Abstract: Disclosed herein are methods for identifying a subject as having NSCLC that is predicted or is likely to respond to treatment with an ALK inhibitor, for example crizotinib. The methods include identifying a sample including NSCLC tumor cells as ALK-positive or ALK-negative using immunohistochemistry (IHC) and scoring methods disclosed herein. A subject is identified as having NSCLC likely to respond to treatment with an ALK inhibitor if the sample is identified as ALK-positive and is identified as having NSCLC not likely to respond to treatment with an ALK inhibitor if the sample is identified as ALK-negative. According to certain embodiments of the methods, subjects predicted to respond to an ALK inhibitor may then be treated with an ALK inhibitor such as crizotinib.

Abstract: A slide processing apparatus controls the temperature and orientation of a microscope slide carrying one or more specimens. The apparatus heats the specimen-bearing microscope slide while the slide is oriented to both facilitate adhesion between the specimens and the slide and control movement of the specimens relative to the microscope slide. A slide dryer of the apparatus conductively heats the specimens using a conductive slide heater that physically engages the microscope slide.

Abstract: The present disclosure relates machine learning techniques for segmenting non-tumor regions in specimen images to support tumor detection and analysis. Particularly, aspects of the present disclosure are directed to accessing one or more images that comprise a non-target region (e.g., a non-tumor region) and a target region (e.g., a tumor region), predicting, by a two-dimensional segmentation model, segmentation maps for the non-target region based on discriminative features encoded from the one or more images, a segmentation mask for the one or more images based on the segmentation maps, applying the segmentation mask to the one or more images to generate non-target region masked images that exclude the non-target region from the one or more images, and classifying, by an image analysis model, a biological material or structure within the target region based on a set of features extracted from the non-target region masked images.

Abstract: A method for using a federated learning classifier in digital pathology includes distributing, by a centralized server, a global model to a plurality of client devices. The client devices further train the global model using a plurality images of a specimen and corresponding annotations to generate at least one further trained model. The client devices provide further trained models to the centralized server, which aggregates the further trained models with the global model to generate an updated global model. The updated global model is then distributed to the plurality of client devices.

Abstract: Techniques for acquiring focused images of a microscope slide are disclosed. During a calibration phase, a “base” focal plane is determined using non-synthetic and/or synthetic auto-focus techniques. Furthermore, offset planes are determined for color channels (or filter bands) and used to generate an auto-focus model. During subsequent scans, the auto-focus model can be used to quickly estimate the focal plane of interest for each color channel (or filter band) rather than re-employing the non-synthetic and/or synthetic auto-focus techniques.

Abstract: In general, the presently disclosed technology relates to identification of cancer subtypes. More specifically, the technology relates to methods for determining molecular drivers of cancer and/or progression using a multivariate image data and statistical analysis of in-situ molecular markers and morphological characteristics in the same cells of a biological sample suspected of b cancer. This analysis takes place after a single acquisition that obtains the molecular and anatomic morphology data in parallel. The analysis compares specific morphological and molecular markers to known samples exhibiting particular genetic drivers of the cancer. This method provides statistical information that allows for an increased confidence in the identification of specific molecular drivers of the cancer.

Abstract: The present disclosure describes a method of foreground segmentation and nucleus ranking for scoring dual ISH images. The method has been developed to better identify those nuclei, within a selected field of view, that meet the criteria for dual ISH scoring.

Abstract: Techniques relate to object classifications using bootstrapping of region-level annotations. For each of multiple images, regions within the image can be identified. For each region, a region-specific label can be identified, a set of objects within the region can be detected, and an object-specific label can be assigned to each object. The object-specific label can be the same as the region-specific label assigned to the region within which the object is located. A training data set can be defined to include, for each image of the multiple images, object-location data (indicating intra-image location data for the detected object) and label data (indicating the object-specific labels assigned to the objects). An image-processing model can be trained using the training data. Training can include learning values for a set of parameters that define calculations performed by the image-processing model.

Abstract: A tissue analysis system and method for the spectral deconvolution of a RGB digital image obtained from a stained biological tissue sample, by estimating the stain component images that are obtained from a staining system configuration, where the reference stain vectors are assumed to be sampled from a known color distribution. The prior knowledge of stain variability of the staining system is adopted as initial reference stain vectors and statistical distribution of their variability. Based on the initial reference stain vectors distribution, the tissue analysis system determines both the reference stain vectors and stain component images of the input image. The image is then deconvoluted based on the reference stain vectors and stain component images.

