Jin Tae Kwak1, Sandeep Sankineni2, Sheng Xu1, Baris Turkbey2, Peter L Choyke2, Peter A Pinto3, Maria Merino4, Bradford J Wood5. 1. Center for Interventional Oncology, National Institutes of Health, Bethesda, MD, 20892, USA. 2. Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA. 3. Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA. 4. Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA. 5. Center for Interventional Oncology, National Institutes of Health, Bethesda, MD, 20892, USA. bwood@cc.nih.gov.
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.
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.
Authors: Andrew H Beck; Ankur R Sangoi; Samuel Leung; Robert J Marinelli; Torsten O Nielsen; Marc J van de Vijver; Robert B West; Matt van de Rijn; Daphne Koller Journal: Sci Transl Med Date: 2011-11-09 Impact factor: 17.956
Authors: Peter Gibbs; Gary P Liney; Martin D Pickles; Bashar Zelhof; Greta Rodrigues; Lindsay W Turnbull Journal: Invest Radiol Date: 2009-09 Impact factor: 6.016
Authors: Vijay Shah; Thomas Pohida; Baris Turkbey; Haresh Mani; Maria Merino; Peter A Pinto; Peter Choyke; Marcelino Bernardo Journal: Rev Sci Instrum Date: 2009-10 Impact factor: 1.523
Authors: Baris Turkbey; Haresh Mani; Vijay Shah; Ardeshir R Rastinehad; Marcelino Bernardo; Thomas Pohida; Yuxi Pang; Dagane Daar; Compton Benjamin; Yolanda L McKinney; Hari Trivedi; Celene Chua; Gennady Bratslavsky; Joanna H Shih; W Marston Linehan; Maria J Merino; Peter L Choyke; Peter A Pinto Journal: J Urol Date: 2011-09-25 Impact factor: 7.450
Authors: M L Schiebler; J E Tomaszewski; M Bezzi; H M Pollack; H Y Kressel; E K Cohen; H G Altman; W B Gefter; A J Wein; L Axel Journal: Radiology Date: 1989-07 Impact factor: 11.105
Authors: Baris Turkbey; Vijay P Shah; Yuxi Pang; Marcelino Bernardo; Sheng Xu; Jochen Kruecker; Julia Locklin; Angelo A Baccala; Ardeshir R Rastinehad; Maria J Merino; Joanna H Shih; Bradford J Wood; Peter A Pinto; Peter L Choyke Journal: Radiology Date: 2010-12-21 Impact factor: 11.105
Authors: Erin M McAuley; Devkumar Mustafi; Brian W Simons; Rebecca Valek; Marta Zamora; Erica Markiewicz; Sophia Lamperis; Anthony Williams; Brian B Roman; Chad Vezina; Greg Karczmar; Aytekin Oto; Donald J Vander Griend Journal: Am J Pathol Date: 2017-08-18 Impact factor: 4.307
Authors: Jin Tae Kwak; Sandeep Sankineni; Sheng Xu; Baris Turkbey; Peter L Choyke; Peter A Pinto; Vanessa Moreno; Maria Merino; Bradford J Wood Journal: Radiology Date: 2017-06-05 Impact factor: 11.105
Authors: Deborah K Hill; Andreas Heindl; Konstantinos Zormpas-Petridis; David J Collins; Leslie R Euceda; Daniel N Rodrigues; Siver A Moestue; Yann Jamin; Dow-Mu Koh; Yinyin Yuan; Tone F Bathen; Martin O Leach; Matthew D Blackledge Journal: Front Oncol Date: 2017-12-01 Impact factor: 6.244
Authors: 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 Journal: Quant Imaging Med Surg Date: 2020-02
Authors: Sean D McGarry; Sarah L Hurrell; Kenneth A Iczkowski; William Hall; Amy L Kaczmarowski; Anjishnu Banerjee; Tucker Keuter; Kenneth Jacobsohn; John D Bukowy; Marja T Nevalainen; Mark D Hohenwalter; William A See; Peter S LaViolette Journal: Int J Radiat Oncol Biol Phys Date: 2018-04-24 Impact factor: 8.013