Automated Grading of Bladder Cancer using Deep Learning

PhD thesis in Information technologyUrothelial carcinoma is the most common type of bladder cancer and is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, mainly based on the histological grade and stage. However, it is well known that correct grading of bladder cancer suffers from intra- and interobserver variability and inconsistent reproducibility between pathologists, potentially leading to under- or overtreatment of the patients. The economic burden, unnecessary patient suffering, and additional load on the health care system illustrate the importance of developing new tools to aid pathologists. With the introduction of digital pathology, large amounts of data have been made available in the form of digital histological whole-slide images (WSI). However, despite the massive amount of data, annotations for the given data are lacking. Another potential problem is that the tissue samples of urothelial carcinoma contain a mixture of damaged tissue, blood, stroma, muscle, and urothelium, where it is mainly the urothelium tissue that is diagnostically relevant for grading. A method for tissue segmentation is investigated, where the aim is to segment WSIs into the six tissue classes: urothelium, stroma, muscle, damaged tissue, blood, and background. Several methods based on convolutional neural networks (CNN) for tile-wise classification are proposed. Both single-scale and multiscale models were explored to see if including more magnification levels would improve the performance. Different techniques, such as unsupervised learning, semi-supervised learning, and domain adaptation techniques, are explored to mitigate the challenge of missing large quantities of annotated data. It is necessary to extract tiles from the WSI since it is intractable to process the entire WSI at full resolution at once. We have proposed a method to parameterize and automate the task of extracting tiles from different scales with a region of interest (ROI) defined at one of the scales. The method is reproducible and easy to describe by reporting the parameters. A pipeline for automated diagnostic grading is proposed, called TRIgrade. First, the tissue segmentation method is utilized to find the diagnostically relevant urothelium tissue. Then, the parameterized tile extraction method is used to extract tiles from the urothelium regions at three magnification levels from 300 WSIs. The extracted tiles form the training, validation, and test data used to train and test a diagnostic model. The final system outputs a segmented tissue image showing all the tissue regions in the WSI, a WHO grade heatmap indicating low- and high-grade carcinoma regions, and finally, a slide-level WHO grade prediction. The proposed TRIgrade pipeline correctly graded 45 of 50 WSIs, achieving an accuracy of 90%

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202

Genetic analysis of inherited retinal diseases in indigenous Southern African populations

Background: Inherited retinal diseases (IRDs) constitute a group of clinically and genetically heterogeneous conditions which cause degeneration of retinal photoreceptor cells and result in visual impairment. Characterisation of the genetic basis of IRD is not only beneficial for the affected families, but also contributes towards understanding of the disease pathobiology. Investigations into the molecular basis of IRDs have been ongoing in South Africa (SA) for over 30 years, however the evaluation of reported genetic mutations has yielded low returns in certain populations. Indigenous southern Africans comprise a unique population group with distinct genetic diversity, providing a valuable resource for genetic discoveries; nonetheless, this population remains largely underrepresented in genomic studies. The aim of this investigation was to characterise the underlying genetic mutations in a cohort of indigenous African IRD patients. Methods: The IRD registry in the Division of Human Genetics (University of Cape Town) was reviewed for causative mutations. Subsequently, upon identifying a mutation underlying Usher Syndrome in two indigenous African patients, an assay was designed to screen for this mutation in probands with different IRDs (n=170) and controls (n=51), and haplotype analysis was performed on mutation-positive individuals. The registry review additionally served to identify a suitable cohort for the application of next generation sequencing (NGS) technology. Whole exome sequencing (WES) was performed on genomic DNA samples from 56 individuals from 16 families. The WES data analysis strategy involved prioritisation of variants in reported and candidate IRD genes. Rare, co-segregating, pathogenic, exonic or splice variants were validated by Sanger sequencing. Custom TaqMan assays were designed to screen seven mutations, identified by WES, in 193 unrelated indigenous African probands with IRDs. Results: A homozygous founder mutation, c.6377delC in MYO7A, was identified in 43% of the indigenous African patients with Usher syndrome, which is the most common cause of deaf-blindness. Targeted WES data analysis of all known IRD genes resulted in identification of the underlying genetic defects in six distinct genes (RHO, PRPF3, PRPF31, ABCA4, CERKL, and PDE6B) in six families. Taqman screening revealed four additional probands with identical homozygous mutations in CERKL and PDE6B. An X-linked gene (RP2) mutation was subsequently identified in an affected family with semi-dominant retinitis pigmentosa. Supplementary analysis of the X-linked RPGR ORF15 mutation hotspot (not adequately covered by WES) identified two mutations in three families. A novel IRD gene, IDH3A, was found in one family by analysis of 22 putative candidate genes. The large number of variants in the remainder of the indigenous African exomes presented considerable challenges for identification of additional novel genes. Discussion: The results of this project have important implications for IRD molecular diagnostic services in SA. Using WES, a genetic diagnosis was obtained for ±73% of the indigenous African cohort, and ±70% of the causative mutations identified were novel. This outcome emphasises the superiority of NGS-based approaches over genotyping-based microarrays which screen for IRD mutations previously reported in other (mainly European-derived) populations. The unexpected identification of mutations in known X-linked genes in four families highlighted key considerations for IRD WES analysis. Cascade screening of mutations identified in this study, across larger cohorts of unrelated probands, revealed the genetic cause of IRD in additional cases and the number of indigenous African families in the registry with a genetic diagnosis was effectively doubled. Members of these families can now opt for diagnostic, carrier, or predictive testing of familial mutations. Finally, the information obtained from this research contributes towards a better understanding of the genetic architecture of IRDs in SA

Artificial Intelligence in Oncology Drug Discovery and Development

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence

2023 Summer Experience Program Abstracts

https://openworks.mdanderson.org/sumexp23/1130/thumbnail.jp

P5 eHealth: An Agenda for the Health Technologies of the Future

This open access volume focuses on the development of a P5 eHealth, or better, a methodological resource for developing the health technologies of the future, based on patients’ personal characteristics and needs as the fundamental guidelines for design. It provides practical guidelines and evidence based examples on how to design, implement, use and elevate new technologies for healthcare to support the management of incurable, chronic conditions. The volume further discusses the criticalities of eHealth, why it is difficult to employ eHealth from an organizational point of view or why patients do not always accept the technology, and how eHealth interventions can be improved in the future. By dealing with the state-of-the-art in eHealth technologies, this volume is of great interest to researchers in the field of physical and mental healthcare, psychologists, stakeholders and policymakers as well as technology developers working in the healthcare sector

Background Examples of Literature Searches on Topics of Interest

A zip file of various literature searches & some resources related to our work related to exposure after the Chernobyl accident and as we began looking at helping in Semey Kazakhstan----a collection of literature reviews on various topics we were interested in... eg. establishing a registry of those exposed for longterm follow-up, what we knew about certain areas like genetics and some resources like A Guide to Environmental Resources on the Internet by Carol Briggs-Erickson and Toni Murphy which could be found on the Internet and was written to be used by researchers, environmentalists, teachers and any person who is interested in knowing and doing something about the health of our planet. See more at https://archives.library.tmc.edu/dm-ms211-012-0060

Urological Cancer 2020

This Urological Cancer 2020 collection contains a set of multidisciplinary contributions to the extraordinary heterogeneity of tumor mechanisms, diagnostic approaches, and therapies of the renal, urinary tract, and prostate cancers, with the intention of offering to interested readers a representative snapshot of the status of urological research