Image analysis can be used to detect spatial changes in the histopathology of pancreatic tumours

Pancreatic cancer is frequently associated with intense growth of fibrous tissue at the periphery of tumours, but the histopathological quantification of this stromal reaction has not yet been used as a prognostic factor because of the difficulty of obtaining quantitative measures using manual methods. Manual histological grading is a poor indicator of outcome in this type of cancer and there is a clinical need to establish a more sensitive indicator. Recent pancreatic tumour biology research has focused upon the stromal reaction and there is an indication that its histopathological quantification may lead to a new prognostic indicator. Histological samples from 21 cases of pancreatic carcinoma were stained using the sirius red, light-green method. Multiple images from the centre and periphery of each tumour were automatically segmented using colour cluster analysis to subdivide each image into representative colours. These were classified manually as stroma, cell cytoplasm or lumen in order to measure the area of each component in each image. Measured areas were analysed to determine whether the technique could detect spatial differences in the area of each tissue component over all samples, and within individual samples. Over all 21 cases, the area of stromal tissue at the periphery of the tumours exceeded that at the centre by an average of 10.0 percentage points (P < 0.001). Within individual tumours, the algorithm was able to detect significantly more stroma (P < 0.05) at the periphery than the centre in 11 cases, whilst none of the remaining cases had significantly more stromal tissue at the centre than the periphery. The results demonstrate that semi-automated analysis can be used to detect spatial differences in the area of fibrous tissue in routinely stained sections of pancreatic cancer.