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. 2016 Apr;11(4):657-66.
doi: 10.1007/s11548-015-1287-x. Epub 2015 Sep 4.

Correlation of magnetic resonance imaging with digital histopathology in prostate

Jin Tae Kwak  1 , Sandeep Sankineni  2 , Sheng Xu  1 , Baris Turkbey  2 , Peter L Choyke  2 , Peter A Pinto  3 , Maria Merino  4 , Bradford J Wood  5
Affiliations

Correlation of magnetic resonance imaging with digital histopathology in prostate

Jin Tae Kwak et al. Int J Comput Assist Radiol Surg. 2016 Apr.

Abstract

Purpose: 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.

Keywords: Histopathology; Image registration; Machine learning; Prostate.

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Conflict of interest statement

CONFLICT OF INTEREST

Peter L. Choyke, Peter A. Pinto, and Bradford J. Wood have a cooperative research and development agreement with Philips Healthcare. Jin Tae Kwak, Sandeep Sankineni, Baris Turkbey, Sheng Xu, and Maria Merino declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Block diagram for the method of correlating MRI with digital histopathology.
Figure 2
Figure 2
Tissue segmentation. (a) A whole mount prostate tissue specimen is sectioned and stained with hematoxylin and eosin. Color-coded segmentation images are presented. (b) A tissue image is segmented in a cascaded manner: lumen, nuclei, and epithelium and stroma. Tissue images and segmentation results are shown for 1) TZ cancer and 2) benign peripheral zone. 3) The manual segmentation by a pathologist (top) is compared to the automated segmentation result (bottom).
Figure 3
Figure 3
Tissue specimen and MRI registration for 4 different patients. A H&E stained tissue specimen images is registered with its corresponding slice of T2W MRI and the ADC map of DWI. Tumor lesions are marked in red. (a)(b) TZ cancer (c) PZ cancer (d) benign.
Figure 4
Figure 4
Density maps for 4 different patients. A density map of tissue components is generated for the whole prostate using tissue segmentation and deformable image registration. Tumor lesions are marked in red. (a)(b) TZ cancer (c) PZ cancer (d) benign.
Figure 5
Figure 5
Box plot of MR signal intensity and tissue density. For transition zone cancer (TZ-C), peripheral zone cancer (PZ-C), peripheral zone benign (PZ-B), and benign prostatic hyperplasia (BPH), (a) the signal intensity of T2W and ADC map and (b) the percentage area of different tissue types are plotted.
See this image and copyright information in PMC

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