Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging

Abstract

Rationale and Objectives: The objective of this study was to develop and quantitatively evaluate a radiology-pathology fusion method for spatially mapping tissue regions corresponding to different chemoradiation therapy-related effects from surgically excised whole-mount rectal cancer histopathology onto preoperative magnetic resonance imaging (MRI). Materials and Methods: This study included six subjects with rectal cancer treated with chemoradiation therapy who were then imaged with a 3-T T2-weighted MRI sequence, before undergoing mesorectal excision surgery. Excised rectal specimens were sectioned, stained, and digitized as two-dimensional (2D) whole-mount slides. Annotations of residual disease, ulceration, fibrosis, muscularis propria, mucosa, fat, inflammation, and pools of mucin were made by an expert pathologist on digitized slide images. An expert radiologist and pathologist jointly established corresponding 2D sections between MRI and pathology images, as well as identified a total of 10 corresponding landmarks per case (based on visually similar structures) on both modalities (five for driving registration and five for evaluating alignment). We spatially fused the in vivo MRI and ex vivo pathology images using landmark-based registration. This allowed us to spatially map detailed annotations from 2D pathology slides onto corresponding 2D MRI sections. Results: Quantitative assessment of coregistered pathology and MRI sections revealed excellent structural alignment, with an overall deviation of 1.50 ± 0.63 mm across five expert-selected anatomic landmarks (in-plane misalignment of two to three pixels at 0.67- to 1.00-mm spatial resolution). Moreover, the T2-weighted intensity distributions were distinctly different when comparing fibrotic tissue to perirectal fat (as expected), but showed a marked overlap when comparing fibrotic tissue and residual rectal cancer. Conclusions: Our fusion methodology enabled successful and accurate localization of post-treatment effects on in vivo MRI.

Histological Validation of MRI: A Review of Challenges in Registration of Imaging and Whole‐Mount Histopathology

“…Prostate tissues are readily available for coregistration, since radical prostatectomy is a common treatment for localized prostate cancer 60 . A recent study described coregistration of human rectal tissue sections with in vivo MRI 33 …”

“…60 A recent study described coregistration of human rectal tissue sections with in vivo MRI. 33 Registration algorithms use similar or common characteristics between input images to estimate an optimal geometric transformation/alignment. The matching of similar characteristics typically uses feature-or intensity-based approaches.…”

“…BLOCK-FACE IMAGES. During microtome sectioning, optical images of the embedded tissue block, 84 referred to as "block-face" images, 11,14,33 can be taken prior to cutting each section. These images can be used to reduce the impact of procedural artifacts introduced during thin sectioning, slide mounting, and staining (2D deformation, eg, tears, distortions).…”

Correlation of ultra-high field MRI with histopathology for evaluation of rectal cancer heterogeneity

“…A technique for co-registration of in vivo MRI with histopathology in rectal cancer has been developed by Antunes et al . 16 . Antunes et al .…”

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

“…Within the oncology space, radiomics has found applications as a means of diagnosis, a prognostic tool predicting response to therapy 35 across organ systems, including but not limited to brain, 36–39 head and neck, 40–42 breast, 43–50 lung, 42,51–53 prostate, 54–58 rectum, 59–64 and liver. 65–69 In the context of lung cancer, radiomics has successfully allowed detection of malignancies in screening CT scans, 53 provided a means to differentiate between benign and malignant lesions, 51 enabled the prediction of risk of recurrence post-therapy, 52 and provided a means to noninvasively assess response to therapy 70 as well as helped to identify patients who would most benefit from therapy.…”