The Prostatome – a Novel Prostate Atlas Combining Anatomic and Disease Pathology Data Founded

Scientists have succeeded in fusing prostate cancer pathology data with anatomical magnetic resonance imaging (MRI) data to create the foundation of a first-of-its-kind prostate atlas for prostate cancer.

A team of scientists and clinicians, led by Anant Madabhushi, professor of biomedical engineering and director of the Center of Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University (Cleveland, OH, USA), developed a novel framework, the anatomically constrained registration (AnCoR) scheme, and applied it to perform the fusion of initial data sets for creating the new prostate cancer (PrCa) "prostatome" atlas. The methods developed in the study can now be applied with a larger sample size data set to create a more fully developed atlas, which will allow for highly accurate identification of the MRI signatures associated with PrCa extent and aggressiveness.


The atlas was constructed and validated on 80 patients from two different sites—Boston Medical Center and Beth Israel Deaconess Medical Center. The goal of the study was to integrate pathology and MRI data from multiple different PrCa patients who had undergone surgery involving complete removal of the prostate (radical prostatectomy). All patients had an MRI performed before surgery. The methods developed in this project were used to fuse the MRI scans obtained before surgery with histology data of the surgically excised specimens. This allowed for mapping of cancer extent (determined by a pathologist) in the surgically excised specimen onto the preoperative imaging, enabling construction of a novel PrCa MRI atlas that incorporates PrCa specimen information.

Prof. Madabhushi commented, “The prostatome atlas could allow for integrating imaging data from multiple different patients studies into one unified representation and thereby allow for guiding biopsies, specific treatment targeting, and also serve as an educational tool for medical students and residents.” Additionally, the AnCoR framework could allow for incorporation of complementary imaging and molecular data, thereby enabling their careful correlation for population based radio-omics.

The study, by Rusu M. et al., was described in the July, 2014, issue of the journal Medical Physics.

Related Links:
Case Western Reserve University
Center of Computational Imaging and Personalized Diagnostics