Imaging of prostate cancer: a platform for 3D co-registration of in-vivo MRI ex-vivo MRI and pathology

Abstract

Objectives Multi-parametric MRI is emerging as a promising method for prostate cancer diagnosis. prognosis and treatment planning. However, the localization of in-vivo detected lesions and pathologic sites of cancer remains a significant challenge. To overcome this limitation we have developed and tested a system for co-registration of in-vivo MRI, ex-vivo MRI and histology. Materials and Methods Three men diagnosed with localized prostate cancer (ages 54–72, PSA levels 5.1–7.7 ng/ml) were prospectively enrolled in this study. All patients underwent 3T multi-parametric MRI that included T2W, DCE-MRI, and DWI prior to robotic-assisted prostatectomy. Ex-vivo multi-parametric MRI was performed on fresh prostate specimen. Excised prostates were then sliced at regular intervals and photographed both before and after fixation. Slices were perpendicular to the main axis of the posterior capsule, i.e., along the direction of the rectal wall. Guided by the location of the urethra, 2D digital images were assembled into 3D models. Cancer foci, extra-capsular extensions and zonal margins were delineated by the pathologist and included in 3D histology data. A locally-developed software was applied to register in-vivo, ex-vivo and histology using an over-determined set of anatomical landmarks placed in anterior fibro-muscular stroma, central. transition and peripheral zones. The mean root square distance across corresponding control points was used to assess co-registration error. Results Two specimens were pT3a and one pT2b (negative margin) at pathology. The software successfully fused in-vivo MRI. ex-vivo MRI fresh specimen and histology using appropriate (rigid and affine) transformation models with mean square error of 1.59 mm. Coregistration accuracy was confirmed by multi-modality viewing using operator-guided variable transparency. Conclusion The method enables successful co-registration of pre-operative MRI, ex-vivo MRI and pathology and it provides initial evidence of feasibility of MRI-guided surgical planning.

Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology

“…This aspect of the study is important as it allows the heterogeneity of both cancer and non-cancer regions to be considered. Despite the presence of several previously presented registration frameworks to map post-prostatectomy tissue onto MRI (29-31), such methods have not been used for mpMRI model development, most likely due to the challenges present in obtaining and registering such data. Instead of directly mapping areas of pathologically identified cancer, correlative pathology has been used in other ways including the manual transfer of pathologically defined regions to MRI (11, 13, 14) or by using the pathology results to identify positive or negative sextants or octants for model development and evaluation (13, 18).…”

Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT

“…While the use of contours derived from histological examination of surgically resected tumors would seem an ideal gold standard, errors are often introduced due to changes in tissue morphology during resection, histoprocessing [45], and during registration [46]. While a great deal of interobserver variability exists, the use of STAPLE to draw a probabilistic map has shown validity for predicting expert consensus [47] and providing ground truth in the case of the Cardiac Atlas Project [48].…”

Apparatus for Histological Validation of In Vivo and Ex Vivo Magnetic Resonance Imaging of the Human Prostate

“…A partial 3D histology “map” can be reconstructed from a stack of histology slices and registered with MR images by a 3D process (7, 8); however, the accuracy of this approach is severely limited by the low out of plane resolution of the MRI data. The problem of missing inter-plane histology data can be addressed by manual (9, 10) or automatic (11) selection of the most closely aligned histology and image planes.…”