Abstract: Automated systems to make target compounds from slide stainer waste streams inactive utilizing advanced oxidation processes are described herein. Advanced oxidation processes are promoted by UV irradiation and further accelerated by use of radical initiators, such as hydrogen peroxide. The automated systems further include mechanisms for segregating components of the waste streams.

Abstract: The present disclosure relates to techniques for segmenting and detecting cells within image data using transfer learning and a multi-task scheduler. Particularly, aspects of the present disclosure are directed to accessing a plurality of images of one or more cells, extracting three labels from the plurality of images, where the three labels are extracted using a Voronoi transformation, a local clustering, and application of repel code, training, by a multi-task scheduler, a convolutional neural network model based on three loss functions corresponding to the three labels, generating, by the convolutional neural network model, a nuclei probability map and a background probability map for each of the plurality of images based on the training with the three loss functions, and providing the nuclei probability map and the background probability map.

Abstract: Automated methods for extracting nucleic acid from one or more tissue samples disposed on slides are disclosed. The methods utilize an automated slide staining apparatus that dispenses a plurality of nucleic acid extraction reagents onto the tissue sample, thus creating an extracted nucleic acid sample. The extracted nucleic acid sample may be used directly in downstream applications such as nucleic acid amplification or sequencing procedures, or may be further purified.

Abstract: Disclosed is a device for contactlessly mixing fluid present on the upper surface of the slide, where the device comprises a first nozzle array and a second nozzle array, the first nozzle array adapted to impart a bulk fluid flow to the fluid present on the upper surface of the slide, and the second nozzle array adapted to impart at least a first regional fluid flow to at least a portion of the fluid present on the upper surface of the slide.

Abstract: Disclosed herein are systems and methods for normalizing the titer of a first stain to a titer of the same stain in a template image. Also disclosed are methods of assessing stain titer levels.

Abstract: A microscope slide holder comprising a slide support member and at least one acoustic source for introducing acoustic waves to a microscope slide in communication with the slide support member such that one or more fluids present on the surface of the microscope slide are contactlessly mixed.

Abstract: Disclosed herein are novel quinone methide analog precursors and embodiments of a method and a kit of using the same for detecting one or more targets in a biological sample. The method of detection comprises contacting the sample with a detection probe, then contacting the sample with a labeling conjugate that comprises an enzyme. The enzyme interacts with a quinone methide analog precursor comprising a detectable label, forming a reactive quinone methide analog, which binds to the biological sample proximally to or directly on the target. The detectable label is then detected. In some embodiments, multiple targets can be detected by multiple quinone methide analog precursors interacting with different enzymes without the need for an enzyme deactivation step.

Abstract: Systems and methods discussed herein include, among other things, a method comprising quantifying analyte staining of a biological compartment in a region in which said staining is intermixed with analyte staining of an analytically-distinct distinct biological compartment. Disclosed systems and methods include, for example, a system and method for identifying membrane staining of an analyte of interest in regions where diffuse membrane staining is intermixed with cytoplasmic staining and/or punctate staining is disclosed. Disclosed systems and methods include, for example, a system and method for quantifying membrane staining of an analyte of interest in tissue or cytological samples having regions in which membrane staining is intermixed with cytoplasmic staining and/or punctate staining.

Abstract: Antibody/signal-generating moiety conjugates are disclosed that include an antibody covalently linked to a signal-generating moiety through a heterobifunctional polyalkyleneglycol linker. The disclosed conjugates show exceptional signal-generation in immunohistochemical and in situ hybridization assays on tissue sections and cytology samples. In one embodiment, enzyme-metallographic detection of nucleic acid sequences with hapten-labeled probes can be accomplished using the disclosed conjugates as a primary antibody without amplification.

Abstract: The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.

Abstract: The present application provides for systems and methods for detecting and estimating signals corresponding to one or more biomarkers in biological samples stained for the presence of protein and/or nucleic acid biomarkers. On particular aspect is directed to a method of estimating an amount of signal corresponding to at least one biomarker in an image of a biological sample. The method includes detecting isolated spots in a first image, deriving an optical density value of a representative isolated spot based on signal features from the detected isolated spots, estimating a number of predictive spots in signal aggregates in each of a plurality of sub-regions based on the derived optical density value of the representative isolated spot, and storing the estimated number of predictive spots and detected isolated spots in each of the plurality of generated sub-regions in a database.