Title: Realtime data synthesis and analysis for CANCER detection and Prevention (R-CANCER)
Funder: Department of Health and Social Care

Abstract: Background: The NHS strategy cancer plan aims to improve early detection rates. This requires a timely and accurate collation and interpretation of data on biomarkers, clinical outcomes and trial and social data. The scale and complexity of the cancer infodemic is a major barrier to achieving this goal and the public are vulnerable to misinformation. Aims and Objectives: To create a minimum viable product for the REaltime Data Synthesis and Analysis (REDASA) of colorectal cancer data. We will develop scalable methodologies for autonomous report generation and fact checking based on the analysis of annotated data generated by our curation methodology. Work plan and methods: A proof of concept early detection analysis in colorectal cancer. WP 1: A continuous, autonomously updating colorectal cancer data pipeline from high-quality colorectal cancer datasets, unstructured web crawl and social media data. WP 2: Curation workflow and quality assurance. Completion of current curation UX interface and delivery of scalable platform. Creation of curation quality metrics and a live, open curation workflow for colorectal cancer. WP 3: Feasibility testing of real time data synthesis and analysis. Quality and relevance metrics based on curation analysis and an NLP methodology for the autonomous reporting of abstractive summaries, networked meta analysis and fact checking. WP4: Prospective qualitative validation of the colorectal R-CANCER framework with clinicians through semi-structured questionnaires, interviews and business case for implementation. Anticipated Impact and Dissemination: We will create a community of data curators (>1000) and establish an open annotated cancer data set for the machine learning community. The CURADR platform will be free to NHS employees and promoted by Imperial College and AWS. All data will be communicated through social and formal academic channels and to patients through the Imperial CRUK centre,Imperial BRC outreach initiatives.

Lay abstract: Both doctors and patients need the most up-to-date information so that they can make the very best decisions on treatments for cancer. However, new information on cancer treatment is now generated at such a speed and volume that it has become overwhelming. This is known as an &ldquo;infodemic&rdquo;. The cancer infodemic means that the guidelines for the best practices for cancer prevention and treatment are produced slowly and they are often out of date. The growth in &ldquo;fake news&rdquo; and cancer misinformation, which is often spread online also causes patient harm because it erodes trust in evidence-based clinical medicine or patients are falsely reassured by ineffective or dangerous treatments. R-CANCER is a new software platform that allows both doctors and patients to instantly generate real-time guidance on the best treatment practices for cancer from openly available published data. It allows users to see how trustworthy cancer treatments are. R-CANCER has three components: 1. Our technology continuously searches the internet for studies, databases, information on cancer prevention and detection. We don t just search medical journals, but we also search news sites, social media, and professional web content and this archived. 2. A growing community of researchers, clinicians, and academics use our technology to efficiently assess the quality of the data we record and label it for a machine to read. This data is open to the cancer research community to use. 3. Development of machine learning and artificial intelligence (AI) algorithms, for the automatic interpretation, summarisation, and analysis of our curated data to answer important clinical questions that can be asked by any doctor or patient. This analysis is continuous and always switched &ldquo;on&rdquo; meaning that best practice guidelines can be continually updated. We will also create new ways to measure how trustworthy information on certain cancer treatments is. This work is a collaboration between Imperial College London and Amazon Web Services collaboration. This pilot project will expand our current prototype into a scalable system for formal testing with doctors and patients. The proposed work will take place at Imperial NHS Trust and the Imperial CRUK Centre. Our methodology will be piloted and validated in colorectal cancer, as a single cancer type. The study group will work with clinicians, researchers, and patient groups to define and prioritise critical questions and refine the user interface for the system. Finally, we will optimise and validate the machine learning component of the system to determine the accuracy of its reporting methodologies. The validated data generated from this study will form the basis of a Phase 2 study that scales the number of cancer types and this will determine the clinical impact of the technology in real-world conditions.

Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: CANCER AND NEOPLASMS