An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing.

Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.

An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities.

BACKGROUND: Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). MATERIALS AND METHODS: As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. RESULTS: Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. CONCLUSION: To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.

Nonrandom Selection and Multiple Blood Feeding of Human Hosts by Anopheles Vectors: Implications for Malaria Transmission in Papua New Guinea.

Nonrandom selection and multiple blood feeding of human hosts by Anopheles mosquitoes may exacerbate malaria transmission. Both patterns of blood feeding and their relationship to malaria epidemiology were investigated in Anopheles vectors in Papua New Guinea (PNG). Blood samples from humans and mosquito blood meals were collected in villages and human genetic profiles ("fingerprints") were analyzed by genotyping 23 microsatellites and a sex-specific marker. Frequency of blood meals acquired from different humans, identified by unique genetic profiles, was fitted to Poisson and negative binomial distributions to test for nonrandom patterns of host selection. Blood meals with more than one genetic profiles were classified as mosquitoes that fed on multiple humans. The age of a person bitten by a mosquito was determined by matching the blood-meal genetic profile to the villagers' genetic profiles. Malaria infection in humans was determined by PCR test of blood samples. The results show nonrandom distribution of blood feeding among humans, with biased selection toward males and individuals aged 15-30 years. Prevalence of Plasmodium falciparum infection was higher in this age group, suggesting males in this age range could be super-spreaders of malaria parasites. The proportion of mosquitoes that fed on multiple humans ranged from 6% to 13% among villages. The patterns of host utilization observed here can amplify transmission and contribute to the persistence of malaria in PNG despite efforts to suppress it with insecticidal bed nets. Excessive feeding on males aged 15-30 years underscores the importance of targeted interventions focusing on this demographic group.

Liver disease classification from ultrasound using multi-scale CNN.

PURPOSE: Ultrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subjective. Computer-aided diagnostic tools can improve the specificity and sensitivity of US and help clinicians to perform uniform diagnoses. METHODS: In this work, we propose a novel deep learning model for nonalcoholic fatty liver disease classification from US data. We design a multi-feature guided multi-scale residual convolutional neural network (CNN) architecture to capture features of different receptive fields. B-mode US images are combined with their corresponding local phase filtered images and radial symmetry transformed images as multi-feature inputs for the network. Various fusion strategies are studied to improve prediction accuracy. We evaluate the designed network architectures on B-mode in vivo liver US images collected from 55 subjects. We also provide quantitative results by comparing our proposed multi-feature CNN architecture against traditional CNN designs and machine learning methods. RESULTS: Quantitative results show an average classification accuracy above 90% over tenfold cross-validation. Our proposed method achieves a 97.8% area under the ROC curve (AUC) for the patient-specific leave-one-out cross-validation (LOOCV) evaluation. Comprehensive validation results further demonstrate that our proposed approaches achieve significant improvements compared to training mono-feature CNN architectures ([Formula: see text]). CONCLUSIONS: Feature combination is valuable for the traditional classification methods, and the use of multi-scale CNN can improve liver classification accuracy. Based on the promising performance, the proposed method has the potential in practical applications to help radiologists diagnose nonalcoholic fatty liver disease.

Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network.

PURPOSE: Recently, the outbreak of the novel coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. In fighting against COVID-19, effective diagnosis of infected patient is critical for preventing the spread of diseases. Due to limited availability of test kits, the need for auxiliary diagnostic approach has increased. Recent research has shown radiography of COVID-19 patient, such as CT and X-ray, contains salient information about the COVID-19 virus and could be used as an alternative diagnosis method. Chest X-ray (CXR) due to its faster imaging time, wide availability, low cost, and portability gains much attention and becomes very promising. In order to reduce intra- and inter-observer variability, during radiological assessment, computer-aided diagnostic tools have been used in order to supplement medical decision making and subsequent management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologist in the interpretation of the collected data. METHOD: In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. CXR images are enhanced using a local phase-based image enhancement method. The enhanced images, together with the original CXR data, are used as an input to our proposed CNN architecture. Using ablation studies, we show the effectiveness of the enhanced images in improving the diagnostic accuracy. We provide quantitative evaluation on two datasets and qualitative results for visual inspection. Quantitative evaluation is performed on data consisting of 8851 normal (healthy), 6045 pneumonia, and 3323 COVID-19 CXR scans. RESULTS: In Dataset-1, our model achieves 95.57% average accuracy for a three classes classification, 99% precision, recall, and F1-scores for COVID-19 cases. For Dataset-2, we have obtained 94.44% average accuracy, and 95% precision, recall, and F1-scores for detection of COVID-19. CONCLUSIONS: Our proposed multi-feature-guided CNN achieves improved results compared to single-feature CNN proving the importance of the local phase-based CXR image enhancement. Future work will involve further evaluation of the proposed method on a larger-size COVID-19 dataset as they become available.

Gene expression of adipokines and adipokine receptors in the tumor microenvironment: associations of lower expression with more aggressive breast tumor features.

