Multifeature prostate cancer diagnosis and Gleason grading of histological images.

We present a study of image features for cancer diagnosis and Gleason grading of the histological images of prostate. In diagnosis, the tissue image is classified into the tumor and nontumor classes. In Gleason grading, which characterizes tumor aggressiveness, the image is classified as containing a low- or high-grade tumor.

Improved prediction of prostate cancer recurrence through systems pathology.

We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type-specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence.

Technology insight: will systems pathology replace the pathologist?

By using systems pathology, it might be possible to provide a predictive, personalized therapeutic recommendation for patients with prostate cancer. Systems pathology integrates quantitative data and information from many sources to generate a reliable prediction of the expected natural course of the disease and response to different therapeutic options. In other words, through the integration of relatively large data sets and the use of knowledge engineering, systems pathology aims at predicting the future behavior of tumors and their interaction with the host.