Abstract: A system may include a data store and a processor circuit in communication with the data store and a user input device. The data store may include clinical records associated with patients. For each of the patients, the clinical records may include data corresponding to a set of inputs associated with a medical condition of the patient and data corresponding to a set of outcomes associated with the medical condition of the patient. The processor circuit may be configured to obtain the clinical records via the data store and to receive, via the user input device, a selection of a driving outcome from among the set of outcomes. The processor circuit may be configured to determine a first ranking of the set of inputs based on the driving outcome and a classification model and to provide, at a display, a screen display including a graphical representation of the set of inputs automatically arranged based on the first ranking.

Abstract: The invention relates to a method of predicting an outcome of a colorectal cancer subject, comprising determining or receiving the result of a determination of a first gene expression profile for each of one or more immune defense response genes, and/or of a second gene expression profile for each of one or more T-Cell receptor signaling genes, and/or of a third gene expression profile for each of one or more PDE4D7 correlated genes, said first, second, and third expression profile(s) being determined in a biological sample obtained from the subject, determining the prediction of outcome based on the first gene expression profile(s), or on the second gene expression profile(s), or on the third gene expression profile(s), or on the first, second, and third gene expression profile(s), and, optionally, providing the prediction to a medical caregiver or the subject.

Abstract: The invention relates to a method of predicting a response of a prostate cancer subject to therapy or of personalizing therapy of a prostate cancer subject, comprising determining or receiving the result of a determination of a first gene expression profile for each of one or more immune defense response genes, of a second gene expression profile for each of one or more T-Cell receptor signaling genes, and of a third gene expression profile for each of one or more PDE4D7 correlated genes, said first, second, and third expression profile(s) being determined in a biological sample obtained from the subject, determining the prediction of the therapy response or the personalization of the therapy based on the first, second, and third gene expression profile(s), and, optionally, providing the prediction or the personalization or a therapy recommendation based on the prediction or the personalization to a medical caregiver or the subject.

Abstract: The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Abstract: The invention relates to a method of selecting a dose for radiotherapy of a prostate cancer subject, comprising determining or receiving the result of a determination of a first gene expression profile for each of one or more immune defense response genes, and/or of a second gene expression profile for each of one or more T-Cell receptor signaling genes, and/or of a third gene expression profile for each of one or more PDE4D7 correlated genes, said first, second and third expression profile(s) being determined in a biological sample obtained from the subject, selecting the radiotherapy dose based on the first gene expression profile(s), or on the second gene expression profile(s), or on the third gene expression profile(s), or on the first, second, and third gene expression profile(s), and, optionally, providing the selection or a therapy recommendation based on the selection to a medical caregiver or the subject.

Abstract: The invention relates to a method of predicting an outcome of a bladder or kidney cancer subject, comprising determining or receiving the result of a determination of a first gene expression profile for each of one or more immune defense response genes, and/or of a second gene expression profile for each of one or more T-Cell receptor signaling genes, and/or of a third gene expression profile for each of one or more PDE4D7 correlated genes, said first, second, and third expression profile(s) being determined in a biological sample obtained from the subject, determining the prediction of outcome based on the first gene expression profile(s), or on the second gene expression profile(s), or on the third gene expression profile(s), or on the first, second, and third gene expression profile(s), and, optionally, providing the prediction to a medical caregiver or the subject.

Abstract: The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Abstract: The invention relates to a method of predicting a response of a prostate cancer subject to radiotherapy, comprising determining or receiving the result of a determination of a gene expression profile for each of two or more PDE4D7 correlated genes selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, said gene expression profiles being determined in a biological sample obtained from the subject, and determining the 5 prediction of the radiotherapy response based on the gen expression profiles for the two or more PDE4D7 correlated genes.

Abstract: The invention relates to a method of predicting a response of a prostate cancer subject to radiotherapy, comprising determining or receiving the result of a determination of a gene expression profile for each of three or more interleukin genes selected from the group consisting of: IL17RE, IL1B, IL3, IL7R, IL9R, and EBI3, said gene expression profiles being determined in a biological sample obtained from the subject, and determining, preferably by a processor, the prediction of the radiotherapy response based on the gene expression profiles for the three or more interleukin genes.

Abstract: The invention relates to a method of predicting a response of a prostate cancer subject to radiotherapy, comprising determining or receiving the result of a determination of a gene expression profile for each of five or more immune defense response genes selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, said gene expression profiles being determined in a biological sample obtained from the subject, and determining, by a processor, the prediction of the radiotherapy response based on the gene expression profiles for the five or more immune defense response genes.

Abstract: A method of clustering or grouping subjects that are similar to one another. A dataset contains, for each subject, a set of quantitative values which each represent a respective clinical or pathological feature of that subject. A principal component analysis, PCA, is performed on the dataset. Loadings of one of the first two principal components identified by the PCA are used to generate a respective dataset of weighting values. These weighting values are used to weigh or modify each set of quantitative values in the dataset. A clustering algorithm is performed on the weighted sets of subject data. The process may be iterated until user-defined stopping conditions are satisfied.

