Abstract: Systems and methods are provided for automated assessment of regional myocardial function using wall motion analysis methods that analyze various features/parameters of patient information (image data and non-image data) obtained from medical records of a patient. For example, a method for providing automatic diagnostic support for cardiac imaging generally comprises obtaining image data of a heart of a patient, obtaining features from the image data of the heart, which are related to motion of the myocardium of the heart, and automatically assessing regional myocardial function of one or more regions of a myocardial wall using the obtained features.

Abstract: A method and an apparatus display marks in an image data set, wherein an image data set comprising marks is provided and wherein during a review phase not all marks within the image data set are displayed at the same time. A list of the marks can be generated by sorting the marks depending on a predetermined sorting criterion and wherein the marks are displayed temporally one after another within the image data set in accordance with the generated list. The image data set is for example a medical image data set, wherein the marks are computer-aided detection (CAD) marks and wherein the sorting criterion is the probability of marking illness, in particular the suspiciousness.

Abstract: Systems and methods are provided for processing a medical image to automatically identify the anatomy and view (or pose) from the medical image and automatically assess the diagnostic quality of the medical image. In one aspect a method for automated decision support for medical imaging includes obtaining image data, extracting feature data from the image data, and automatically performing anatomy identification, view identification and/or determining a diagnostic quality of the image data, using the extracted feature data.

Abstract: Hierarchal modeling is used to distinguish one state or class from three or more classes. In a first stage, a normal or other class is distinguished from a diseased or other groups of classes. If the results of the first stage classification indicate diseased or data within the groups of different classes, a subsequent stage of classification is performed. In a subsequent stage of classification, the data is classified to distinguish one or more other classes from the remaining classes. Using two or more stages, medical information is classified by eliminating one or more possible classes in each stage to finally identify a particular class most appropriate or probable for the data.

Abstract: Missing data is addressed in a medical decision support system. The classifier applied to the patient record with missing data is obtained as a function of the available data. For example, one of a plurality of different classifiers is selected based on the features available in the patient record to be classified. The different classifiers are developed using different feature sets. The classifier developed using a feature set closest to or a sub-set of the features available in the patient record is selected for classifying the patient record. As another example, features in a training set corresponding to features available in the patient record are used to build a classifier. The classifier is applied to the patient record by inputting the available features of the patient record.

Abstract: CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

Abstract: A data replication facility for distributed computing environments. A computer network having a plurality of network nodes utilizes a distributed directory provider service (DPS) having an established master node. The DPS supports a file replication service (FRS). The FRS establishes one of the nodes as originator node which receives new or updated files from one or more user/GUIs and/or from one or more software providers such as a security provider. The originator node in cooperation with the master node establish a backup copy of the new or updated file in the master node. Thereafter, the originator node publishes a File Version Variable (FVV) representation of the new or updated file to other network nodes (slave nodes) which obtain such file from the originator or, if need be, from the backup master node.

Abstract: Modeling of prognosis of survivability, side-effect, or both is provided. For example, RILI is predicted using bullae information. The amount, volume or ratio of Bullae, even alone, may indicate the likelihood of complication, such as the likelihood of significant (e.g., stage 3) pneumonitis. As another example, RILI is predicted using uptake values of an imaging agent. Standardized uptake from a functional image (e.g., FDG uptake from a positron emission image), alone or in combination with other features, may indicate the likelihood of side-effect. In another example, survivability, such as two-year survivability, is predicted using blood biomarkers. The characteristics of a patient's blood may be measured and, alone or in combination with other features, may indicate the likelihood of survival. The modeling may be for survivability, side-effect, or both and may use one or more of the blood biomarker, uptake value, and bullae features.

Abstract: A system for modeling complete response prediction is provided. The system includes an input that is operable to receive treatment information representing treatment data that may be used to predict a complete response of a tumor. The complete response may include a disappearance of all or substantially all of a disease. A processor may be operable to use a model to predict complete response of the tumor as a function of the treatment data. The model represents a probability of complete response to treatment given the treatment data. A display is operable to output an image as a function of the complete response prediction.

Abstract: Functional imaging information is used to determine a probability of residual disease given a treatment. The functional imaging information shows different characteristic levels for different regions of the tumor. The probability is output for planning use and/or used to automatically determine dose by region. Using the probability, the dose may be distributed by region so that some regions receive a greater dose than other regions. This distribution by region of dose more likely treats the tumor with a same dose, allows a lesser dose to sufficient treat the tumor, and/or allows a greater dose with a lesser or no increase in risk to normal tissue. The dose plan may account for personalized tumors as each patient may have distinct tumors. Probability of dose application accuracy may also be used, so that a combined treatment probability allows efficient dose planning.

