Abstract: Disclosed is a gene expression panel that can predict radiation sensitivity (radiosensitivity) of a tumor in a subject. A method of predicting radiation sensitivity is provided that is based on cellular clonogenic survival after 2 Gy (SF2) for 48 cell lines. Gene expression is used as the basis of the prediction model. The radiosensitivity cell-based prediction model is validated using clinical patient data from rectal and esophagus cancer patients that received RT before surgery. The radiosensitivity genomic-based prediction model identifies patients with rectal cancer that may benefit from RT treatment by assigning higher values of SF2 to radio-resistant patients and lower values of SF2 to radio-sensitive patients.

Abstract: Disclosed is a gene expression panel that can predict radiation sensitivity (radiosensitivity) of a tumor in a subject. A method of predicting radiation sensitivity is provided that is based on cellular clonogenic survival after 2 Gy (SF2) for 48 cell lines. Gene expression is used as the basis of the prediction model. The radiosensitivity cell-based prediction model is validated using clinical patient data from rectal and esophagus cancer patients that received RT before surgery. The radiosensitivity genomic-based prediction model identifies patients with rectal cancer that may benefit from RT treatment by assigning higher values of SF2 to radio-resistant patients and lower values of SF2 to radio-sensitive patients.

Abstract: An example method for analyzing quantitative information obtained from radiological images includes identifying a ROI or a VOI in a radiological image, segmenting the ROI or the VOI from the radiological image and extracting quantitative features that describe the ROI or the VOI. The method also includes creating a radiological image record including the quantitative features, imaging parameters of the radiological image and clinical parameters and storing the radiological image record in a data structure containing a plurality of radiological image records. In addition, the method includes receiving a request with the patient's radiological image or information related thereto, analyzing the data structure to determine a statistical relationship between the request and the radiological image records and generating a patient report with a diagnosis, a prognosis or a recommended treatment regimen for the patient's disease based on a result of analyzing the data structure.

Abstract: The invention provides a molecular marker set that can be used for prognosis of colorectal cancer in a colorectal cancer patient. The invention also provides methods and computer systems for evaluating prognosis of colorectal cancer in a colorectal cancer patient based on the molecular marker set. The invention also provides methods and computer systems for determining chemotherapy for a colorectal cancer patient and for enrolling patients in clinical trials.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: An example method for analyzing quantitative information obtained from radiological images includes identifying a ROI or a VOI in a radiological image, segmenting the ROI or the VOI from the radiological image and extracting quantitative features that describe the ROI or the VOI. The method also includes creating a radiological image record including the quantitative features, imaging parameters of the radiological image and clinical parameters and storing the radiological image record in a data structure containing a plurality of radiological image records. In addition, the method includes receiving a request with the patient's radiological image or information related thereto, analyzing the data structure to determine a statistical relationship between the request and the radiological image records and generating a patient report with a diagnosis, a prognosis or a recommended treatment regimen for the patient's disease based on a result of analyzing the data structure.

Abstract: Disclosed is a gene expression panel that can predict radiation sensitivity (radiosensitivity) of a tumor in a subject. A method of predicting radiation sensitivity is provided that is based on cellular clonogenic survival after 2 Gy (SF2) for 48 cell lines. Gene expression is used as the basis of the prediction model. The radiosensitivity cell-based prediction model is validated using clinical patient data from rectal and esophagus cancer patients that received RT before surgery. The radiosensitivity genomic-based pre-diction model identifies patients with rectal cancer that may benefit from RT treatment by assigning higher values of SF2 to radio-resistant patients and lower values of SF2 to radio-sensitive patients.

Abstract: An example method for analyzing quantitative information obtained from radiological images includes identifying a ROI or a VOI in a radiological image, segmenting the ROI or the VOI from the radiological image and extracting quantitative features that describe the ROI or the VOI. The method also includes creating a radiological image record including the quantitative features, imaging parameters of the radiological image and clinical parameters and storing the radiological image record in a data structure containing a plurality of radiological image records. In addition, the method includes receiving a request with the patient's radiological image or information related thereto, analyzing the data structure to determine a statistical relationship between the request and the radiological image records and generating a patient report with a diagnosis, a prognosis or a recommended treatment regimen for the patient's disease based on a result of analyzing the data structure.