Abstract: The invention relates to a method of predicting a response of a prostate cancer subject to radiotherapy, comprising determining or receiving the result of a determination of a gene expression profile for each of eight or more T-Cell receptor signaling genes selected from the group consisting of: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, said gene expression profiles being determined in a biological sample obtained from the subject, and determining, by a processor, the prediction of the radiotherapy response based on the gene expression profiles for the eight or more T-Cell receptor signaling genes.

Abstract: The invention relates to a method of predicting a response of a prostate cancer subject to radiotherapy, comprising determining or receiving the result of a determination of a gene expression profile for each of one or more chemokine genes selected from the group consisting of: CCL2 and CXCL17, said gene expression profile(s) being determined in a biological sample obtained from the subject, and determining, by a processor, the prediction of the radiotherapy response based on the gene expression profile(s) for the one or both chemokine genes.

Abstract: The invention relates to a method of predicting a response of a prostate cancer subject to radiotherapy, comprising determining or receiving the result of a determination of a gene expression profile for each of two or more immune checkpoint genes selected from the group consisting of: LAG3, LGALS9, TDO2, TMIGD2, and VTCN1, said gene expression profiles being determined in a biological sample obtained from the subject, and determining, by a processor, the prediction of the radiotherapy response based on the gene expression profiles for the two or more immune checkpoint genes.

Abstract: The invention relates to a method of predicting a response of a prostate cancer subject to radiotherapy, comprising determining or receiving the result of a determination of a gene expression profile for each of two or more DNA repair genes selected from the group consisting of: APTX, BRCA2, NUDT1, PMS2, and POLD2, said gene expression profiles being determined in a biological sample obtained from the subject, and determining, by a processor, the prediction of the radiotherapy response based on the gene expression profiles for the two or more DNA repair genes.

Abstract: A computer implemented method for generating a graphical representation of a predicted effectiveness of a first treatment. The method comprises using (102) a clinical model to determine at least one indicator related to an outcome of a first treatment. An effectiveness of the first treatment is then predicted (104) based on the at least one indicator. The predicted effectiveness of the first treatment is then displayed (106) to a user, using a first graphical representation.

Abstract: The invention relates to a method of pre-surgical risk stratification of a prostate cancer subject, comprising determining a gene expression profile for phosphodiesterase 4D variant 7 (PDE4D7) in a biological sample obtained from the subject, determining a gene expression profile for DExH-box helicase 9 (DHX9) in the same or another biological sample obtained from the subject, and determining a pre-surgical prognostic risk score for the subject based on the gene expression profile for PDE4D7 and the gene expression profile for DHX9. This may allow for an improved stratification of the subject in a pre-surgical setting that may result in better primary treatment decisions. For instance, the pre-surgical prognostic risk score may allow to make better recommendations on whether to select active surveillance vs. active intervention, e.g., radical prostatectomy, for certain sub-populations of prostate cancer patients.

Abstract: The invention discloses apparatus for performing a prognostic evaluation of a subject potentially having prostate cancer. The apparatus comprises a memory comprising instruction data representing a set of instructions; and a processor configured to communicate with the memory and to execute the set of instructions, wherein the set of instructions, when executed by the processor, cause the processor to obtain a subject profile (102) associated 5 with the subject; obtain clinical data (104) associated with the subject; obtain imaging data (106) acquired in respect of the subject's prostate; obtain pathological information (108) relating to a biopsy acquired in respect of the subject's prostate; determine, based on at least the subject profile, the clinical data, the imaging data and the pathological information, a prognostic score (110, 112) relating to the cancer. Computer-implemented methods and a 10 computer program product are also disclosed.

Abstract: The invention relates to a method of post-surgical risk stratification of a prostate cancer subject, comprising determining a transmembrane protease, serine 2-ETS-related gene (TMPRSS2-ERG) fusion status in a biological sample obtained from the subject, determining a gene expression profile for each of one or more phosphodiesterase 4D variants in a biological sample obtained from the subject, determining an expression based risk score for the subject based on the gene expression profile for a selected phosphodiesterase 4D variant, and determining a post-surgical prognostic risk score for the subject based on the expression based risk score and post-surgical clinical variables of the subject, wherein the selected phosphodiesterase 4D variant is selected depending on the TM-PRSS2-ERG fusion status. This may allow for an improved stratification of the subject in a post-surgical setting that may result in better post-surgical, secondary treatment decisions.

Abstract: The present invention relates to a distributor device (10000) for the even distribution of a fluid (10) into 2 or more fluid streams each containing gas (11) and liquid (12), with a falling film evaporator (100000), in which the distributor device according to the invention serves to distribute the 2 or more fluid streams onto the heating pipes of the evaporator, and to the use of the distributor device (10000) according to the invention and in particular the falling film evaporator (100000) according to the invention in the production and/or preparation of chemical products. The distributor device according to the invention is characterized, in particular, by a swirl breaker (600) for the gas phase (11) which arises from separating the fluid (10) into a gas phase (11) and a liquid phase (12) within the distributor device.