Koninklijke Philips Corporation N.v.

Nilanjana Banerjee

Angel Janevski

Vinay Varadan

Nevenka Dimitrova

Sitharthan Kamalakaran

Yasser Alsafadi

Mark Simpson

Nicolas Wadih Chbat

James David Schaffer

Yee Him Cheung

Robert Lucito

Alexander Ryan Mankovich

Vartika Agrawal

Johanna Maria De Bont

Jozef Hieronymus Maria Raijmakers

Sitharthan Kalalakaran

Mine Danisman Tasar

Lyndsay Harris

Anca Ioana Daniela Bacur

Jasper Van Leeuwen

Sayan Maity

In patient cohort identification, clustering () of patients is performed using a patient comparison metric dependent on a set of features (). Information is displayed on sample patients who are similar or dissimilar to a query patient according to the clustering. User inputted comparison values are received comparing the sample patients with the query patient. The set of features and/or feature weights are adjusted to generate an adjusted patient comparison metric having improved agreement with the user inputted comparison values. The clustering is repeated using the adjusted patient comparison metric. A patient cohort is identified from a cluster () containing the query patient produced by the last clustering repetition. The information on the sample patients may be shown by simultaneously displaying two or more graphical modality representations () each plotting the sample patients and the query patient against two or more features of the modality.

Relevance feedback to improve the performance of clustering model that clusters patients with similar profiles together

A method () for forming novel signatures of biological data is provided. The method comprises ranking features based on a trend value, which is created based on multiple signatures identified by a pattern discovery method. Furthermore, a device () and a computer program product (), performing the steps according to the method () is provided. Uses of the method, for statistically analyzing clinical data, designing assays based on multiple molecular signatures and interpreting assays based on multiple molecular signatures are also provided.

Biomarkers based on sets of molecular signatures

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. Such populating advantageously re-uses outputs computed for the patient when an analytical module is associated with two or more different clinical questions by the CQ-M matrix. This system empowers the clinician to focus on the clinical aspects of patient management while allowing the data complexities of patient genomic data interpretation to be handled by the clinical decision support system.

System and method for real time clinical questions presentation and management

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 () is configured to execute computer-executable instructions stored in a memory (), the instructions comprising visually representing gene activity level () for at least one gene across a plurality of populations, retrieving a pathway () 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. The instructions further comprise a formal visual grammar representation visually representing information flow () through interactions between genes in pathway, linking the visual representation of the information flow to actionable information () for a clinician, and outputting the visual representation of the information flow and the linked actionable information to the to the clinician.

Pathway visualization for clinical decision support

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.

Diagnostic genetic analysis using variant-disease association with patient-specific relevance assessment

A catalog () of molecular marker tests specifies molecular marker tests annotated with clinical applicability annotations. An electronic patient medical record () stores genetic sequencing data () of a patient. A clinical decision support (CDS) system () 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 () 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 () 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.

Retroactive extraction of clinically relevant information from patient sequencing data for clinical decision support

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-&#x3b2; signaling pathway. The differential gene expression level information compares baseline gene expression level information from a baseline sample () of a breast tumor of a patient acquired before initiating () a breast cancer therapy regimen to the patient and response gene expression level information from a response sample () 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-&#x3b2; signaling pathway. Related predictive outcome assessment test development methods are also disclosed.

Predictive outcome assessment for chemotherapy with neoadjuvant bevacizumab

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 biomedical 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. Furthermore, an assay for detecting, diagnosing, graduating, monitoring or prognosticating a medical condition, or for detecting, 1 diagnosing, monitoring or prognosticating the responsiveness of a subject to a therapy against said medical condition, in particular ovarian cancer, is provided, as well as a corresponding method for classifying a subject comprising and a medical decision support system.

Identification of multi-modal associations between biomedical markers

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.

Medical analysis system

An imaging visualization workstation () includes a graphical display device () 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 () of the medical subject; combining the medical image with a graphical representation of information () 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.

Clinical workstation integrating medical imaging and biopsy data and methods using same

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.

Integrated phenotyping employing image texture features

A method () 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 (), a system (), and a computer program product () and a use for clinical decision support, performing the steps according to the method () is provided.

Device and method for comparing molecular signatures

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. The signatures are assigned with similarity measure indicators indicating whether the first and the second stratification values match with the true reference stratification values. These are then implementing as input in determining the reliability of the signatures.

Method of determining a reliability indicator for signatures obtained from clinical data and use of the reliability indicator for favoring one signature over the other

A medical apparatus () 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 ().

Growing community of inventors

Armonk, NY, United States of America

Nilanjana Banerjee