Patents by Inventor Grisselle CENTENO

Grisselle CENTENO has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • SUPERVISED LEARNING METHODS FOR THE PREDICTION OF TUMOR RADIOSENSITIVITY TO PREOPERATIVE RADIOCHEMOTHERAPY
    Publication number: 20220002807
    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.
    Type: Application
    Filed: June 8, 2021
    Publication date: January 6, 2022
    Inventors: Florentino A. Rico, Grisselle Centeno, Ludwig Kuznia, Steven A. Eschrich, Javier F. Torres-Roca
  • SUPERVISED LEARNING METHODS FOR THE PREDICTION OF TUMOR RADIOSENSITIVITY TO PREOPERATIVE RADIOCHEMOTHERAPY
    Publication number: 20190367989
    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.
    Type: Application
    Filed: July 16, 2019
    Publication date: December 5, 2019
    Inventors: Florentino A. Rico, Grisselle Centeno, Ludwig Kuznia, Steven A. Eschrich, Javier F. Torres-Roca
  • SUPERVISED LEARNING METHODS FOR THE PREDICTION OF TUMOR RADIOSENSITIVITY TO PREOPERATIVE RADIOCHEMOTHERAPY
    Publication number: 20170283873
    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.
    Type: Application
    Filed: September 11, 2015
    Publication date: October 5, 2017
    Inventors: Florentino A. RICO, Grisselle CENTENO, Ludwig KUZNIA, Steven A. ESCHRICH, Javier F. TORRES-ROCA