Systems and methods for patient-specific imaging and modeling of drug delivery

Abstract

Systems and methods are disclosed for providing personalized chemotherapy and drug delivery using computational fluid dynamics and medical imaging with machine learning from a vascular anatomical model. One method includes receiving a patient-specific anatomical model of at least one vessel of the patient and a target tissue where a drug is to be supplied; receiving patient-specific information defining the administration of a drug; deriving patient-specific data from the patient specific anatomical model and/or the patient; determining one or more blood flow characteristics in a vascular network leading to the one or more locations in the target tissue where drug delivery data will be estimated or measured, using the patient-specific anatomical model and the patient-specific data; and computing drug delivery data at the one or more locations in the target tissue using transportation, spatial, and/or temporal distribution of the drug particles.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method of estimating drug delivery at a target tissue, the method comprising:
 receiving one or more patient-specific models of (1) a target tissue where a drug is delivered, and (2) of a vascular network supplying blood to the target tissue from each of a plurality of individuals with known drug delivery data at one or more locations of the target tissue;   receiving information on drug administration for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received;   deriving one or more patient specific data from the patient-specific models from whom patient-specific models were received, the patient-specific data including one or more physiological conditions;   receiving drug delivery data for one or more locations of the target tissue for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received;   calculating feature vectors including information regarding the one or more locations on the target tissue with known drug delivery data, information on drug administration and the patient-specific data, for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received;   associating the feature vectors with the received drug delivery data at one or more locations of the target tissue, for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received; and   training a system that can predict drug delivery data at one or more locations on a patient-specific model of a target tissue and of a vascular network supplying blood to the target tissue, from one or more patient-specific data and information on drug administration, using the associated feature vectors.   
     
     
         22 . The computer-implemented method of  claim 21 , further comprising:
 receiving a patient-specific model of the target tissue where drug is delivered and of the vascular network supplying blood to the target tissue of a patient seeking analysis;   receiving information on drug administration in the patient seeking analysis;   receiving data on one or more locations in the target tissue of the patient seeking analysis where drug delivery data will be estimated or measured;   deriving one or more patient specific data from the patient-specific model and/or the patient seeking analysis;   forming feature vectors comprising of information regarding the one or more locations on the target tissue where drug delivery data will be estimated or measured, information on drug administration and the patient-specific data;   applying the trained system to estimate the drug delivery data at one or more locations on the target tissue of the patient-specific model of the patient seeking analysis, using the feature vectors; and   outputting the estimated or measured drug delivery data to an electronic storage medium or display.   
     
     
         23 . The computer-implemented method of  claim 22 , wherein the estimated or measured drug delivery data within the electronic storage medium includes data extracted from one or more scanning modalities, including, but not limited to, MR, FDG-PET, stress echo/MRI contractile reserve, multidetector CT, dual energy CT, μCT, or μMR. 
     
     
         24 . The computer-implemented method of  claim 21 , wherein training a system that can predict drug delivery data includes using, one or more of: a support vector machine (SVM), a multi-layer perceptron (MLP), a multivariate regression (MVR), deep learning, random forests, k-nearest neighbors, Bayes networks, and a weighted linear or logistic regression. 
     
     
         25 . The computer-implemented method of  claim 21 , further comprising:
 developing a patient-specific fluid dynamics model of blood flow in the patient-specific models, using patient-specific data, for each of the plurality of individuals with known drug delivery data at one or more locations of the target tissue;   calculating a transportation, spatial, and/or temporal distribution of drug particles in the fluid dynamics model of blood flow, using the information on drug administration, for each of a plurality of individuals with known drug delivery data at one or more locations of the target tissue; and   incorporating information related to a transportation, spatial, and/or temporal distribution of the drug particles into the feature vectors, for each of a plurality of individuals with known drug delivery data at one or more locations of the target tissue.   
     
     
         26 . The computer-implemented method of  claim 21 , wherein the information on drug administration includes drug administration amount, drug concentration, administration location, administration frequency, route of drug administration, administration time, type of therapy, or a combination thereof. 
     
