Learning to predict RNA sequence expressions from whole slide images with applications for search and classification.

Deep learning methods are widely applied in digital pathology to address clinical challenges such as prognosis and diagnosis. As one of the most recent applications, deep models have also been used to extract molecular features from whole slide images. Although molecular tests carry rich information, they are often expensive, time-consuming, and require additional tissue to sample.

Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens.

Importance: For prostate cancer, Gleason grading of the biopsy specimen plays a pivotal role in determining case management. However, Gleason grading is associated with substantial interobserver variability, resulting in a need for decision support tools to improve the reproducibility of Gleason grading in routine clinical practice.

Objective: To evaluate the ability of a deep learning system (DLS) to grade diagnostic prostate biopsy specimens.

Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection.

Background: Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF). OOF is often only detected on careful review, potentially causing rescanning, and workflow delays.

Erratum: Publisher Correction: Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.

[This corrects the article DOI: 10.1038/s41746-019-0112-2.].

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis.

The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use.

Similar image search for histopathology: SMILY.

The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Although these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location of regions of interest on each image. As pathology images are extremely large (up to 100,000 pixels in each dimension), further laborious visual search of each image may be needed to find the feature of interest.

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies.

Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools.

Introduction: The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on visual selection for many research and clinical applications. However, this can be a daunting task when a large number of cells are required for molecular analysis or when a sizeable number of specimens need to be evaluated.

Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer.

Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology to improve histopathologic interpretation and clinical care. To evaluate the potential impact of digital assistance on interpretation of digitized slides, we conducted a multireader multicase study utilizing our deep learning algorithm for the detection of breast cancer metastasis in lymph nodes.

Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists.

Context.—: Nodal metastasis of a primary tumor influences therapy decisions for a variety of cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error-prone, especially for small tumor foci.

Cancer Treatment with Anti-PD-1/PD-L1 Agents: Is PD-L1 Expression a Biomarker for Patient Selection?

Strategies to help improve the efficacy of the immune system against cancer represent an important innovation, with recent attention having focused on anti-programmed death (PD)-1/PD-ligand 1 (L1) monoclonal antibodies. Clinical trials have shown objective clinical activity of these agents (e.g.

High-Throughput Microdissection for Next-Generation Sequencing.

Precision medicine promises to enhance patient treatment through the use of emerging molecular technologies, including genomics, transcriptomics, and proteomics. However, current tools in surgical pathology lack the capability to efficiently isolate specific cell populations in complex tissues/tumors, which can confound molecular results. Expression microdissection (xMD) is an immuno-based cell/subcellular isolation tool that procures targets of interest from a cytological or histological specimen.

SIVQ-LCM protocol for the ArcturusXT instrument.

SIVQ-LCM is a new methodology that automates and streamlines the more traditional, user-dependent laser dissection process. It aims to create an advanced, rapidly customizable laser dissection platform technology. In this report, we describe the integration of the image analysis software Spatially Invariant Vector Quantization (SIVQ) onto the ArcturusXT instrument.

Optimal molecular profiling of tissue and tissue components: defining the best processing and microdissection methods for biomedical applications.

Isolation of well-preserved pure cell populations is a prerequisite for sound studies of the molecular basis of any tissue-based biological phenomenon. This updated chapter reviews current methods for obtaining anatomically specific signals from molecules isolated from tissues, a basic requirement for productive linking of phenotype and genotype. The quality of samples isolated from tissue and used for molecular analysis is often glossed over or omitted from publications, making interpretation and replication of data difficult or impossible.

Pathology informatics fellowship retreats: The use of interactive scenarios and case studies as pathology informatics teaching tools.

Background: Last year, our pathology informatics fellowship added informatics-based interactive case studies to its existing educational platform of operational and research rotations, clinical conferences, a common core curriculum with an accompanying didactic course, and national meetings.

Methods: The structure of the informatics case studies was based on the traditional business school case study format. Three different formats were used, varying in length from short, 15-minute scenarios to more formal multiple hour-long case studies.

Semiautomated laser capture microdissection of lung adenocarcinoma cytology samples.

Objective: In the past decade molecular diagnostics has changed the clinical management of lung adenocarcinoma patients. Molecular diagnostics, however, is largely dependent on the quantity and quality of the tumor DNA that is retrieved from the tissue or cytology samples. Frequently, patients are diagnosed on cytology specimens where the tumor cells are scattered within the cell block, making selecting for tumor enrichment difficult.

Image microarrays derived from tissue microarrays (IMA-TMA): New resource for computer-aided diagnostic algorithm development.

Background: Conventional tissue microarrays (TMAs) consist of cores of tissue inserted into a recipient paraffin block such that a tissue section on a single glass slide can contain numerous patient samples in a spatially structured pattern. Scanning TMAs into digital slides for subsequent analysis by computer-aided diagnostic (CAD) algorithms all offers the possibility of evaluating candidate algorithms against a near-complete repertoire of variable disease morphologies. This parallel interrogation approach simplifies the evaluation, validation, and comparison of such candidate algorithms.

Interleukin-1α mediates the antiproliferative effects of 1,25-dihydroxyvitamin D3 in prostate progenitor/stem cells.

Vitamin D(3) is a promising preventative and therapeutic agent for prostate cancer, but its implementation is hampered by a lack of understanding about its mechanism of action. Upon treatment with 1α,25-dihydroxyvitamin D(3) [1,25(OH)(2)D(3), vitamin D(3)], the metabolically active form of vitamin D(3), adult prostate progenitor/stem cells (PrP/SC) undergo cell-cycle arrest, senescence, and differentiation to an androgen receptor-positive luminal epithelial cell fate. Microarray analyses of control- and vitamin D(3)-treated PrP/SCs revealed global gene expression signatures consistent with induction of differentiation.

Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology.

Introduction: HISTORICALLY, EFFECTIVE CLINICAL UTILIZATION OF IMAGE ANALYSIS AND PATTERN RECOGNITION ALGORITHMS IN PATHOLOGY HAS BEEN HAMPERED BY TWO CRITICAL LIMITATIONS: 1) the availability of digital whole slide imagery data sets and 2) a relative domain knowledge deficit in terms of application of such algorithms, on the part of practicing pathologists. With the advent of the recent and rapid adoption of whole slide imaging solutions, the former limitation has been largely resolved. However, with the expectation that it is unlikely for the general cohort of contemporary pathologists to gain advanced image analysis skills in the short term, the latter problem remains, thus underscoring the need for a class of algorithm that has the concurrent properties of image domain (or organ system) independence and extreme ease of use, without the need for specialized training or expertise.

Functional genomics: new insights into the 'function' of low levels of gene expression in stem cells.

Understanding the global gene expression profile of stem cells and their multilineage differentiation will be essential for their ultimate therapeutic application. Efforts to characterize stem cells have relied on analyzing the genome-wide expression profiles that are biased towards the identification of genes that display the most pronounced differential expression. Rather than being viewed as a "blank" state, recent studies suggest that stem cells express low levels of multiple lineage specific genes prior to differentiation, a phenomenon known as "lineage priming.

Ethanol alters the osteogenic differentiation of amniotic fluid-derived stem cells.

Background: Fetal alcohol spectrum disorder (FASD) is a set of developmental defects caused by prenatal alcohol exposure. Clinical manifestations of FASD are highly variable and include mental retardation and developmental defects of the heart, kidney, muscle, skeleton, and craniofacial structures. Specific effects of ethanol on fetal cells include induction of apoptosis as well as inhibition of proliferation, differentiation, and migration.