PURPOSE: Limited epidemiologic data are available on the expression of adipokines leptin (LEP) and adiponectin (ADIPOQ) and adipokine receptors (LEPR, ADIPOR1, ADIPOR2) in the breast tumor microenvironment (TME). The associations of gene expression of these biomarkers with tumor clinicopathology are not well understood. METHODS: NanoString multiplexed assays were used to assess the gene expression levels of LEP, LEPR, ADIPOQ, ADIPOR1, and ADIPOR2 within tumor tissues among 162 Black and 55 White women with newly diagnosed breast cancer. Multivariate mixed effects models were used to estimate associations of gene expression with breast tumor clinicopathology (overall and separately among Blacks). RESULTS: Black race was associated with lower gene expression of LEPR (P = 0.002) and ADIPOR1 (P = 0.01). Lower LEP, LEPR, and ADIPOQ gene expression were associated with higher tumor grade (P = 0.0007, P < 0.0001, and P < 0.0001, respectively) and larger tumor size (P < 0.0001, P = 0.0005, and P < 0.0001, respectively). Lower ADIPOQ expression was associated with ER- status (P = 0.0005), and HER2-enriched (HER2-E; P = 0.0003) and triple-negative (TN; P = 0.002) subtypes. Lower ADIPOR2 expression was associated with Ki67+ status (P = 0.0002), ER- status (P < 0.0001), PR- status (P < 0.0001), and TN subtype (P = 0.0002). Associations of lower adipokine and adipokine receptor gene expression with ER-, HER2-E, and TN subtypes were confirmed using data from The Cancer Genome Atlas (P-values < 0.005). CONCLUSION: These findings suggest that lower expression of ADIPOQ, ADIPOR2, LEP, and LEPR in the breast TME might be indicators of more aggressive breast cancer phenotypes. Validation of these findings are warranted to elucidate the role of the adipokines and adipokine receptors in long-term breast cancer prognosis.

AI in Medical Imaging Informatics: Current Challenges and Future Directions.

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.

Immunohistochemical analysis of adipokine and adipokine receptor expression in the breast tumor microenvironment: associations of lower leptin receptor expression with estrogen receptor-negative status and triple-negative subtype.

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 < 0.0001) in the overall sample and among Black women only. LEP, ADIPOQ, ADIPOR1, and ADIPOR2 IHC expression were not significantly associated with breast tumor clinicopathology. 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.

Two well-differentiated pancreatic neuroendocrine tumor mouse models.

Multiple endocrine neoplasia type 1 (MEN1) is a genetic syndrome in which patients develop neuroendocrine tumors (NETs), including pancreatic neuroendocrine tumors (PanNETs). The prolonged latency of tumor development in MEN1 patients suggests a likelihood that other mutations cooperate with Men1 to induce PanNETs. We propose that Pten loss combined with Men1 loss accelerates tumorigenesis. To test this, we developed two genetically engineered mouse models (GEMMs)-MPR (Men1flox/flox Ptenflox/flox RIP-Cre) and MPM (Men1flox/flox Ptenflox/flox MIP-Cre) using the Cre-LoxP system with insulin-specific biallelic inactivation of Men1 and Pten. Cre in the MPR mouse model was driven by the transgenic rat insulin 2 promoter while in the MPM mouse model was driven by the knock-in mouse insulin 1 promoter. Both mouse models developed well-differentiated (WD) G1/G2 PanNETs at a much shorter latency than Men1 or Pten single deletion alone and exhibited histopathology of human MEN1-like tumor. The MPR model, additionally, developed pituitary neuroendocrine tumors (PitNETs) in the same mouse at a much shorter latency than Men1 or Pten single deletion alone as well. Our data also demonstrate that Pten plays a role in NE tumorigenesis in pancreas and pituitary. Treatment with the mTOR inhibitor rapamycin delayed the growth of PanNETs in both MPR and MPM mice, as well as the growth of PitNETs, resulting in prolonged survival in MPR mice. Our MPR and MPM mouse models are the first to underscore the cooperative roles of Men1 and Pten in cancer, particularly neuroendocrine cancer. The early onset of WD PanNETs mimicking the human counterpart in MPR and MPM mice at 7 weeks provides an effective platform for evaluating therapeutic opportunities for NETs through targeting the MENIN-mediated and PI3K/AKT/mTOR signaling pathways.

Bacterial Profiling of Soil For Forensic Investigations: Consideration of Ex Situ Changes in Questioned and Known Soil Samples.

Soil, being diverse and ubiquitous, can potentially link a suspect or victim to a crime scene. Recently scientists have examined the microbial makeup of soil for determining its origin, and differentiating soil samples is well-established. However, when soil is transferred to evidence its microbial makeup may change over time, leading to false exclusions. In this research, "known" soils from diverse habitats were stored under controlled conditions, while evidence soils were aged on mock evidence. Limited quantities of soil were also assayed. Bacterial profiles were produced using next-generation sequencing of the 16S rRNA gene. Overall, known soils stored open at room temperature were more similar to evidence soils over time than were known soils stored bagged and/or frozen. Evidence soils, even as little as 1 mg, associated with the correct habitat 99% of the time, accentuating the importance of considering ex situ microbial changes in soil for its successful use as forensic evidence.

