Pathologist Concordance for Ovarian Carcinoma Subtype Classification and Identification of Relevant Histologic Features Using Microscope and Whole Slide Imaging.

CONTEXT.—: Despite several studies focusing on the validation of whole slide imaging (WSI) across organ systems or subspecialties, the use of WSI for specific primary diagnosis tasks has been underexamined. OBJECTIVE.—: To assess pathologist performance for the histologic subtyping of individual sections of ovarian carcinomas using a light microscope and WSI. DESIGN.—: A panel of 3 experienced gynecologic pathologists provided reference subtype diagnoses for 212 histologic sections from 109 ovarian carcinomas based on optical microscopy review. Two additional attending pathologists provided diagnoses and also identified the presence of a set of 8 histologic features important for ovarian tumor subtyping. Two experienced gynecologic pathologists and 2 fellows reviewed the corresponding WSI images for subtype classification and feature identification. RESULTS.—: Across pathologists specialized in gynecologic pathology, concordance with the reference diagnosis for the 5 major ovarian carcinoma subtypes was significantly higher for a pathologist reading on a microscope than each of 2 pathologists reading on WSI. Differences were primarily due to more frequent classification of mucinous carcinomas as endometrioid with WSI. Pathologists had generally low agreement in identifying histologic features important to ovarian tumor subtype classification with either an optical microscopy or WSI. This result suggests the need for refined histologic criteria for identifying such features. Interobserver agreement was particularly low for identifying intracytoplasmic mucin with WSI. Inconsistencies in evaluating nuclear atypia and mitoses with WSI were also observed. CONCLUSIONS.—: Further research is needed to specify the reasons for these diagnostic challenges and to inform users and manufacturers of WSI technology

Mathematical modelling of the spatial dispersion of brain MRI lesions in multiple sclerosis

Many previous studies in multiple sclerosis (MS) have focused on the relationship between white matter lesion volume and clinical parameters, but few have investigated the independent contribution of the spatial dispersion of lesions to patient disability. In this thesis, we investigate whether a mathematical measure of the 3D spatial dispersion of lesions can reveal clinical significance that is independent of volume. Our hypothesis is that for any two given patients with similar lesion loads, the one with greater lesion dispersion would tend to have a greater disability. We investigate four different approaches for quantifying lesion dispersion and examine the ability of these lesion dispersion measures to act as potential surrogate markers of disability. We propose one connectedness-based measure (compactness), two region-based measures (ratio of minimum bounding spheres and ratio of lesion convex hull to the brain volume), two distance-based measures (Euclidean distance from a fixed point and pair-wise Euclidean distances) and one measure based on network theory (small-worldness). Our data include three sets of MRIs (n = 24, 174, 182) selected from two MS clinical trials. We segment all white matter lesions in each scan with a semi-automatic method to produce binary images of lesion voxels, quantify their spatial dispersion using the defined measures, then perform a statistical analysis to compare the dispersion values to total lesion volume and patient disability. We use linear and rank correlations to investigate the relationships between dispersion, disability, and total lesion volume, and regression analysis to investigate whether there is a potentially meaningful relationship between dispersion and disability, independent of volume. Our main finding is that one distance based measure, Euclidean distance from a fixed point, consistently correlates with disability score across all three datasets, and has predictive value that is at least partly independent of lesion volume. The results provide support for our hypothesis and suggest that a potentially meaningful relationship exists between patient disability and measurements of lesion dispersion. Finding such relationships can improve the understanding of MS and potentially lead to the discovery of novel surrogate biomarkers for clinical use in designing treatment trials and providing prognostic advice to individual patients.Applied Science, Faculty ofGraduat

Improving cervical neoplasia diagnosis via novel in vivo imaging technologies and deep learning algorithms

