A computer-implemented method for predicting information relating to a coronary lesion of a patient, the method comprising: receiving (554, 556, 558) imaging data and one or more of physiologic or hemodynamic information for a patient; for each point of a plurality of points in the imaging data, generating a respective feature vector based on the obtained one or more of the physiologic or hemodynamic information; operating a trained machine-learning model on the respective feature vectors, wherein: the trained machine-learning model has been trained based on (i) training feature vectors corresponding to training imaging data and one or more of physiologic or hemodynamic training information from a plurality of individuals and (ii) data regarding one or more of plaque growth, shrinkage, or onset for the plurality of individuals; the trained machine-learning model has been trained to learn associations between the training feature vectors and the plaque growth, shrinkage, and onset for the plurality of individuals; and the trained machine-learning model is configured to use the learned associations to generate a respective patient-specific estimate of a probability of one or more of plaque growth, shrinkage, or onset for each point of the plurality of points in the imaging data based on input of the respective feature vectors; and generating and displaying, using a computer processor, a patient-specific prediction regarding a progression of coronary artery disease for the patient, using the respective estimates of the probability of one or more of plaque growth, shrinkage, or onset at the plurality of points in the imaging data of the patient. receiving (554, 556, 558) imaging data and one or more of physiologic or hemodynamic information for a patient; for each point of a plurality of points in the imaging data, generating a respective feature vector based on the obtained one or more of the physiologic or hemodynamic information; operating a trained machine-learning model on the respective feature vectors, wherein: the trained machine-learning model has been trained based on (i) training feature vectors corresponding to training imaging data and one or more of physiologic or hemodynamic training information from a plurality of individuals and (ii) data regarding one or more of plaque growth, shrinkage, or onset for the plurality of individuals; the trained machine-learning model has been trained to learn associations between the training feature vectors and the plaque growth, shrinkage, and onset for the plurality of individuals; and the trained machine-learning model is configured to use the learned associations to generate a respective patient-specific estimate of a probability of one or more of plaque growth, shrinkage, or onset for each point of the plurality of points in the imaging data based on input of the respective feature vectors; and the trained machine-learning model has been trained based on (i) training feature vectors corresponding to training imaging data and one or more of physiologic or hemodynamic training information from a plurality of individuals and (ii) data regarding one or more of plaque growth, shrinkage, or onset for the plurality of individuals; the trained machine-learning model has been trained to learn associations between the training feature vectors and the plaque growth, shrinkage, and onset for the plurality of individuals; and the trained machine-learning model is configured to use the learned associations to generate a respective patient-specific estimate of a probability of one or more of plaque growth, shrinkage, or onset for each point of the plurality of points in the imaging data based on input of the respective feature vectors; and generating and displaying, using a computer processor, a patient-specific prediction regarding a progression of coronary artery disease for the patient, using the respective estimates of the probability of one or more of plaque growth, shrinkage, or onset at the plurality of points in the imaging data of the patient.