Abstract: A computer-implemented method for privacy-preserving data mining to determine cancer survival rates includes providing a random matrix B agreed to by a plurality of entities, wherein each entity i possesses a data matrix Ai of cancer survival data that is not publicly available, providing a class matrix Di for each of the data matrices Ai, providing a kernel K(Ai, B) by each of said plurality of entities to allow public computation of a full kernel, and computing a binary classifier that incorporates said public full kernel, wherein said classifier is adapted to classify a new data vector according to a sign of said classifier.

Abstract: A system and method for tracking a global shape of an object in motion is disclosed. One or more control points along an initial contour of the global shape are defined. Each of the one or more control points is tracked as the object is in motion. Uncertainty of a location of a control point in motion is represented using a number of techniques. The uncertainty to constrain the global shape is exploited using a prior shape model. In an alternative embodiment, multiple appearance models are built for each control point and the motion vectors produced by each model are combined in order to track the shape of the object. The movement of the shape of the object can be visually tracked using a display and color vectors.

Abstract: The present invention provides methods and compositions for predicting patient responses to cancer treatment using hypoxia gene signatures. These methods can comprise measuring in a biological sample from a patient the levels of gene expression of a group of the genes designated herein. The present invention also provides for microarrays that can detect expression from a group of genes.

Abstract: A method for providing automatic decision support for the selection of a treatment protocol includes receiving information pertaining to the patient, the condition and the preferences partially or wholly by automatically parsing patient records. The information pertaining to the patient, the condition and the preferences may be partially or wholly ascertained by prompting a user for manual input of the desired data. The information is used to calculate one or more surrogate variables. A knowledge base including a plurality of treatment protocols is accessed and one or more of the plurality of treatment protocols are selected from the knowledge base based on the received information pertaining to the patient, the condition, and the preferences, the surrogate variables, and using a probabilistic framework. The selected treatment protocols are provided as treatment guidance.

Abstract: A radio frequency (RF) receiver having an analog gain control section for controlling the gain of a received RF signal, and a digital demodulator section for demodulating the output of the gain control section, includes an analog GO circuit connected to recognize the presence of a data packet within a received RF signal in the analog gain control section, and in response to produce an analog GO signal, a digital GO circuit connected to recognize the presence of a data packet within the received RF signal from the digital demodulator section, and in response to produce a digital GO signal, and control circuitry connected to enable gain control functions within the analog gain control section and digital demodulator functions within the digital demodulator section in response to respective analog and digital GO signals.

Abstract: A list of biomarkers indicative of patient outcome is reduced. A computer program is applied to a set of biomarkers indicative of a patient outcome (e.g., prognosis, diagnosis, or treatment result). The computer program models the set of biomarkers with a subset of the biomarkers. The subset is identified without labeling based on the patient outcome. Instead, biomarker scores (e.g., sequence score) are used to identify the subset of biomarkers.

Abstract: An RF receiver includes an analog GO circuit that recognizes the presence of a data packet within a received RF signal in the receiver's analog gain control section, and in response produces an analog GO signal, and a digital GO circuit that recognizes the presence of a data packet from the receiver's digital demodulator section, and in response produces a digital GO signal. Gain control functions within the analog gain control section, and digital demodulator functions within the digital demodulator section, are enabled in response to respective analog and digital GO signals. The system is capable of distinguishing a valid data packet from noise, co-channel interference and adjacent panel interference. A symbol timing recovery circuit within the receiver identifies periodic samples of a data packet preamble that most closely match the preamble bits, with the receiver employing a continuation of the identified periodic samples to decode the remainder of the data packet.

Abstract: The present invention provides methods and compositions for predicting patient responses to cancer treatment using a proliferation gene signature. These methods can comprise measuring in a biological sample from a patient the levels of gene expression of a group of the genes designated herein. The present invention also provides for microarrays that can detect expression from a group of genes.

Abstract: Improvements in mining information from patient records and/or use of such mined information are provided. The identity of the patient is used to link to patient records at different institutions for mining. The user controls one or more thresholds for mining and/or inferring. By providing a user interface that allows selection of a portion of the statistical summary, data supporting the statistics may be output. To assist in understanding the knowledge base used for mining or inferring, a visual representation is output. The mining may be used for diagnosis related groupings.

Abstract: Medical or other data is de-identified by obfuscation. Located instances are replaced. By replacing with values in a same format and level of generality, multiple possible identifications—the replacement values and the instances not located—are provided in the data, obfuscating the original identification. By replacing as a function of a probability, the resulting data set has different instances distributed in a way making identification of the actual or original instances not located by searching more difficult.