Accuracy and variability of tumor burden measurement on multi-parametric MRI

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.

How does prostate biopsy guidance error impact pathologic cancer risk assessment?

“…By using histology image contours to define tumor targeting, this work models idealized tumor targeting, wherein boundary delineation on the planning image is exactly concordant with lesions on histopathology. Results from our simulation thus represent a best-case scenario, since lesions contoured by experts on MRI are not volumetrically concordant with true histologic lesions 13 . As histology slices are inherently 2D and oriented approximately axially, our simulations were conducted in 2D under the assumption that prostate tumor size and shape are invariant to slicing angle.…”