Two directions are explored for improving the current cervical cancer diagnosis procedure. The first investigates the future deployment of in vivo confocal imaging in the clinic, for detecting precancerous tissues, and the second proposes an algorithm for automatic interpretation of histology images (acquired by light microscopy). We acquired i) confocal microscopy images of cervical biopsies taken from 50 patients, at different tissue depths and ii) histology images of different sections cut from each biopsy. From the confocal images, we identified four features that carry enough information relevant to cell morphology and tissue architecture. We demonstrated that the relevant information in these features is comparable to that extracted from the same features in histology images. This implies that we can obtain the relevant information from confocal imaging, without having to cut a biopsy from the patient’s cervix. We then studied the confocal images and determined the grade lesion of every biopsy and found that confocal imaging resulted in less false positives than the diagnosis given by the gynecologist (based on the appearance of the cervix under colposcopy). Utilizing confocal microscopy technology in the clinic would thus decrease the number of unnecessary biopsies. We then developed a deep learning algorithm that automatically and quantitatively assesses HPV contaminated and proliferating cells in histology images of biopsy sections. The automatic assessment of this procedure is important as it plays a significant role in differentiating between disease grades but forms a challenging and complex task and demands a large amount of time when performed manually by a pathologist. We demonstrated that this algorithm could help the pathologists to differentiate between different grades of cervical precancerous tissues. Our results are also more reproducible compared to other methods (like color deconvolution) that are widely being used in the field of digital pathology. The in vivo imaging and automatic image analysis algorithms demonstrated in the thesis can potentially enable i) real time diagnosis in the clinic, and ii) fast interpretation of histology images in a reproducible and cost-effective manner. While developed for cervical neoplasia, these methods could be extended to oral cavity, skin, and other epithelial tissue cancers.Applied Science, Faculty ofBiomedical Engineering, School ofGraduat

Spatial Dispersion of Lesions as a Surrogate Biomarker for Disability in Multiple Sclerosis

Many previous studies in multiple sclerosis (MS) have focused on the relationship between white matter lesion volume and clinical parameters, but few have investigated the independent contribution of the spatial dispersion of lesions to patient disability. In this study, we examine the ability of four different measures of lesion dispersion including one connectedness-based measure (compactness), one regionbased measure (ratio of lesion convex hull to brain volume) and two distance-based measures (Euclidean distance from a fixed point and pair-wise Euclidean distances) to act as potential surrogate markers of disability. We use a set of T2-weighted and proton density-weighted MRIs of 24 MS patients, collected from a single selected scanning site participating in an MS clinical trial. For each patient, clinica

Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks - Fig 5

<p>Heat-maps resulting from biomarker labeling on an example IHC image (a) using WI-Net. The IHC image (size 2048×2048 pixels) belonging to a whole slide image obtained from the scanner was fed to the WI-Net (a). The outputs of the WI-Net were the heat-maps (size 2048 × 2048 pixels) marking p16 positive regions (b), Ki-67 positive regions (c), p16 and Ki-67 positive regions (d), p16 and Ki-67 negative regions (e). The four heat-maps were combined to produce an overall biomarker heat-map (f).</p

Sample of nuclei images obtained from an IHC image.

<p>ROI (red enclosure) selected in an IHC image, within which the nuclei were segmented in order to obtain nuclei images for training N-Net (left). Nuclei images expressing different proteins (right); p16 positive, Ki-67 positive, p16 and Ki-67 positive, and p16 and Ki-67 negative.</p

WI-Net performance evaluation of two IHC images taken from two WSIs: Based on biomarker heat-maps generated by WI-Net.

<p>The ground truth is taken as the labels obtained manually by humans.</p

Comparison between color deconvolution approach and WI-Net approach for locating p16 and Ki-67 positive pixels in two IHC images.

<p>The IHC image on the left two columns corresponds to a high-grade cervical lesion. The IHC image on the right two columns corresponds to a normal cervical epithelium. The first row shows the RGB images. The second row shows the regions marked as p16 positive by the two methods. The third row shows the regions marked as Ki-67 positive by the two methods.</p

The ROI selected on an IHC image (left), and evenly distributed layers in the ROI parallel to the basal layer (right).

<p>The ROI selected on an IHC image (left), and evenly distributed layers in the ROI parallel to the basal layer (right).</p