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Comput Biol Med. 2018 Mar 1;94:65-73. doi: 10.1016/j.compbiomed.2018.01.003. Epub 2018 Jan 31.

Pilot study for supervised target detection applied to spatially registered multiparametric MRI in order to non-invasively score prostate cancer.

Author information

1
OncoScore, Garrett Park, MD 20896, USA; University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: mayerru@yahoo.com.
2
University of Maryland, Baltimore, MD 21201, USA. Electronic address: charles.simone@umm.edu.
3
WRNMMC, Bethesda, MD 20889, USA. Electronic address: wkjskinner@gmail.com.
4
NCI, Bethesda, MD 20892, USA. Electronic address: ismail.turkbey@nih.gov.
5
NCI, Bethesda, MD 20892, USA. Electronic address: pchoyke@nih.gov.

Abstract

BACKGROUND:

Gleason Score (GS) is a validated predictor of prostate cancer (PCa) disease progression and outcomes. GS from invasive needle biopsies suffers from significant inter-observer variability and possible sampling error, leading to underestimating disease severity ("underscoring") and can result in possible complications. A robust non-invasive image-based approach is, therefore, needed.

PURPOSE:

Use spatially registered multi-parametric MRI (MP-MRI), signatures, and supervised target detection algorithms (STDA) to non-invasively GS PCa at the voxel level.

METHODS AND MATERIALS:

This study retrospectively analyzed 26 MP-MRI from The Cancer Imaging Archive. The MP-MRI (T2, Diffusion Weighted, Dynamic Contrast Enhanced) were spatially registered to each other, combined into stacks, and stitched together to form hypercubes. Multi-parametric (or multi-spectral) signatures derived from a training set of registered MP-MRI were transformed using statistics-based Whitening-Dewhitening (WD). Transformed signatures were inserted into STDA (having conical decision surfaces) applied to registered MP-MRI determined the tumor GS. The MRI-derived GS was quantitatively compared to the pathologist's assessment of the histology of sectioned whole mount prostates from patients who underwent radical prostatectomy. In addition, a meta-analysis of 17 studies of needle biopsy determined GS with confusion matrices and was compared to the MRI-determined GS.

RESULTS:

STDA and histology determined GS are highly correlated (R = 0.86, p < 0.02). STDA more accurately determined GS and reduced GS underscoring of PCa relative to needle biopsy as summarized by meta-analysis (p < 0.05).

CONCLUSION:

This pilot study found registered MP-MRI, STDA, and WD transforms of signatures shows promise in non-invasively GS PCa and reducing underscoring with high spatial resolution.

KEYWORDS:

Gleason scoring; MRI; Meta-analysis; Multi-parametric MRI; Prostate cancer; Supervised target detection

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