Correlation of magnetic resonance imaging with digital histopathology in prostate.
OBJECTIVE
We propose a systematic approach to correlate MRI and digital histopathology in prostate.


METHODS
T2-weighted (T2W) MRI and diffusion-weighted imaging (DWI) are acquired, and a patient-specific mold (PSM) is designed from the MRI. Following prostatectomy, a whole mount tissue specimen is placed in the PSM and sectioned, ensuring that tissue blocks roughly correspond to MRI slices. Rigid body and thin plate spline deformable registration attempt to correct deformation during image acquisition and tissue preparation and achieve a more complete one-to-one correspondence between MRIs and tissue sections. Each tissue section is stained with hematoxylin and eosin and segmented by adopting a machine learning approach. Utilizing this tissue segmentation and image registration, the density of cellular and tissue components (lumen, nucleus, epithelium, and stroma) is estimated per MR voxel, generating density maps for the whole prostate.


RESULTS
This study was approved by the local IRB, and informed consent was obtained from all patients. Registration of tissue specimens and MRIs was aided by the PSM and subsequent image registration. Tissue segmentation was performed using a machine learning approach, achieving ≥0.98 AUCs for lumen, nucleus, epithelium, and stroma. Examining the density map of tissue components, significant differences were observed between cancer, benign peripheral zone, and benign prostatic hyperplasia (p value <5e−2). Similarly, the signal intensity of the corresponding areas in both T2W MRI and DWI was significantly different (p value <1e−10).


CONCLUSIONS
The proposed approach is able to correlate MRI and digital histopathology of the prostate and is promising as a potential tool to facilitate a more cellular and zonal tissue-based analysis of prostate MRI, based upon a correlative histopathology perspective.
Center for Interventional Oncology, National Institutes of Health, Bethesda, MD, 20892, USA.
Jin Tae
JT
Kwak
Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Sandeep
S
Sankineni
Center for Interventional Oncology, National Institutes of Health, Bethesda, MD, 20892, USA.
Sheng
S
Xu
Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Baris
B
Turkbey
Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Peter L
PL
Choyke
Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Peter A
PA
Pinto
Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Maria
M
Merino
Center for Interventional Oncology, National Institutes of Health, Bethesda, MD, 20892, USA. bwood@cc.nih.gov.
Bradford J
BJ
Wood
D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*
Histopathology; Image registration; Machine learning; Prostate