Spatial density and diversity of architectural histology in prostate cancer: influence on diffusion weighted magnetic resonance imaging

Stephanie A Harmon; G Thomas Brown; Thomas Sanford; Sherif Mehralivand; Joanna H Shih; Sheng Xu; Maria J Merino; Peter L Choyke; Peter A Pinto; Bradford J Wood; Jesse K McKenney; Baris Turkbey

doi:10.21037/qims.2020.01.06

Spatial density and diversity of architectural histology in prostate cancer: influence on diffusion weighted magnetic resonance imaging

Quant Imaging Med Surg. 2020 Feb;10(2):326-339. doi: 10.21037/qims.2020.01.06.

Authors

Stephanie A Harmon^{1

2}, G Thomas Brown^{1

3}, Thomas Sanford², Sherif Mehralivand², Joanna H Shih⁴, Sheng Xu⁵, Maria J Merino⁶, Peter L Choyke², Peter A Pinto⁷, Bradford J Wood⁵, Jesse K McKenney⁸, Baris Turkbey²

Affiliations

¹ Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Bethesda, MD, USA.
² Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
³ National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
⁴ Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁵ Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁶ Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁷ Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁸ Department of Anatomic Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.

Abstract

Background: To assess the influence of specific histopathologic patterns on MRI diffusion characteristics by performing rigorous whole-mount/imaging registration and correlating histologic architectures observed in prostate cancer with diffusion characteristics in prostate MRIs.

Methods: Fifty-two whole-mount pathology blocks from 15 patients who underwent multiparametric MRI (mpMRI) at a single institution prior to radical prostatectomy were retrospectively analyzed. Regions containing individual morphologic patterns (N=21 patterns, including variations of cribriforming, expansile sheets, single cells, patterns of early intraluminal complexity, and mucin rupture patterns) were digitally annotated by an expert genitourinary pathologist. Distinct tumor foci on each slide were also assigned a Gleason grade and scored as having any high-risk histologic pattern. Digital sections were aligned to MRI using a patient-specific mold and registered using local mean weighted piecewise transformation based on anatomic control points. Density and presence of morphological patterns was correlated to apparent diffusion coefficient (ADC) signal intensity using mixed effects model accounting for nested intra-foci, intra-patient correlation. Influence of intra-tumoral heterogeneity was assessed by affinity propagation clustering (APC) of morphology features and correlated to foci- and cluster-level ADC metrics.

Results: One hundred eleven distinct tumor foci were evaluated. Beta diversity, reflecting average morphology representation across inter- and intra-foci areas, demonstrated higher intra-tumor diversity within high-risk foci (P<0.05). ADC signal demonstrated an inverse correlation with foci-level Gleason grade (P>0.05), which was strengthened in cluster-level analysis for intra-foci regions containing high-risk morphologies (P=0.017). In voxel-based analysis, dense regions demonstrate lower ADC, but the presence and density for each morphology influenced ADC independently (ANOVA P<0.001).

Conclusions: Architectural features influence ADC characteristics of MRI, with more complex tumors having lower ADC values regulated by presence and density of specific morphologies.

Keywords: Multiparametric MRI (mpMRI); digital pathology; prostate cancer architecture.