Abstract: The present invention relates to effective diagnosis of patients and assisting clinicians in treatment planning. In particular, invention provides a medical analysis system that enables refinement of molecular classification. The system provides a molecular profiling solution that will allow improved diagnosis, prognosis, response prediction to provide the right chemotherapy, and follow-up to monitor for cancer recurrence.

Abstract: A method of identifying co-expressed coding and noncoding genes is disclosed. The method may include receiving genetic sequences, mapping the genetic sequences to known coding and noncoding genes, correlating the mapped genes, and generating a co-expression network. A system for generating a co-expression network and providing the co-expression network to a user on a display is disclosed. The system may include a memory, one or more processors, one or more databases, and a display.

Abstract: An imaging visualization workstation (30) includes a graphical display device (32) and an electronic data processor, and is configured to perform a method including: spatially registering a biopsy sample extracted from a medical subject with a medical image (12) of the medical subject; combining the medical image with a graphical representation of information (20, 22) generated from the biopsy sample to generate a combined image in which the graphical representation is spatially delineated based on the spatial registration of the biopsy sample; and displaying the combined image on the graphical display device of the imaging visualization workstation. A method comprises extracting a biopsy sample spatial sample from a medical subject, processing the biopsy sample to generate biopsy information, acquiring a medical image of the subject, spatially registering the biopsy sample with the medical image, and displaying the medical image modified to include an annotation generated from the biopsy information.

Abstract: Methods and systems for visualizing gene expression data in a way that permits the comparison of different patient groups to facilitate medical applications, including cancer diagnostics and treatment planning, particularly breast cancer. The method organises gene expression data for at least one patient into a plurality of windows of a specified size, calculates an average RSEM score for all of the genes in each window and presents the average RSEM scores in a two-dimensional array, wherein one axis organises the windows by patient and the other axis organises the windows by sequence.

Abstract: Image texture feature values are computed for a set of image texture features from an image of an anatomical feature of interest in a subject, and the subject is classified respective to a molecular feature of interest based on the computed image texture feature values. The image texture feature values may be computed from one or more gray level co-occurrence matrices (GLCMs), and the image texture features may include Haralick and/or Tamura image texture features. To train the classifier, reference image texture feature values are computed for at least the set of image texture features from images of the anatomical feature of interest in reference subjects. The reference image texture feature values are divided into different population groups representing different values of the molecular feature of interest, and the classifier is trained to distinguish between the different population groups based on the reference image texture feature values.

Abstract: In a predictive outcome assessment test for predicting whether a patient undergoing a breast cancer treatment regimen will achieve pathological complete response (pCR), differential gene expression level information are generated for an input set of genes belonging to the TGF-? signaling pathway. The differential gene expression level information compares baseline gene expression level information from a baseline sample (70) of a breast tumor of a patient acquired before initiating (71) a breast cancer therapy regimen to the patient and response gene expression level information from a response sample (72) of the breast tumor acquired after initiating the breast cancer therapy regimen by administering a first dose of bevacizumab to the patient. A pCR prediction for the patient is computed based on the differential gene expression level information for the input set of genes belonging to the TGF-? signaling pathway. Related predictive outcome assessment test development methods are also disclosed.

Abstract: Image texture feature values are computed for a set of image texture features from an image of an anatomical feature of interest in a subject, and the subject is classified respective to a molecular feature of interest based on the computed image texture feature values. The image texture feature values may be computed from one or more gray level co-occurrence matrices (GLCMs), and the image texture features may include Haralick and/or Tamura image texture features. To train the classifier, reference image texture feature values are computed for at least the set of image texture features from images of the anatomical feature of interest in reference subjects. The reference image texture feature values are divided into different population groups representing different values of the molecular feature of interest, and the classifier is trained to distinguish between the different population groups based on the reference image texture feature values.

Abstract: Relevance of a study genetic variant observed in diagnostic subject genetic data that is associated by a clinical study with a phenotype characteristic is assessed as follows. A set of polymorphisms functionally related to the study genetic variant are identified. A foreground distribution is computed of variants observed in the diagnostic subject genetic data for the set of polymorphisms. A background distribution is computed of variants observed in genetic data of subjects of the clinical study for the set of polymorphisms. A comparison metric is computed comparing the foreground distribution and the background distribution. Relevance of the study variant to the diagnostic subject is quantified based on the comparison metric, with higher similarity of the foreground and background distributions corresponding to higher relevance.

Abstract: An imaging visualization workstation (30) includes a graphical display device (32) and an electronic data processor, and is configured to perform a method including: spatially registering a biopsy sample extracted from a medical subject with a medical image (12) of the medical subject; combining the medical image with a graphical representation of information (20, 22) generated from the biopsy sample to generate a combined image in which the graphical representation is spatially delineated based on the spatial registration of the biopsy sample; and displaying the combined image on the graphical display device of the imaging visualization workstation. A method comprises extracting a biopsy sample spatial sample from a medical subject, processing the biopsy sample to generate biopsy information, acquiring a medical image of the subject, spatially registering the biopsy sample with the medical image, and displaying the medical image modified to include an annotation generated from the biopsy information.

Abstract: When generating visual representations of gene activity pathways for clinical decision support, a validated pathway database that stores a plurality of validated pathways is accessed, wherein each pathway describes at least one interaction between a plurality of genes. A processor (18) is configured to execute computer-executable instructions stored in a memory (16), the instructions comprising visually representing gene activity level (28) for at least one gene across a plurality of populations, retrieving a pathway (32) from the validated pathway database, wherein the pathway includes the at least one gene, and visually representing gene activity levels for all genes in the pathway.

