A digital pathology method for analyzing a cancer biopsy tissue sample of a patient in accordance with a whole-tumor workflow and a hot spot workflow comprising: a) acquiring multiple images (5', 6', 7', 8') from histopathological tissue slides (1', 2', 3') of the biopsy tissue sample; b) selecting a field of view in one of the images, wherein the field of view comprises a whole tumor corresponding to the whole-tumor workflow or a hot spot corresponding to the hot spot workflow; c) detecting a boundary of the whole tumor or the hot spot in one of the images by execution of an image processing algorithm on the one of the images or manually entering an annotation being indicative of the boundary of the whole tumor or the hot spot for the one of the images; d) identifying the boundaries (11', 12', 13') of the whole tumor or the hot spot in each one of the other images (6', 7', 8') using the detected boundary, such as by inter-marker registration; e) calculating a slide score (A, B, C, D) for each one of the images, wherein the calculation of the slide score for one of the images is performed using only the image portion of the one of the images within the boundary of the whole tumor or the hot spot of that image; f) entering the slide scores into a statistical model that models a cancer reoccurrence risk; g) retrieving, from memory, parameter values for the statistical model based upon the field of view comprising either the whole tumor or the hot spot; h) calculating a risk stratification score by the statistical model using the slide scores; and i) generating a signal by thresholding the risk stratification score, wherein thresholding the risk stratification score comprises determining a cutoff point of the risk stratification score, wherein determining the cutoff point of the risk stratification score comprises optimally stratifying training patient sample sets into low and high risk groups by maximizing a separation between Kaplan-Meier survival probability estimate curves between the low and high risk groups, and wherein the statistical model is generated based on performing steps a) to f) for each cancer biopsy tissue sample of each patient of a cohort of cancer patients, wherein step f) is performed based upon the field of view comprising either the whole tumor or the hot spot, and wherein step f) further comprises entering survival data of said each patient, fitting the statistical model using the entered scores and respective survival data to provide the parameter values for the statistical model based upon the field of view comprising either the whole tumor or the hot spot, and storing the parameter values in the memory. a) acquiring multiple images (5', 6', 7', 8') from histopathological tissue slides (1', 2', 3') of the biopsy tissue sample; b) selecting a field of view in one of the images, wherein the field of view comprises a whole tumor corresponding to the whole-tumor workflow or a hot spot corresponding to the hot spot workflow; c) detecting a boundary of the whole tumor or the hot spot in one of the images by execution of an image processing algorithm on the one of the images or manually entering an annotation being indicative of the boundary of the whole tumor or the hot spot for the one of the images; d) identifying the boundaries (11', 12', 13') of the whole tumor or the hot spot in each one of the other images (6', 7', 8') using the detected boundary, such as by inter-marker registration; e) calculating a slide score (A, B, C, D) for each one of the images, wherein the calculation of the slide score for one of the images is performed using only the image portion of the one of the images within the boundary of the whole tumor or the hot spot of that image; f) entering the slide scores into a statistical model that models a cancer reoccurrence risk; g) retrieving, from memory, parameter values for the statistical model based upon the field of view comprising either the whole tumor or the hot spot; h) calculating a risk stratification score by the statistical model using the slide scores; and i) generating a signal by thresholding the risk stratification score, wherein thresholding the risk stratification score comprises determining a cutoff point of the risk stratification score, wherein determining the cutoff point of the risk stratification score comprises optimally stratifying training patient sample sets into low and high risk groups by maximizing a separation between Kaplan-Meier survival probability estimate curves between the low and high risk groups, and wherein