Statistical Analysis of Survival Models Using Feature Quantification on Prostate Cancer Histopathological Images.

BACKGROUND: Grading of prostatic adenocarcinoma is based on the Gleason scoring system and the more recently established prognostic grade groups. Typically, prostate cancer grading is performed by pathologists based on the morphology of the tumor on hematoxylin and eosin (H and E) slides. In this study, we investigated the histopathological image features with various survival models and attempted to study their correlations. METHODS: Three texture methods (speeded-up robust features, histogram of oriented gradient, and local binary pattern) and two convolutional neural network (CNN)-based methods were applied to quantify histopathological image features. Five survival models were assessed on those image features in the context with other prostate clinical prognostic factors, including primary and secondary Gleason patterns, prostate-specific antigen levels, age, and clinical tumor stages. RESULTS: Based on statistical comparisons among different image features with survival models, image features from CNN-based method with a recurrent neural network called CNN-long-short-term memory provided the highest hazard ratio of prostate cancer recurrence under Cox regression with an elastic net penalty. CONCLUSIONS: This approach outperformed the other image quantification methods listed above. Using this approach, patient outcomes were highly correlated with the histopathological image features of the tissue samples. In future studies, we plan to investigate the potential use of this approach for predicting recurrence in a wider range of cancer types.

Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images.

Computational image analysis is one means for evaluating digitized histopathology specimens that can increase the reproducibility and reliability with which cancer diagnoses are rendered while simultaneously providing insight as to the underlying mechanisms of disease onset and progression. A major challenge that is confronted when analyzing samples that have been prepared at disparate laboratories and institutions is that the algorithms used to assess the digitized specimens often exhibit heterogeneous staining characteristics because of slight differences in incubation times and the protocols used to prepare the samples. Unfortunately, such variations can render a prediction model learned from one batch of specimens ineffective for characterizing an ensemble originating from another site. In this work, we propose to adopt unsupervised domain adaptation to effectively transfer the discriminative knowledge obtained from any given source domain to the target domain without requiring any additional labeling or annotation of images at the target site. In this paper, our team investigates the use of two approaches for performing the adaptation: (1) color normalization and (2) adversarial training. The adversarial training strategy is implemented through the use of convolutional neural networks to find an invariant feature space and Siamese architecture within the target domain to add a regularization that is appropriate for the entire set of whole-slide images. The adversarial adaptation results in significant classification improvement compared with the baseline models under a wide range of experimental settings.

Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images.

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.

Intra- and Inter-Element Variability in Mitochondrial and Nuclear DNA from Fresh and Environmentally Exposed Skeletal Remains.

Successful identification of skeletonized remains often relies upon DNA analyses, frequently focusing on the mid-diaphysis of weight-bearing long bones. This study explored intra-bone DNA variability using bovine and porcine femora, along with calcanei and tali. DNA from fresh and short-term environmentally exposed bone was extracted utilizing demineralization and standard lysis buffer protocols, and DNA quantity and quality were measured. Overall, femoral epiphyses, metaphyses, and the tarsals had more nuclear and mitochondrial DNA than did the femoral diaphyses. DNA loss was much more rapid in buried bones than in surface exposed bones, while DNA quality differed based on environment, but not bone region/element. The demineralization protocol generated more DNA in some bone regions, while the standard lysis was more effective in others, and neither significantly affected DNA quality. Taken together, these findings reinforce the importance of considering inter- and intra-bone heterogeneity when sampling skeletal material for forensic DNA-based identifications.

Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images.

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.

Time Radically Alters Ex Situ Evidentiary Soil 16S Bacterial Profiles Produced Via Next-Generation Sequencing,.

Previous research has revealed the potential of soil bacterial profiling for forensic purposes; however, investigators have not thoroughly examined fluctuations in microbial profiles from soil aged on evidence. In this research, soils collected from multiple habitats were placed on evidence items and sampled over time, and then bacterial profiles were generated via next-generation sequencing of the 16S rRNA locus. Bacterial abundance charts and nonmetric multidimensional scaling plots provided visual representation of bacterial profiles temporally, while supervised classification was used to statistically associate evidence to a source. The ex situ evidence soils displayed specific, consistent taxonomic changes as they aged, resulting in their drift in multidimensional space, but never toward a different habitat. Ninety-five percent of the 364 evidentiary profiles statistically classified to the correct habitat, with misclassification generally stemming from evidence type and increased age. Ultimately, understanding bacterial changes that occur temporally in ex situ soils should enhance their use in forensic investigations.

Electromechanical Coupling Factor of Breast Tissue as a Biomarker for Breast Cancer.

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 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 -test, the electromechanical coupling factor under compression shows statistically significant differences ( 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.

Differentiation among prostate cancer patients with Gleason score of 7 using histopathology whole-slide image and genomic data.

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.

Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks.

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.

Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology.

Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes.