A computer-implemented method of reporting coronary plaque vulnerability from patient-specific anatomic image data, the method comprising: acquiring anatomical image data of at least part of the patient's vascular system; performing, using a processor, an analysis including image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, or a structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating a first cardiac risk prediction indicative of a risk of rupture of one or more regions of plaque present in the patient's vascular system and/or a risk of a myocardial infarction event, based on results of said analysis of the anatomical image data; modifying said analysis of the anatomical image data based on a proposed treatment and making a new prediction of said cardiac risk based on the modified analysis; and reporting, using the processor, an effect of the treatment on the first cardiac risk prediction calculated from the coronary plaque vulnerability, based on the new prediction, wherein: predicting the cardiac risk includes: generating a patient-specific geometric model of said at least part of the patient's vascular system, extracting geometrical features, image features, hemodynamic features, and/or biomechanical features from the model and creating feature vectors for points in the geometric model, and performing, using a processor, calculations of the cardiac risk by applying a trained machine learning algorithm to the feature vectors. acquiring anatomical image data of at least part of the patient's vascular system; performing, using a processor, an analysis including image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, or a structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating a first cardiac risk prediction indicative of a risk of rupture of one or more regions of plaque present in the patient's vascular system and/or a risk of a myocardial infarction event, based on results of said analysis of the anatomical image data; modifying said analysis of the anatomical image data based on a proposed treatment and making a new prediction of said cardiac risk based on the modified analysis; and reporting, using the processor, an effect of the treatment on the first cardiac risk prediction calculated from the coronary plaque vulnerability, based on the new prediction, wherein: predicting the cardiac risk includes: generating a patient-specific geometric model of said at least part of the patient's vascular system, extracting geometrical features, image features, hemodynamic features, and/or biomechanical features from the model and creating feature vectors for points in the geometric model, and performing, using a processor, calculations of the cardiac risk by applying a trained machine learning algorithm to the feature vectors. generating a patient-specific geometric model of said at least part of the patient's vascular system, extracting geometrical features, image features, hemodynamic features, and/or biomechanical features from the model and creating feature vectors for points in the geometric model, and performing, using a processor, calculations of the cardiac risk by applying a trained machine learning algorithm to the feature vectors. <EOS>