Method and System for Determining a Phenotype of a Neoplasm in a Human or Animal Body

Image analysis method according to any of the previous claims, wherein the image feature parameters further include at least one element of a group comprising:

• first-order gray level statistics obtained from image pixels or area's of the image from the image data, such as minimum intensity, maximum intensity, mean intensity, intensity range, intensity variance, intensity standard deviation, skewness, kurtosity, and entropy;
• second-order gray level statistics obtained from co-occurrence matrices of the image data, such as contrast, correlation between neighboring image areas or pixels, energy, homogeneity, inverse difference moment, sum average, sum variance, and sum entropy;
• run-length gray level statistics, short run emphasis, long run emphasis, run percentage;
• or shape and size based features, such as perimeter, cross-sectional area, major axis length, maximum diameter, and volume.

Decision support system arranged for performing an image analysis method for providing information for enabling determination of a phenotype of a neoplasm in a human or animal body for enabling prognostication, said system comprising an input connected to a processing unit for receiving by the processing unit image data of the neoplasm;

• wherein the processing unit is further arranged for deriving a plurality of image feature parameter values from the received image data, said image parameter values relating to image features associated with the neoplasm, wherein the processing unit is connected to a memory for obtaining therefrom at least one signature model comprising one or more signature selector values associated with the image features, wherein the signature selector values indicate whether the associated image features are comprised by the signature model, and wherein the processing unit is arranged for multiplying for the at least one signature model the image feature parameter values with the associated signature selector values for obtaining one or more neoplasm signature model values associated with the neoplasm;
• wherein the image feature parameter values are indicative of image feature parameters, wherein the signature model includes at least all of the image feature parameters from a group comprising:
• statistics energy, shape compactness, gray-level non-uniformity, wavelet high-low-high gray-level run-length gray-level non-uniformity.