Computerized Detection of Prostate Cancer in Multi-Parametric MRI

Prostate cancer is the most commonly diagnosed malignancy and the second leading cause of cancer death among men in the Netherlands. Due to the shortcomings of the current diagnostic pathway for prostate cancer, especially with respect to assessing cancer aggressiveness, alternative strategies are being investigated. Magnetic resonance imaging (MRI) has emerged as an important modality to assist and potentially replace (part of) the current diagnostic pathway. The high complexity of prostate MRI and the lack of sufficient expertise among the radiological community at large has opened the door for (semi-)automated analysis of prostate MRI by computer systems, with or without human intervention.

Transversal slide through the prosate in a T2-weighted MRI sequence.
Transversal slide through the prosate in a T2-weighted MRI sequence.

Within this project such as system was developed and evaluated. It consisted of several key components: segmentation of the prostate gland in MRI, discovering cancer-specific features, system development and evaluation. The results were reported through a number of publications which are listed below and summarized in my PhD Thesis.

Publications

G. Litjens, R. Elliott, N. Shih, M. Feldman, T. Kobus, C. Hulsbergen - van de Kaa, J. Barentsz, H. Huisman, A. Madabhushi. Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.. Radiology, 2016.
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G. Litjens, J. Barentsz, N. Karssemeijer, H. Huisman. Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI. Eur Radiol, 2015.
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G. Litjens. Computerized detection of cancer in multi-parametric prostate MRI. 2015.
PDF Project
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. Edwards, B. Maan, F. van der Heijden, S. Ghose, J. Mitra, J. Dowling, D. Barratt, H. Huisman, A. Madabhushi. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Med Image Anal, 2014.
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G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer, H. Huisman. Computer-aided detection of prostate cancer in MRI. IEEE Trans Med Imaging, 2014.
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G. Litjens, N. Karssemeijer, J. Barentsz, H. Huisman. Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging. RSNA, 2014.
Project
G. Litjens, H. Huisman, R. Elliott, N. Shih, M. Feldman, J. Futterer, J. Bomers, A. Madabhushi. Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation. in: Medical Imaging, volume 9036 of the Proceedings of the SPIE, 2014.
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G. Litjens, R. Elliott, N. Shih, M. Feldman, J. Barentsz, C. Hulsbergen - van de Kaa, I. Kovacs, H. Huisman, A. Madabhushi. Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI. in: Medical Imaging, volume 9035 of the Proceedings of the SPIE, 2014.
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G. Litjens, H. Huisman, R. Elliott, N. Shih, M. Feldman, S. Viswanath, J. Futterer, J. Bomers, A. Madabhushi. Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy. J Med Imaging, 2014.
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G. Litjens, J. Barentsz, N. Karssemeijer, H. Huisman. Initial prospective evaluation of the prostate imaging reporting and data standard (PI-RADS): Can it reduce unnecessary MR guided biopsies?. RSNA, 2013.
Project
G. Litjens, T. Hambrock, C. Hulsbergen - van de Kaa, J. Barentsz, H. Huisman. Interpatient Variation in Normal Peripheral Zone Apparent Diffusion Coefficient: Effect on the Prediction of Prostate Cancer Aggressiveness. Radiology, 2012.
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G. Litjens, J. Barentsz, N. Karssemeijer, H. Huisman. Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach. in: Medical Imaging, volume 8315 of the Proceedings of the SPIE, 2012.
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G. Litjens, N. Karssemeijer, H. Huisman. A multi-atlas approach for prostate segmentation in MRI. in: MICCAI Workshop: Prostate Cancer Imaging: The PROMISE12 Prostate Segmentation Challenge, 2012.
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G. Litjens, O. Debats, W. van de Ven, N. Karssemeijer, H. Huisman. A pattern recognition approach to zonal segmentation of the prostate on MRI. in: Med Image Comput Comput Assist Interv, volume 7511 of the Lect Notes Comput Sci, 2012.
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G. Litjens, J. Barentsz, N. Karssemeijer, H. Huisman. Computerized characterization of central gland lesions using texture and relaxation features from T2-weighted prostate MRI. RSNA, 2012.
Project
G. Litjens, P. Vos, J. Barentsz, N. Karssemeijer, H. Huisman. Automatic Computer Aided Detection of Abnormalities in Multi-Parametric Prostate MRI. in: Medical Imaging, volume 7963 of the Proceedings of the SPIE, 2011.
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G. Litjens, J. Barentsz, N. Karssemeijer, H. Huisman. Zone-specific Automatic Computer-aided Detection of Prostate Cancer in MRI. RSNA, 2011.
Project