Mehdi Moradi1, S Sara Mahdavi2, Guy Nir1, Omid Mohareri1, Anthony Koupparis3, Louis-Olivier Gagnon4, Ladan Fazli5, Rowan G Casey6, Joseph Ischia7, Edward C Jones4, S Larry Goldenberg8, Septimiu E Salcudean1. 1. University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada. 2. British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4E6, Canada. 3. Bristol Urological Institute, Brunel Building, Southmead Hospital, Bristol BS10 5NB, UK. 4. Vancouver General Hospital, Vancouver, British Columbia V5Z 1M9, Canada. 5. Vancouver Prostate Center, Vancouver, British Columbia V6H 3Z6, Canada. 6. Consultant Urologist, Essex Cancer Centre, Colchester University NHS Foundation Trust, Essex, CO62QL, UK. 7. University of Melbourne, Melbourne, Victoria 3010, Australia. 8. Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada.
Abstract
PURPOSE: Ultrasound-based solutions for diagnosis and prognosis of prostate cancer are highly desirable. The authors have devised a method for detecting prostate cancer using a vibroelastography (VE) system developed in our group and a tissue classification approach based on texture analysis of VE images. METHODS: The VE method applies wide-band mechanical vibrations to the tissue. Here, the authors report on the use of this system for cancer detection and show that the texture of VE images characterized by the first and the second order statistics of the pixel intensities form a promising set of features for tissue typing to detect prostate cancer. The system was used to image patients prior to radical surgery. The removed specimens were sectioned and studied by an experienced histopathologist. The authors registered the whole-mount histology sections to the ultrasound images using an automatic registration algorithm. This enabled the quantitative evaluation of the performance of the authors' imaging method in cancer detection in an unbiased manner. The authors used support vector machine (SVM) classification to measure the cancer detection performance of the VE method. Regions of tissue of size 5 × 5 mm, labeled as cancer and noncancer based on automatic registration to histology slides, were classified using SVM. RESULTS: The authors report an area under ROC of 0.81 ± 0.10 in cancer detection on 1066 tissue regions from 203 images. All cancer tumors in all zones were included in this analysis and were classified versus the noncancer tissue in the peripheral zone. This outcome was obtained in leave-one-patient-out validation. CONCLUSIONS: The developed 3D prostate vibroelastography system and the proposed multiparametric approach based on statistical texture parameters from the VE images result in a promising cancer detection method.
PURPOSE: Ultrasound-based solutions for diagnosis and prognosis of prostate cancer are highly desirable. The authors have devised a method for detecting prostate cancer using a vibroelastography (VE) system developed in our group and a tissue classification approach based on texture analysis of VE images. METHODS: The VE method applies wide-band mechanical vibrations to the tissue. Here, the authors report on the use of this system for cancer detection and show that the texture of VE images characterized by the first and the second order statistics of the pixel intensities form a promising set of features for tissue typing to detect prostate cancer. The system was used to image patients prior to radical surgery. The removed specimens were sectioned and studied by an experienced histopathologist. The authors registered the whole-mount histology sections to the ultrasound images using an automatic registration algorithm. This enabled the quantitative evaluation of the performance of the authors' imaging method in cancer detection in an unbiased manner. The authors used support vector machine (SVM) classification to measure the cancer detection performance of the VE method. Regions of tissue of size 5 × 5 mm, labeled as cancer and noncancer based on automatic registration to histology slides, were classified using SVM. RESULTS: The authors report an area under ROC of 0.81 ± 0.10 in cancer detection on 1066 tissue regions from 203 images. All cancer tumors in all zones were included in this analysis and were classified versus the noncancer tissue in the peripheral zone. This outcome was obtained in leave-one-patient-out validation. CONCLUSIONS: The developed 3D prostate vibroelastography system and the proposed multiparametric approach based on statistical texture parameters from the VE images result in a promising cancer detection method.
Authors: Mark L Palmeri; Tyler J Glass; Zachary A Miller; Stephen J Rosenzweig; Andrew Buck; Thomas J Polascik; Rajan T Gupta; Alison F Brown; John Madden; Kathryn R Nightingale Journal: Ultrasound Med Biol Date: 2016-03-03 Impact factor: 2.998