Abstract: A system and method for analyzing clinical data includes a reference database comprising a stored set of reference data comprising enumerated results of a reference population for a clinical test. The system also includes a patient database comprising a stored set of clinical data corresponding to a patient result for the clinical test that is selected from the enumerated results. A processor is included in the system and is programmed to access the patient and reference databases, identify a distribution of reference data over the enumerated test results, and calculate a relevance index based on the distribution of reference data. The processor is further programmed to compare the patient result to the distribution of reference data, calculate a disagreement index based on the comparison, and calculate a deviation index from the relevance and disagreement indices. A graphical user interface is also included to output a visualization of the deviation index.

Abstract: A system and method for analyzing and visualizing a local feature of interest includes access of a clinical image dataset comprising clinical image data acquired from a patient, identification of a region of interest (ROI) from the clinical image dataset, and extraction of at least one local feature corresponding to the ROI. The system and method also include definition of a local feature dataset comprising data representing at least one local feature, access of a pre-computed reference dataset comprising image data representing an expected value of the at least one identified derived characteristic of interest, and comparison of the characteristic dataset to the pre-computed reference dataset. Further, the system and method include calculation of at least one deviation metric from the comparison and output of a visualization of the at least one deviation metric.

Abstract: A system and method for analyzing and visualizing spectral CT data includes access of a set of image data acquired from a patient comprising spectral CT data, identification of a plurality of target regions of interest (TROIs) and a reference region of interest (RROI) from the set of image data, extraction of a plurality of target spectral Hounsfield unit (HU) curves from image data representing the plurality of TROIs, extraction of a reference spectral HU curve from image data representing the RROI, normalization of the plurality of target spectral HU curves with respect to the reference spectral HU curve, and display of the plurality of normalized target spectral HU curves.

Abstract: A system and method for analyzing and visualizing spectral CT data includes access of a set of image data acquired from a patient comprising spectral CT data, identification of a plurality of target regions of interest (TROIs) and a reference region of interest (RROI) from the set of image data, extraction of a plurality of target spectral Hounsfield unit (HU) curves from image data representing the plurality of TROIs, extraction of a reference spectral HU curve from image data representing the RROI, normalization of the plurality of target spectral HU curves with respect to the reference spectral HU curve, and display of the plurality of normalized target spectral HU curves.

Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and decision support in a drug development process. A computer-implemented method for integrated comparative holistic analysis of drug development and related quantifiable patient treatment decision support for a medical condition having a known disease signature.

Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and patient treatment decision support in a drug therapy development process. A computer-implemented method for developing a therapeutic drug treatment plan for a patient using quantifiable comparative holistic decision support. In addition, the inventive may also be summarized as a computer-implemented method for developing a therapeutic drug treatment plan for a patient using quantifiable comparative holistic decision support for a known disease signature corresponding to a subject medical condition of a patient.

Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and decision support in a drug development process. An example of the inventive apparatus includes a standardizer to at least one of standardize and normalize data related to drug performance for a known disease signature; and a deviation analyzer to analyze said data based on at least one of a plurality of different efficacy metrics, wherein a quantified variation between a first data set of results corresponding to an identified target drug and a second data set of results corresponding to a control, wherein said first data set of results is provided for comparison with the second data set of results and the deviation therebetween is compared to the at least one efficacy metric associated with the disease signature.

Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and decision support in a drug development process. An example method includes providing a first set of data corresponding to a drug of interest: providing a disease signature; providing a reference set of drug interaction data associated with the disease signature, comparing the first set of data to the reference set using a holistic analysis, and reporting the results of the comparison. An example of the apparatus includes, a standardizer to at least one of standardize and normalize drug interaction data related to a pre-selected disease signature: a deviation analyzer to compare the drug interaction data to data corresponding to a drug under review using a holistic analysis, and a reporter for reporting the output of the deviation analyzer.

Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and decision support in a drug development process. An example method includes accessing data related to drug development; pre-processing the data to prepare the data for measurement and analysis; and analyzing the data based on at least one of a plurality of different metrics. Each metric corresponds to a quantified variation between a first data set of results corresponding to a category in the drug development process. The first data set of results is provided for comparison with a second data set of results corresponding to at least one other category in the drug development process. At least some of the plurality of metrics are aggregated to generate a visual representation representing an integrated comparative visualization for the identified category.

Abstract: A system and method for analyzing and visualizing a local feature of interest includes access of a clinical image dataset comprising clinical image data acquired from a patient, identification of a region of interest (ROI) from the clinical image dataset, and extraction of at least one local feature corresponding to the ROI. The system and method also include definition of a local feature dataset comprising data representing at least one local feature, access of a pre-computed reference dataset comprising image data representing an expected value of the at least one identified derived characteristic of interest, and comparison of the characteristic dataset to the pre-computed reference dataset. Further, the system and method include calculation of at least one deviation metric from the comparison and output of a visualization of the at least one deviation metric.

Abstract: A system and method for analyzing and visualizing local clinical features includes the access of a set of clinical data acquired from a patient, the set of clinical data comprising image data and clinical test result data. The system also includes identification of a region of interest (ROI) from the image data, identification of a clinical test dataset from the clinical test result data, the clinical test dataset comprising data from clinical tests associated with the ROI. Further the system includes the access of a deviation map representing a deviation between the clinical test dataset and a reference dataset, and the output of a visualization of the deviation map.

Abstract: A system and method for analyzing and visualizing local clinical features includes access of a medical image dataset comprising image data acquired from a patient and identification of a region of interest (ROI) dataset corresponding to an ROI from the medical image dataset. The system also includes application of an automated algorithm to the ROI dataset, identification of an intermediate result used by the automated algorithm to analyze the ROI, and access of reference data corresponding to the intermediate result, the reference data derived from a reference dataset and representing an expected behavior of the intermediate result. Further, the system includes comparison of the intermediate result to the reference data, generation of a deviation metric based on the comparison, the deviation metric representing a deviation of the intermediate result, and creation of a visual representation of the deviation metric.

Abstract: A system and method for analyzing and visualizing local clinical features includes identification of a first region of interest (ROI) from a medical image dataset acquired from a patient and extraction of a feature dataset representing a feature of interest specific to the ROI. The system also includes identification of a second ROI from the medical image dataset, extraction of a reference dataset comprising reference data representing an expected behavior of the feature of interest, comparison of the feature dataset to the reference dataset, generation of a deviation metric representing a deviation of the feature of interest based on the comparison, and creation of a visual representation of the deviation metric.

Abstract: A system and method for analyzing clinical data includes a reference database comprising a stored set of reference data comprising enumerated results of a reference population for a clinical test. The system also includes a patient database comprising a stored set of clinical data corresponding to a patient result for the clinical test that is selected from the enumerated results. A processor is included in the system and is programmed to access the patient and reference databases, identify a distribution of reference data over the enumerated test results, and calculate a relevance index based on the distribution of reference data. The processor is further programmed to compare the patient result to the distribution of reference data, calculate a disagreement index based on the comparison, and calculate a deviation index from the relevance and disagreement indices. A graphical user interface is also included to output a visualization of the deviation index.