Computer-aided detection of prostate cancer
Prostate cancer is the second most common cancer in men, with 10000 new cases and 2500 deaths every year in the Netherlands (Cancer Society). These numbers increase every year due to the ageing of the general populace.
Recently, it has been shown that a screening program for prostate cancer using the prostate specific antigen (PSA) blood test and transrectal ultrasound (TRUS) biopsy could reduce mortality up to 30%. However, this mortality reduction would lead to a massive amount of over-treatment in men, to save one life 1410 men would have to screened and 48 men would have to get treatment for their cancer.
In addition, most of these 48 men do not die due to this disease because prostate cancer in general grows slowly. Thus it is very important in diagnosis to separate clinical significant cancer from indolent cancer.
Over the past decade prostate MRI has shown to be a great candidate for prostate cancer detection. Not only does it have a higher detection accuracy than the traditional PSA/TRUS combination, it gives the radiologist the possibility to quickly assess the extent and aggressiveness of the cancer. Using MRI it is also possible to detect metastases in the bones or in the lymph nodes. Prostate MRI could also be used in a screening situation, for example as a replacement for the TRUS biopsy, which is an invasive, painful procedure.
A general prostate MRI exam consists of multiple MR images to give both anatomical and functional information about the prostate tissue. The three typically acquired image types are T2-weighted, diffusion-weighted and dynamic contrast enhanced images. High-resolution T2-weighted images are used to assess the anatomical structure of the prostate tissue. The diffusion-weighted images provide an indication of the cellular structure of the prostate. Dynamic contrast enhanced images can be used to determine attributes like blood vessel surface area and permeability in prostate tissue.
One of the remaining issues with prostate MRI is that it requires substantial experience to detect and categorize prostate cancer. The availability of such experienced radiologists is scarce. In addition, even for an experienced radiologist it is difficult to extract all the information from the data. To give an indication, in a typical exam 3 T2-weighted images are acquired, in addition to 5 diffusion-weighted images and 35 dynamic-contrast enhanced images.
Development of a computer-aided detection system for prostate cancer
Computer-aided detection of prostate cancer can perhaps provide a solution. Computer algorithms allow us to combine the enormous amount of images into a much smaller amount of images with a high information content. For example, using pharmacokinetic analysis we can reduce the 35 dynamic contrast enhanced images to 2 pharmacokinetic parameter maps.
These images can be used by the radiologist directly, but using machine learning and pattern recognition techniques computer-aided detection of suspicious regions is also possible. This creates a synthetic 'second reader', allowing every image set to be read by two readers, the radiologist and the computer. In the end this will help reduce the workload for the radiologists while increasing their detection performance.
Computer-aided detection of prostate cancer in clinical practice
To make our algorithms available to the clinic a prostate workstation was created. This workstation is supposed to streamline the reading and reporting of prostate MRI, while also providing advanced features like pharmacokinetic analysis and computer-aided detection in a user-friendly way. The first iteration of our workstation is in current clinical practice and is used on a daily basis to report prostate MR. Currently work is well underway for the next version.
The close interaction with the clinic provides us with valuable feedback and support to improve our algorithms. This will result in better tools, which allow the radiologists to work more efficiently and more accurately. In the end this will result in an improvement in patient care.
- Exploring the clinical value of novel high resolution anatomic and molecular / functional MR imaging in prostate cancer funded by the Dutch Cancer Foundation.
- G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer and H. Huisman. "Computer-aided detection of prostate cancer in MRI", IEEE Trans Med Imag 2014;33:1083-1092.
- G. Litjens, R. Toth, W. van de Ven, C. Hoeks, S. Kerkstra, B. van Ginneken, G. Vincent, G. Guillard, N. Birbeck, J. Zhang, R. Strand, F. Malmberg, Y. Ou, C. Davatzikos, M. Kirschner, F. Jung, J. Yuan, W. Qiu, Q. Gao, P.E. Edwards, B. Maan, F. van der Heijden, S. Ghose, J. Mitra, J. Dowling, D. Barratt, H. Huisman and A. Madabhushi. "Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge", Medical Image Analysis 2014;18:359-373. Abstract/PDF DOI PMID Citations found: 6
- G. Litjens, P. Vos, J. Barentsz, N. Karssemeijer and H. Huisman. "Automatic Computer Aided Detection of Abnormalities in Multi-Parametric Prostate MRI", in: Medical Imaging, volume 7963 of Proceedings of the SPIE, 2011. Abstract/PDF DOI Citations found: 6
- P.C. Vos, T. Hambrock, J.O. Barentsz and H.J. Huisman. "Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI", Physics in Medicine and Biology 2010;55:1719-1734. Abstract/PDF DOI PMID Citations found: 35