Abstract: A method (10) for clinical decision support by comparison of multiple molecular signatures of biological data is provided. The method comprises comparing at least two of said molecular signatures are different kinds of molecular signatures. Furthermore, a device (70), a system (100), and a computer program product (200) and a use for clinical decision support, performing the steps according to the method (10) is provided.

Abstract: In a clinical decision support method, outputs of computer-implemented analytical modules are computed for a patient. Information is displayed for the patient pertaining to a clinical question comprising outputs computed for the patient of analytical modules associated with the clinical question. The analytical modules may include modules configured to perform in silico genetic/genomic tests using genetic/genome sequencing (whole genome, whole exome, whole transcriptome, targeted gene panels, etc) or microarray data. A clinical question-module matrix (CQ-M matrix) may be generated for the patient associating clinical questions with analytical modules, and the method may further include populating the clinical questions with outputs computed for the patient of the analytical modules associated with the clinical questions by the CQ-M matrix.

Abstract: A catalog (34) of molecular marker tests specifies molecular marker tests annotated with clinical applicability annotations. An electronic patient medical record (22) stores genetic sequencing data (20) of a patient. A clinical decision support (CDS) system (30) is configured to track the clinical context of the patient wherein the clinical context includes at least a disease diagnosis and a current patient care stage. A catalog search module (32) is configured to search the catalog of molecular marker tests to identify a molecular marker test having clinical applicability to the patient in the clinical context tracked by the CDS system. The search is automatically triggered by occurrence of a trigger event defined by a set of triggering rules. A testing module (44) is configured to perform a molecular marker test identified by the identification module in silico using the genetic sequencing data of the patient stored in the electronic patient medical record.

Abstract: This invention relates to a method and an apparatus for determining a reliability indicator for at least one set of signatures obtained from clinical data collected from a group of samples. The signatures are obtained by detecting characteristics in the clinical data from the group of sample sand each of the signatures generate a first set of stratification values that stratify the group of samples. At least one additional and parallel stratification source to the signatures obtained from group of sample sis provided, the at least one additional and parallel stratification source to the signatures being independent from the signatures and generates a second set of stratification values. A comparison is done for each respective sample, where the first stratification values are compared with a true reference stratification values, and where the second stratification values are compared with the true reference stratification values.

Abstract: The present invention relates to a method for stratifying a patient into a clinically relevant group comprising the identification of the probability of an alteration within one or more sets of molecular data from a patient sample in comparison to a database of molecular data of known phenotypes, the inference of the activity of a biological network on the basis of the probabilities, the identification of a network information flow probability for the patient via the probability of interactions in the network, the creation of multiple instances of network information flow for the patient sample and the calculation of the distance of the patient from other subjects in a patient database using multiple instances of the network information flow.

Abstract: A state machine (22) stores a current state (30) comprising a clinical context defined by available patient-related information relating to a medical patient, and identifies one or more available analytical tools of a set of analytical tools (24) that are applicable to the current state. A graphical user interface module (16) receives a user selection of an available analytical tool. The state machine loads patient-related information (40) to the user-selected available analytical tool (24sel) and invokes the user-selected available analytical tool to operate on the loaded patient-related information to generate additional patient-related information relating to the medical patient and/or graphical patient-related content relating to the medical patient. The state machine transitions from the current state (30) to a next state (30?) and/or invokes the graphical user interface module to display the graphical patient related content.

Abstract: The present invention relates to a method for identifying multi-modal associations between biomedical markers which allows for the determination of network nodes and/or high ranking network members or combinations thereof, indicative of having a diagnostic, prognostic or predictive value for a medical condition, in particular ovarian cancer. The present invention further relates to a biomedical marker or group of biomedical markers associated with a high likelihood of responsiveness of a subject to a cancer therapy, preferably a platinum based cancer therapy, wherein said bio-medical marker or group of biomedical markers comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 8, 19, 20 or all markers selected from PKMYT1, SKIL, RAB8A, HIRIP3, CTNNB1, NGFR, ZCCHC11, LSP1, CD200, PAX8, CYBRD1, HOXC11, TCEAL1, FZD10,FZD1, BBS4, IRS2, TLX3, TSPAN2, TXN, and CFLAR.

Abstract: A medical apparatus (901, 100) assists clinicians, nurses or other users in choosing an intervention for the treatment of a patent suffering from an acute dynamic disease, e.g. sepsis. The medical apparatus is based on a method where a model of the disease is adapted or personalized to the patient. To ensure that the apparatus remains capable of predicting the health of the patient, the apparatus is continuously provided with new, more recent patient values and the model is continuously adapted to the new patient values. Since the medical apparatus is configured to be continuously adapted to current state of health, the apparatus is able to assist the user by generating disease management information, e.g. suggestions for medications, to an output device (902, 104).

Abstract: The present invention relates to methods, arrays and computer programs for assisting in classifying breast cancer diseases. In particular the invention relates to classifying breast cancer disorders by determining the methylation status of one or more sequences according to SEQ ID NO: 1-111. The classification may be further strengthened by also taking the expression levels of one or more proteins into account.

Abstract: A method for assigning ranking scores to pathways in a set of pathways for classifying patients is disclosed. The method comprises the steps of comparing biomolecular datasets from different groups of patients and performing an analysis in order to assign ranking scores to pathways in a set of pathways. Furthermore, a method for using cancer pathway evaluation to support clinical decision making is disclosed. This assessment is further used for stratifying ovarian cancer patients based on chemosensitivity to platinum based drugs, the standard chemotherapy. We present the method for evaluation and ranking of the most relevant pathways responsible for platinum sensitivity. Clinical decision support software system should be able to then visualize this information for a clinician, contextualize it within a patient data set and help make a final decision on the potential responsiveness.