     
         27 . The computer-implemented method of  claim 21 , wherein the one or more patient specific data includes: a vascular anatomical image characteristic; a target tissue image characteristic; an estimated perfusion territory in the target tissue; an estimated blood supply to the target tissue; estimated blood flow data; a patient characteristic; a disease burden characteristic; an electromechanical measurement; the transportation, spatial, and/or temporal distribution of drug particles in the vascular network; or a combination thereof. 
     
     
         28 . The computer-implemented method of  claim 21 , wherein the one or more drug delivery data includes, one or more of:
 an estimate of an amount of drug delivered to the one or more locations in the target tissue;   an estimate of a concentration of drug particles delivered to the one or more locations in the target tissue;   a circulatory destination probability of drug particles released at the drug delivery location to the target tissue, the circulatory destination probability based on a ratio of the amount of drug particles reaching the target tissue with respect to a total number of released drug particles;   a transportation, spatial, and/or temporal distribution of the drug;   an estimate blood flow data; or   
       a combination thereof. 
     
     
         29 . The computer-implemented method of  claim 21 , wherein the target tissue is, one or more of:
 a tissue or organ affected with tumorous growth, including, one or more of, a brain, breast, prostate, cervix, lung, skin, colon, or stomach;   a tissue or organ affected by a stenosis within the vascular and/or a microvascular network; or   a tissue or organ affected by a thrombosis within the vascular and/or a microvascular network.   
     
     
         30 . The computer-implemented method of  claim 22 , further including:
 receiving one or more desired drug delivery data at one or more locations in the target tissue;   assessing effectiveness of drug delivery by comparing one or more actual drug delivery data at the one or more locations in the target tissue with the desired drug delivery data at the one or more locations in the target tissue; and   reconfiguring the drug administration so that one or more actual drug delivery data at the one or more locations in the target tissue matches or is within range of the desired drug delivery data at the one or more locations in the target tissue.   
     
     
         31 . The computer-implemented method of  claim 30 , wherein the reconfiguring the drug administration includes systematically adjusting one or more of a drug amount, a drug concentration, a drug administration location, a drug administration frequency, a route of drug administration, an administration time, a type of therapy, or a combination thereof, in order to increase the effectiveness of the drug delivery. 
     
     
         32 . The computer-implemented method of  claim 22 , further including:
 receiving, at different time points, one or more medical images of patients;   extracting patient specific data from the one or more medical images;   assessing effectiveness of drug delivery by comparing the patient specific data extracted from the two images; and   reconfiguring the drug administration in order to increase the effectiveness of drug delivery.   
     
     
         33 . The computer-implemented method of  claim 32 , wherein the effectiveness of drug delivery is assessed by comparing the one or more medical images and determining a degree to which a tumor or lesion has regressed. 
     
     
         34 . The computer-implemented method of  claim 32 , wherein the reconfiguring the drug administration includes systematically adjusting one or more of a drug amount, a drug concentration, a drug administration location, a drug administration frequency, a route of drug administration, an administration time, a type of therapy, or a combination thereof, in order to increase the effectiveness of the drug delivery. 
     
     
         35 . A system for estimating drug delivery at a target tissue, the system comprising:
 a data storage device storing instructions for estimating drug delivery at a target tissue; and   a processor configured to execute the instructions to perform a method including:
 receiving one or more patient-specific models of (1) a target tissue where a drug is delivered, and (2) of a vascular network supplying blood to the target tissue from each of a plurality of individuals with known drug delivery data at one or more locations of the target tissue; 
   receiving information on drug administration for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received;
 deriving one or more patient specific data from the patient-specific models from whom patient-specific models were received, the patient-specific data including one or more physiological conditions; 
   receiving drug delivery data for one or more locations of the target tissue for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received;
 calculating feature vectors including information regarding the one or more locations on the target tissue with known drug delivery data, information on drug administration and the patient-specific data, for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received; 
   associating the feature vectors with the received drug delivery data at one or more locations of the target tissue, for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received; and
 training a system that can predict drug delivery data at one or more locations on a patient-specific model of a target tissue and of a vascular network supplying blood to the target tissue, from one or more patient-specific data and information on drug administration, using the associated feature vectors. 
   
