Person:

Gilbertson, John R

Introduction:: The advent of digital slides offers new opportunities within the practice of pathology such as the use of image analysis techniques to facilitate computer aided diagnosis (CAD) solutions. Use of CAD holds promise to enable new levels of decision support and allow for additional layers of quality assurance and consistency in rendered diagnoses. However, the development and testing of prostate cancer CAD solutions requires a ground truth map of the cancer to enable the generation of receiver operator characteristic (ROC) curves. This requires a pathologist to annotate, or paint, each of the malignant glands in prostate cancer with an image editor software - a time consuming and exhaustive process. Recently, two CAD algorithms have been described: probabilistic pairwise Markov models (PPMM) and spatially-invariant vector quantization (SIVQ). Briefly, SIVQ operates as a highly sensitive and specific pattern matching algorithm, making it optimal for the identification of any epithelial morphology, whereas PPMM operates as a highly sensitive detector of malignant perturbations in glandular lumenal architecture. Methods:: By recapitulating algorithmically how a pathologist reviews prostate tissue sections, we created an algorithmic cascade of PPMM and SIVQ algorithms as previously described by Doyle el al. [1] where PPMM identifies the glands with abnormal lumenal architecture, and this area is then screened by SIVQ to identify the epithelium. Results:: The performance of this algorithm cascade was assessed qualitatively (with the use of heatmaps) and quantitatively (with the use of ROC curves) and demonstrates greater performance in the identification of malignant prostatic epithelium. Conclusion:: This ability to semi-autonomously paint nearly all the malignant epithelium of prostate cancer has immediate applications to future prostate cancer CAD development as a validated ground truth generator. In addition, such an approach has potential applications as a pre-screening/quality assurance tool.

Background: Histopathology is the only accepted method to measure and stage the breast tumor size. However, there is a need to find another method to measure and stage the tumor size when the pathological assessment is not available. Micro-computed tomography. (micro-CT) has the ability to measure tumor in three dimensions in an intact lumpectomy specimen. In this study, we aimed to determine the accuracy of micro-CT to measure and stage the primary tumor size in breast lumpectomy specimens, as compared to the histopathology. Materials and Methods: Seventy-two women who underwent lumpectomy surgery at the Massachusetts General Hospital Department of Surgery from June 2011 to September 2011, and from August 2013 to December 2013 participated in this study. The lumpectomy specimens were scanned using micro-CT followed by routine pathological processing. The maximum dimension of the invasive breast tumor was obtained from the micro-CT image and was compared to the corresponding pathology report for each subject. Results: The invasive tumor size measurement by micro-CT was underestimated in 24 cases. (33%), overestimated in 37 cases. (51%), and matched it exactly in 11 cases. (15%) compared to the histopathology measurement for all the cases. However, micro-CT T-stage classification differed from histopathology in only 11. (15.2%) with 6 cases. (8.3%) classified as a higher stage by micro-CT, and 5 cases. (6.9%) classified as lower compared to histopathology. In addition, micro-CT demonstrated a statically significant strong agreement (κ =0.6, P < 0.05) with pathological tumor size and staging for invasive ductal carcinoma. (IDC) group. In contrast, there was no agreement. (κ = −2, P = 0.67) between micro-CT and pathology in estimating and staging tumor size for invasive lobular carcinoma. (ILC) group. This could be explained by a small sample size. (7) for ILC group. Conclusions: Micro-CT is a promising modality for measuring and staging the IDC.