Image analysis method for providing information for enabling determination of a phenotype of a neoplasm in a human or animal body for enabling prognostication, comprising the steps of: receiving, by a processing unit, image data of the neoplasm; and deriving, by the processing unit, a plurality of image feature parameter values from the image data, wherein the image feature parameter values are indicative of image feature parameters that are derivable from image features that are obtainable from the image data and which are associated with the neoplasm; and deriving, by said processing unit using a signature model, one or more neoplasm signature model values associated with the neoplasm from said image feature parameter values, wherein the signature model represents a combination of predetermined image features that enable to distinguish specific phenotypes of neoplasms, and wherein said signature model includes a functional relation between said image feature parameter values or characteristic values of said image feature parameter values for deriving said neoplasm signature model values therefrom; wherein the signature model is based on the image feature parameters from a group comprising: gray-level non-uniformity, wavelet high-low-high gray-level non-uniformity, statistics energy, and shape compactness; wherein the image feature parameter gray-level non-uniformity is obtainable by quantifying a textural heterogeneity of the image data in three dimensions within a volume of the neoplasm, wherein the image feature parameter wavelet high-low-high gray-level non-uniformity is obtainable by performing a high-low-high filtered wavelet transformation on the image data in three dimensions within a volume of the neoplasm and by quantifying a textural heterogeneity of the transformed image data, wherein the image feature parameter shape compactness is representative of a degree to which the neoplasm is compact, and wherein the image feature parameter statistics energy is obtainable by determining a total energy within a volume of the neoplasm and normalizing the total energy over a voxel volume. receiving, by a processing unit, image data of the neoplasm; and deriving, by the processing unit, a plurality of image feature parameter values from the image data, wherein the image feature parameter values are indicative of image feature parameters that are derivable from image features that are obtainable from the image data and which are associated with the neoplasm; and deriving, by said processing unit using a signature model, one or more neoplasm signature model values associated with the neoplasm from said image feature parameter values, wherein the signature model represents a combination of predetermined image features that enable to distinguish specific phenotypes of neoplasms, and wherein said signature model includes a functional relation between said image feature parameter values or characteristic values of said image feature parameter values for deriving said neoplasm signature model values therefrom; wherein the signature model is based on the image feature parameters from a group comprising: gray-level non-uniformity, wavelet high-low-high gray-level non-uniformity, statistics energy, and shape compactness; wherein the image feature parameter gray-level non-uniformity is obtainable by quantifying a textural heterogeneity of the image data in three dimensions within a volume of the neoplasm, wherein the image feature parameter wavelet high-low-high gray-level non-uniformity is obtainable by performing a high-low-high filtered wavelet transformation on the image data in three dimensions within a volume of the neoplasm and by quantifying a textural heterogeneity of the transformed image data, wherein the image feature parameter shape compactness is representative of a degree to which the neoplasm is compact, and wherein the image feature parameter statistics energy is obtainable by determining a total energy within a volume of the neoplasm and normalizing the total energy over a voxel volume.