     
     
         36 . The system of  claim 35 , further comprising:
 receiving a patient-specific model of the target tissue where drug is delivered and of the vascular network supplying blood to the target tissue of a patient seeking analysis;   receiving information on drug administration in the patient seeking analysis;   receiving data on one or more locations in the target tissue of the patient seeking analysis where drug delivery data will be estimated or measured;   deriving one or more patient specific data from the patient-specific model and/or the patient seeking analysis;   forming feature vectors comprising of information regarding the one or more locations on the target tissue where drug delivery data will be estimated or measured, information on drug administration and the patient-specific data;   applying the trained system to estimate the drug delivery data at one or more locations on the target tissue of the patient-specific model of the patient seeking analysis, using the feature vectors; and   outputting the estimated drug delivery data to an electronic storage medium or a display.   
     
     
         37 . The system of  claim 35 , further comprising:
 developing a patient-specific fluid dynamics model of blood flow in the patient-specific models, using patient-specific data, for each of the plurality of individuals with known drug delivery data at one or more locations of the target tissue;   determining the transportation, spatial, and/or temporal distribution of the drug particles in the fluid dynamics model of blood flow, using the information on drug administration, for each of a plurality of individuals with known drug delivery data at one or more locations of the target tissue; and   incorporating information related to the transportation, spatial, and/or temporal distribution of the drug particles into the feature vectors, for each of a plurality of individuals with known drug delivery data at one or more locations of the target tissue.   
     
     
         38 . A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for estimating drug delivery at a target tissue, the method comprising:
 receiving one or more patient-specific models of (1) a target tissue where a drug is delivered, and (2) of a vascular network supplying blood to the target tissue from each of a plurality of individuals with known drug delivery data at one or more locations of the target tissue;   receiving information on drug administration for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received;   deriving one or more patient specific data from the patient-specific models from whom patient-specific models were received, the patient-specific data including one or more physiological conditions;   receiving drug delivery data for one or more locations of the target tissue for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received;   calculating feature vectors including information regarding the one or more locations on the target tissue with known drug delivery data, information on drug administration and the patient-specific data, for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received;   associating the feature vectors with the received drug delivery data at one or more locations of the target tissue, for each of the plurality of individuals with known drug delivery data from whom patient-specific models were received; and   training a system that can predict drug delivery data at one or more locations on a patient-specific model of a target tissue and of a vascular network supplying blood to the target tissue, from one or more patient-specific data and information on drug administration, using the associated feature vectors.   
     
     
         39 . The non-transitory computer readable medium of  claim 38 , further comprising:
 receiving a patient-specific model of the target tissue where drug is delivered and of the vascular network supplying blood to the target tissue of a patient seeking analysis;   receiving information on drug administration in the patient seeking analysis   receiving data on one or more locations in the target tissue of the patient seeking analysis where drug delivery data will be estimated or measured;   deriving one or more patient specific data from the patient-specific model and/or the patient seeking analysis;   forming feature vectors comprising of information regarding the one or more locations on the target tissue where drug delivery data will be estimated or measured, information on drug administration and the patient-specific data;   applying the trained system to estimate the drug delivery data at one or more locations on the target tissue of the patient-specific model of the patient seeking analysis, using the feature vectors; and   outputting the estimated drug delivery data to an electronic storage medium or a display.   
     
     
         40 . The non-transitory computer readable medium of  claim 38 , further comprising
 developing a patient-specific fluid dynamics model of blood flow in the patient-specific models, using patient-specific data, for each of the plurality of individuals with known drug delivery data at one or more locations of the target tissue;   determining the transportation, spatial, and/or temporal distribution of the drug particles in the fluid dynamics model of blood flow, using the information on drug administration, for each of a plurality of individuals with known drug delivery data at one or more locations of the target tissue; and   incorporating information related to the transportation, spatial, and/or temporal distribution of the drug particles into the feature vectors, for each of a plurality of individuals with known drug delivery data at one or more locations of the target tissue.