Abstract: Methods and systems for predictive measures of anti-EGFR therapy response in wild type RAS/EGFR+ samples, e.g., histochemical staining methods for staining EGFR, AREG, and EREG, digital analysis of stained slides, and scoring algorithms that allow prediction of a response to anti-EGFR therapies. Analysis of the stained slides and scoring algorithms may include but are not limited to: a percent tumor cell positivity, computerized clustering algorithms, area density (e.g., area of tumor positive for one or more markers over total tumor area), average intensity (e.g., computerized methodology measuring average gray scale pixel intensity), average intensity broken down according to membrane, cytoplasmic, or punctate staining patterns), or any other appropriate parameter or combination of parameters. The methods of the present invention allow for resolving spatial expression patterns of the ligands and the receptor to determine what patterns are predictive for response to anti-EGFR therapies.

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: Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

Abstract: A controller interacts with an image processing chain configured to process a video stream captured by a video camera and including an object detection algorithm which outputs a frame-wise detection score for each image region, and a masking function which applies a privacy mask in dependence of the detection score. When the controller detects a sudden change in luminance of a scene which is being imaged the video camera, it maintains or expands, for a period while the video camera is imaging the scene, the privacy mask which was applied at the detected sudden change. The maintaining or expanding of the privacy mask may be achieved by locally reducing a masking threshold applied by the masking function. The expansion of the privacy mask may be gradual over time and related to an estimated speed of a moving object in the scene.

Abstract: Automated systems and methods for determining the variability between derived expression scores for a series of biomarkers between different identified cell clusters in a whole slide image are presented. The variability between derived expression scores may be a derived inter-marker heterogeneity metric.

Abstract: Provided herein are certain inhibitors of ROCK (rho-kinases or rho-associated kinases), which are useful as medicament, for the treatment Huntington's disease in particular.

Abstract: A cutting tool and a method of operating a cutting jaw is provided. The cutting tool includes a first jaw member having a first cutting edge and a first relief area adjacent the first cutting edge, the first jaw member further having a first projection extending from the first cutting edge into the first relief area. A second jaw member has a second cutting edge and a second relief area adjacent the second cutting edge, the second jaw member further having a second projection extending from the second cutting edge into the second relief area. Wherein the first projection and the second projection are sized to engage a leg of an ear clamp and prevent fully closing the ear clamp when the cutting tool is used in a crimping operation.

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: Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

Abstract: An informatics platform provides an architecture to integrate information from relevant patient information systems.

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 subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

Abstract: Provided are mobile imaging devices and methods thereof. A mobile imaging device includes a plurality of light source sets, sensors, and a controller. At least one program is non-transiently stored in and executable by the controller. The program causes the controller to perform a method including acquiring a corresponding value for each condition in a first plurality of conditions when the device is at a first position using the sensors to collect a plurality of measurements associated with a region of interest that is not exposed to the plurality of light source sets. The method includes firing with the device held at the first position, if the corresponding value satisfies a corresponding condition specification for a respective condition in the first plurality of conditions, the plurality of light source sets based on a second plurality of conditions. Accordingly, light emits that is substantially limited to a spectral range.

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: Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: In one example, a method of extracting patient information for a medical application comprises: receiving patient data of a patient; processing the patient data using a learning system with Artificial Intelligence (AI)-assisted clinical extraction tool, the processing comprising: extracting, based on a trained language extraction model that reflects language semantics and a user's prior habit of entering other patient data, data elements from the patient data and data categories represented by the data elements, and mapping at least some of the extracted data elements to pre-determined data representations based on the data categories; populating fields of a data record of the patient based on the pre-determined data representations; and storing the populated data record in a database accessible by the medical application.

Abstract: The subject disclosure presents systems and methods for improved meso-dissection of biological specimens and tissue slides including importing one or more reference slides with annotations, using inter-marker registration algorithms to automatically map the annotations to an image of a milling slide, and dissecting the annotated tissue from the selected regions in the milling slide for analysis, while concurrently tracking the data and analysis using unique identifiers such as bar codes.

Abstract: Systems and methods disclosed herein describe a platform that automatically creates and executes a scoring guide for use in anatomical pathology. The platform can employ a fully-automated workflow for clustering the biological objects of interest and for providing cell-by-cell read-outs of heterogeneous tumor biomarkers based on their stain appearance. The platform can include a module for automatically creating and storing a scoring guide in a training database based on training digital images (240, 250), and an object classification module that executes the scoring guide when presented with new digital images to be scored pursuant to the scoring guide (299).

Abstract: The subject disclosure provides systems, computer-implemented methods, and clinical workflows for meso-dissection of biological specimens and tissue slides by incorporating annotation and inter-marker registration modules within digital pathology imaging and meso-dissection (or milling) systems. Images of a reference slide a milling slide may be acquired using the same imaging system, with the annotations on the image associated with the milling slide being based on the inter-marker registration. Each image along with its respective annotations and meta-data may be associated with a project or a case, and stored in an image management system. A same-marker registration may be used to map annotations from the annotated image of the milling slide to a live image of the milling slide. The milling slide may be milled based on the annotations, with milled tissue output into a contained that is labeled in association with the labeled input slides.