This review utilizes prostate cancer (CaP) as a case study to assess the application of quantitative histomorphometry to characterize the aggressive phenotype and also to make predictions of outcome like recurrence, metastasis and survival. Dr. Robert Veltri describes the use of a microspectrophotometry microscope and novel software to capture nuclear morphometry of Feulgen (DNA) stained features and successfully identify indolent and aggressive CaP as well as predict outcomes such as biochemical recurrence, metastasis and survival. His research also indicates that quantitative nuclear morphometry by this method indicates a field effect nearby the can that also has predictive value. However, the original technology was bottle-necked by the fact that it could not be extended to whole slide images. Subsequently, the initiation of a collaboration with Dr. Anant Madabhushi who has developed high throughput quantitative image and histomorphometric tools that are amenable to work on whole slide digitized images, has allowed for confirmation of Dr. Veltri’s prior and published observations that nuclear size, shape and texture as well as the glandular structure or architecture of prostate cancer are critical in predicting disease aggressiveness. Dr. Madabhushi’s algorithms take advantage of advanced machine vision imaging images for diagnosis and prognosis. Additionally Dr. Madabhushi’s group has also been developing machine learning tools to combine image based and molecular measurements for creating unified predictors of disease aggressiveness and patient outcome. It is hoped that the additional development and validation of these tools will set the stage for creation of decision tools to aid the pathologist to predict severe outcomes early so that appropriate interventions can be made by the urologist and patient.
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)