1. A method of providing decision support in diagnosis of a disease in a subject, the method comprising: receiving a multi-slice computed tomography dataset; extracting, with a hardware processor, a volume of interest from the multi-slice computed tomography dataset; segmenting, with the hardware processor, the multi-slice computed tomography dataset to delineate at least one lesion within the volume of interest from a background of the multi-slice computed tomography dataset; extracting, with the hardware processor, image-based features from the at least one lesion in two and a half or pseudo-three dimensions over a set of two or more slices of the multi-slice computed tomography dataset, wherein the image-based features include a numerical feature; converting, with the hardware processor, clinical information into multiple categories and from a categorical format into a numerical format of the image-based features and; combining the converted clinical information with the image-based features, wherein the combined converted clinical information and the image-based features form a feature pool; and selecting, with the hardware processor, an optimal feature subset from the feature pool, wherein the selecting including selecting both image-based features and converted clinical information based on a training dataset; creating, with the hardware processor, a classifier or a committee of classifiers using the selected optimal feature subset and training dataset; and employing the classifier or committee to provide a prediction of a diagnosis of the at least one lesion.