A computer-implemented method for non-invasively classifying a tumorous modification of a tissue into one of at least two classes, wherein each class refers to a different stage of the tumorous modification, wherein the method comprises the steps of: a) receiving raw magnetic resonance imaging (MRI) data that has been recorded by applying at least one diffusion weighted imaging (DWI) sequence using three to nine different b-values to a tissue being suspicious to a tumorous modification without application of a contrast agent; b) extracting at least two quantification scheme parameters from the raw MRI data by using two to four quantification schemes, wherein each quantification scheme is selected from 'diffusional kurtosis imaging' (DKI), "traditional monoexponential model', 'intravoxel incoherent motions' (IVIM), or 'fractional order calculus' (FROC), wherein a fitting procedure is applied for quantitatively determining the at least two quantification scheme parameters from the applied quantification schemes, wherein each of the quantification scheme parameters is related to a microstructural property of the tissue; c) applying a weight to each quantification scheme parameter, wherein the weight is dependent on a kind of the tissue and on the quantification scheme, whereby a set of weighted quantification scheme parameters is obtained; d) determining a scoring value by combining the weighted quantification scheme parameters within the set, wherein each of the weighted quantification scheme parameters is used only once for determining the scoring value such that only a single statistical parameter related to or applied to a particular quantification scheme parameter is included in determining the scoring value; and e) classifying the tumorous modification of the tissue into one of at least two classes according to the scoring value. a) receiving raw magnetic resonance imaging (MRI) data that has been recorded by applying at least one diffusion weighted imaging (DWI) sequence using three to nine different b-values to a tissue being suspicious to a tumorous modification without application of a contrast agent; b) extracting at least two quantification scheme parameters from the raw MRI data by using two to four quantification schemes, wherein each quantification scheme is selected from 'diffusional kurtosis imaging' (DKI), "traditional monoexponential model', 'intravoxel incoherent motions' (IVIM), or 'fractional order calculus' (FROC), wherein a fitting procedure is applied for quantitatively determining the at least two quantification scheme parameters from the applied quantification schemes, wherein each of the quantification scheme parameters is related to a microstructural property of the tissue; c) applying a weight to each quantification scheme parameter, wherein the weight is dependent on a kind of the tissue and on the quantification scheme, whereby a set of weighted quantification scheme parameters is obtained; d) determining a scoring value by combining the weighted quantification scheme parameters within the set, wherein each of the weighted quantification scheme parameters is used only once for determining the scoring value such that only a single statistical parameter related to or applied to a particular quantification scheme parameter is included in determining the scoring value; and e) classifying the tumorous modification of the tissue into one of at least two classes according to the scoring value. <EOS>