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Proceedings Paper

Accuracy and variability of tumor burden measurement on multi-parametric MRI
Author(s): Mehrnoush Salarian; Eli Gibson; Maysam Shahedi; Mena Gaed; José A. Gómez; Madeleine Moussa; Cesare Romagnoli; Derek W. Cool; Matthew Bastian-Jordan; Joseph L. Chin; Stephen Pautler; Glenn S. Bauman; Aaron D. Ward
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Paper Abstract

Measurement of prostate tumour volume can inform prognosis and treatment selection, including an assessment of the suitability and feasibility of focal therapy, which can potentially spare patients the deleterious side effects of radical treatment. Prostate biopsy is the clinical standard for diagnosis but provides limited information regarding tumour volume due to sparse tissue sampling. A non-invasive means for accurate determination of tumour burden could be of clinical value and an important step toward reduction of overtreatment. Multi-parametric magnetic resonance imaging (MPMRI) is showing promise for prostate cancer diagnosis. However, the accuracy and inter-observer variability of prostate tumour volume estimation based on separate expert contouring of T2-weighted (T2W), dynamic contrastenhanced (DCE), and diffusion-weighted (DW) MRI sequences acquired using an endorectal coil at 3T is currently unknown. We investigated this question using a histologic reference standard based on a highly accurate MPMRIhistology image registration and a smooth interpolation of planimetric tumour measurements on histology. Our results showed that prostate tumour volumes estimated based on MPMRI consistently overestimated histological reference tumour volumes. The variability of tumour volume estimates across the different pulse sequences exceeded interobserver variability within any sequence. Tumour volume estimates on DCE MRI provided the lowest inter-observer variability and the highest correlation with histology tumour volumes, whereas the apparent diffusion coefficient (ADC) maps provided the lowest volume estimation error. If validated on a larger data set, the observed correlations could support the development of automated prostate tumour volume segmentation algorithms as well as correction schemes for tumour burden estimation on MPMRI.

Paper Details

Date Published: 20 March 2014
PDF: 6 pages
Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410I (20 March 2014); doi: 10.1117/12.2043716
Show Author Affiliations
Mehrnoush Salarian, The Univ. of Western Ontario (Canada)
Eli Gibson, The Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)
Maysam Shahedi, The Univ. of Western Ontario (Canada)
Mena Gaed, The Univ. of Western Ontario (Canada)
José A. Gómez, The Univ. of Western Ontario (Canada)
Madeleine Moussa, The Univ. of Western Ontario (Canada)
Cesare Romagnoli, The Univ. of Western Ontario (Canada)
Derek W. Cool, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Matthew Bastian-Jordan, The Univ. of Western Ontario (Canada)
Joseph L. Chin, The Univ. of Western Ontario (Canada)
Stephen Pautler, The Univ. of Western Ontario (Canada)
Glenn S. Bauman, The Univ. of Western Ontario (Canada)
Aaron D. Ward, The Univ. of Western Ontario (Canada)

Published in SPIE Proceedings Vol. 9041:
Medical Imaging 2014: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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