H M Reynolds1, S Williams2, A Zhang3, R Chakravorty3, D Rawlinson3, C S Ong4, M Esteva3, C Mitchell5, B Parameswaran6, M Finnegan7, G Liney8, A Haworth1. 1. Department of Physical Sciences, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia and Sir Peter MacCallum Department of Oncology, University of Melbourne, East Melbourne, Victoria 3002, Australia. 2. Department of Pathology, University of Melbourne, Parkville, Victoria 3010, Australia and Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia. 3. Electrical and Electronic Engineering, University of Melbourne, Parkville, Victoria 3010, Australia. 4. Electrical and Electronic Engineering, University of Melbourne, Parkville, Victoria 3010, Australia; Machine Learning Research Group, National ICT Australia, Canberra 2601, Australia; and Research School of Computer Science, Australian National University, Canberra 2601, Australia. 5. Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia. 6. Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia. 7. Department of Physical Sciences, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia. 8. Ingham Institute for Applied Medical Research, Liverpool Hospital, New South Wales 2170, Australia.
Abstract
PURPOSE: Focal therapy has been proposed as an alternative method to whole-gland treatment for prostate cancer when aiming to reduce treatment side effects. The authors recently validated a radiobiological model which takes into account tumor location and tumor characteristics including tumor cell density, Gleason score, and hypoxia in order to plan optimal dose distributions for focal therapy. The authors propose that this model can be informed using multiparametric MRI (mpMRI) and in this study present a registration framework developed to map prostate mpMRI and histology data, where histology will provide the "ground truth" data regarding tumor location and biology. The authors aim to apply this framework to a growing database to develop a prostate biological atlas which will enable MRI based planning for prostate focal therapy treatment. METHODS: Six patients scheduled for routine radical prostatectomy were used in this proof-of-concept study. Each patient underwent mpMRI scanning prior to surgery, after which the excised prostate specimen was formalin fixed and mounted in agarose gel in a custom designed sectioning box. T2-weighted MRI of the specimen in the sectioning box was acquired, after which 5 mm sections of the prostate were cut and histology sections were microtomed. A number of image processing and registration steps were used to register histology images with ex vivo MRI and deformable image registration (DIR) was applied to 3D T2w images to align the in vivo and ex vivo MRI data. Dice coefficient metrics and corresponding feature points from two independent annotators were selected in order to assess the DIR accuracy. RESULTS: Images from all six patients were registered, providing histology and in vivo MRI in the ex vivo MRI frame of reference for each patient. Results demonstrated that their DIR methodology to register in vivo and ex vivo 3D T2w MRI improved accuracy in comparison with an initial manual alignment for prostates containing features which were readily visible on MRI. The average estimated uncertainty between in vivo MRI and histology was 3.3 mm, which included an average error of 3.1 mm between in vivo and ex vivo MRI after applying DIR. The mean dice coefficient for the prostate contour between in vivo and ex vivo MRI increased from 0.83 before DIR to 0.93 after DIR. CONCLUSIONS: The authors have developed a registration framework for mapping in vivo MRI data of the prostate with histology by implementing a number of processing steps and ex vivo MRI of the prostate specimen. Validation of DIR was challenging, particularly in prostates with few or mostly linear rather than spherical shaped features. Refinement of their MR imaging protocols to improve the data quality is currently underway which may improve registration accuracy. Additional mpMRI sequences will be registered within this framework to quantify prostate tumor location and biology.
PURPOSE: Focal therapy has been proposed as an alternative method to whole-gland treatment for prostate cancer when aiming to reduce treatment side effects. The authors recently validated a radiobiological model which takes into account tumor location and tumor characteristics including tumor cell density, Gleason score, and hypoxia in order to plan optimal dose distributions for focal therapy. The authors propose that this model can be informed using multiparametric MRI (mpMRI) and in this study present a registration framework developed to map prostate mpMRI and histology data, where histology will provide the "ground truth" data regarding tumor location and biology. The authors aim to apply this framework to a growing database to develop a prostate biological atlas which will enable MRI based planning for prostate focal therapy treatment. METHODS: Six patients scheduled for routine radical prostatectomy were used in this proof-of-concept study. Each patient underwent mpMRI scanning prior to surgery, after which the excised prostate specimen was formalin fixed and mounted in agarose gel in a custom designed sectioning box. T2-weighted MRI of the specimen in the sectioning box was acquired, after which 5 mm sections of the prostate were cut and histology sections were microtomed. A number of image processing and registration steps were used to register histology images with ex vivo MRI and deformable image registration (DIR) was applied to 3D T2w images to align the in vivo and ex vivo MRI data. Dice coefficient metrics and corresponding feature points from two independent annotators were selected in order to assess the DIR accuracy. RESULTS: Images from all six patients were registered, providing histology and in vivo MRI in the ex vivo MRI frame of reference for each patient. Results demonstrated that their DIR methodology to register in vivo and ex vivo 3D T2w MRI improved accuracy in comparison with an initial manual alignment for prostates containing features which were readily visible on MRI. The average estimated uncertainty between in vivo MRI and histology was 3.3 mm, which included an average error of 3.1 mm between in vivo and ex vivo MRI after applying DIR. The mean dice coefficient for the prostate contour between in vivo and ex vivo MRI increased from 0.83 before DIR to 0.93 after DIR. CONCLUSIONS: The authors have developed a registration framework for mapping in vivo MRI data of the prostate with histology by implementing a number of processing steps and ex vivo MRI of the prostate specimen. Validation of DIR was challenging, particularly in prostates with few or mostly linear rather than spherical shaped features. Refinement of their MR imaging protocols to improve the data quality is currently underway which may improve registration accuracy. Additional mpMRI sequences will be registered within this framework to quantify prostate tumor location and biology.
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