Abstract: An example method for analyzing quantitative information obtained from radiological images includes identifying a ROI or a VOI in a radiological image, segmenting the ROI or the VOI from the radiological image and extracting quantitative features that describe the ROI or the VOI. The method also includes creating a radiological image record including the quantitative features, imaging parameters of the radiological image and clinical parameters and storing the radiological image record in a data structure containing a plurality of radiological image records. In addition, the method includes receiving a request with the patient's radiological image or information related thereto, analyzing the data structure to determine a statistical relationship between the request and the radiological image records and generating a patient report with a diagnosis, a prognosis or a recommended treatment regimen for the patient's disease based on a result of analyzing the data structure.

Abstract: Actively dividing tumors appear to progress to a life threatening condition more rapidly than slowly dividing tumors. Assessing actively dividing tumors currently involves a manual assessment of the number of mitotic cells in a histological slide prepared from the tumor and assessed by a trained pathologist. Disclosed is a method for using cumulative information from a series of expressed genes to determine tumor prognosis. This cumulative information can be used to categorize tumor samples into high mitotic states or low mitotic states using a mathematical algorithm and gene expression data derived from microarrays or quantitative-Polymerase Chain Reaction (Q-PCR) data. The specific mathematical description outlines how the algorithm assesses the most informative subset of genes from the full list of genes during the assessment of each sample.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: This invention is a model that simulates the complexity of biological signaling in a cell in response to radiation therapy. Using gene expression profiles and radiation survival assays in an algorithm, a systems model was generated of the radiosensitivity network. The network consists of ten highly interconnected genetic hubs with significant signal redundancy. The model was validated with in vitro tests perturbing network components, correctly predicting radiation sensitivity ? times. The model's clinical relevance was shown by linking clinical radiosensitivity targets to the model network. Clinical applications were confirmed by testing model predictions against clinical response to preoperative radiochemotherapy in patients with rectal or esophageal cancer.

Abstract: Here the inventors describe a tumor classifier based on protein expression. Also disclosed is the use of proteomics to construct a highly accurate artificial neural network (ANN)-based classifier for the detection of an individual tumor type, as well as distinguishing between six common tumor types in an unknown primary diagnosis setting. Discriminating sets of proteins are also identified and are used as biomarkers for six carcinomas. A leave-one-out cross validation (LOOCV) method was used to test the ability of the constructed network to predict the single held out sample from each iteration with a maximum predictive accuracy of 87% and an average predictive accuracy of 82% over the range of proteins chosen for its construction.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.

Abstract: This invention is a model that simulates the complexity of biological signaling in a cell in response to radiation therapy. Using gene expression profiles and radiation survival assays in an algorithm, a systems model was generated of the radiosensitivity network. The network consists of ten highly interconnected genetic hubs with significant signal redundancy. The model was validated with in vitro tests perturbing network components, correctly predicting radiation sensitivity 2/3 times. The model's clinical relevance was shown by linking clinical radiosensitivity targets to the model network. Clinical applications were confirmed by testing model predictions against clinical response to preoperative radiochemotherapy in patients with rectal or esophageal cancer.

Abstract: A classifier to predict cellular radiation sensitivity based on gene expression profiles in thirty-five cell lines from the NCI panel of 60 cancer cell lines (NCI-60), using a novel approach to predictive gene analysis. Three novel genes are provided, retinoblastoma binding protein 4 (RbAp48), G-protein signaling regulator 19 (RGS19) and ribose-5-phosphate isomerase A (R5PIA) whose expression values were correlated with radiation sensitivity.

Abstract: Actively dividing tumors appear to progress to a life threatening condition more rapidly than slowly dividing tumors. Assessing actively dividing tumors currently involves a manual assessment of the number of mitotic cells in a histological slide prepared from the tumor and assessed by a trained pathologist. Disclosed is a method for using cumulative information from a series of expressed genes to determine tumor prognosis. This cumulative information can be used to categorize tumor samples into high mitotic states or low mitotic states using a mathematical algorithm and gene expression data derived from microarrays or quantitative-Polymerase Chain Reaction (Q-PCR) data. The specific mathematical description outlines how the algorithm assesses the most informative subset of genes from the full list of genes during the assessment of each sample.

Abstract: Described are mathematical models and method, e.g., computer-implemented methods, for predicting tumor sensitivity to radiation therapy, which can be used, e.g., for selecting a treatment for a subject who has a tumor.