Materials and Methods The software program evaluated consists of three modules: a network-based interface developed in the Java programming language, an Oracle 8i relational database with liaison software, and shell scripts developed in the Perl programming language to automate the authoring, maintenance, and updating of content in a dynamic quiz bank (DBQ). Four faculty members, one radiology resident, and two undergraduates majoring in computer science volunteered to create questions for the DQB and to evaluate ease of authoring. Results Faculty members with various levels of computer proficiency were able to establish a comprehensive DQB of more than 1,000 interactive questions. These radiologists reported the scripts reliable and easy to use. The DQB, offered in a pathology course for 2nd-year medical students, was used by 151 students and may have played a role in improving standardized test scores. Eighty-seven percent (n = 131) of the students believed that the DQB was extremely useful as an educational tool. Conclusion The DQB software program facilitated access to, and authoring and maintenance of, Web-based educational materials developed in the departments of radiology and pathology.

Fibrillar collagens store, transmit and dissipate elastic energy during tensile deformation. Results of previous studies suggest that the collagen molecule is made up of alternating rigid and flexible domains, and extension of the flexible domains is associated with elastic energy storage. In this study, we model the flexibility of the alpha1-chains found in types I-III collagen molecules and microfibrils in order to understand the molecular basis of elastic energy storage in collagen fibers by analysing the areas under conformational plots for dipeptide sequences. Results of stereochemical modeling suggest that the collagen triple helix is made up of rigid and flexible domains that alternate with periods that are multiples of three amino acid residues. The relative flexibility of dipeptide sequences found in the flexible regions is about a factor of five higher than that found for the flexibility of the rigid regions, and the flexibility of types II and III collagen molecules appears to be higher than that found for the type I collagen molecule. The different collagen alpha1-chains were compared by correlating the flexibilities. The results suggest that the flexibilities of the alpha1-chains of types I and III collagen are more closely related than the flexibilities of the alpha1-chains in types I and II and II and III collagen. The flexible domains found in the alpha1-chains of types I-III collagen were found to be conserved in the microfibril and had periods of about 15 amino acid residues and multiples thereof. The flexibility profiles of types I and II collagen microfibrils were found to be more highly correlated than those for types I and III and II and III. These results suggest that the domain structure of the alpha1-chains found in types I-III collagen is an efficient means for storage of elastic energy during stretching while preserving the triple helical structure of the overall molecule. It is proposed that all collagens that form fibers are designed to act as storage elements for elastic energy. The function of fibers rich in type I collagen is to store and then transmit this energy while fibers rich in types II and III collagen may store and then reflect elastic energy for dissipation through viscous fibrillar slippage. Impaired elastic energy storage by extracellular matrices may lead to cellular damage and changes in signaling by mechanochemical transduction at the extracellular matrix-cell interface.

Background: Although androgen deprivation therapy initially decreases PC tumor burden, resistance to further androgen receptor (AR)-directed treatments or chemotherapy is inevitable once CRPC is established. We postulated that the stress of ADT triggers widespread alterations in expression that renders a metastable physiologic state conditioned by epigenetic changes that might be initially reversible by targeting non-androgen pathways. We conducted a pilot study to explore genome-wide expression alterations in PC foci surviving 3 months ADT (eADT). Methods: mRNA from 7 frozen microdissected PC foci and normal counterparts (NC) were processed for RNA-seq. RNA-seq changes in eADT specimens were compared first with NC and the untreated PC in the TCGA PRAD (TCGA) database to castrate resistant (mCRPC) specimens in the dbGAP study phs000915.v1.p1database. The raw data (fastq files) was quantified using kallisto, normalized by TMM using R package edgeR, batch effects corrected using R package SVA. Analysis of differential gene expression by R package sleuth. Pathway and gene set by GSEA, GAGE/pathview packages for Gene Ontology (GO) and KEGG. Results: TMPRSS2-ERG+, 5/7. Highest DEG in eADT vs. TCGA vs mCRPC were non-coding RNA’s. Among 17431 differentially regulated paths; GSEA of eADT vs TCGA or mCRPC: 341 (1.95%) and 1366 (7.84%) up- vs 46 (0.26%) and 59 (0.34%) down-regulated. KEGG paths, eADT vs. TCGA or mCRPC, 11 and 53 up vs. 2 and 3 down- respectively. Highly down- path in eADT vs TCGA (log q Conclusions: This pilot data shows that ADT triggers a wide range of gene expression alterations that support PC cell survival and may be vulnerable to therapeutic targeting in addition to the androgen pathway. Validation of these findings is planned in a larger set of samples from the same bank. Citation Format: Anna C. Ferrari, Hatem Sabaawy, Mark Stein, David Foran, Ying Chen, Srinivasan Yegnasubramanian. Genome-wide alterations in gene expression of prostate cancer (PC) cells surviving neo-adjuvant androgen deprivation therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2415. doi:10.1158/1538-7445.AM2017-2415