Title: IDEAL: Artificial Intelligence and Big Data for Early Lung Cancer Diagnosis
Funder: Department of Health and Social Care

Abstract: IDEAL comprises 6 work packages (WP) over 36 months. Each WP has a leader, with Milestones, Tasks (T) and Deliverables (D). See Appendix A for a GANTT chart. WP1: Project Management (M1-M36)(Lead: FG, Involved: VP-DB-MC-SMB, FTE:1.35) The project will be led by FG supported by a Project Manager (PM), who will ensure that the project meets the required quality, time & cost, identify risks, manage spend & provide reports. D1.1. Risk assessment (M1) D1.2. Regular progress reports WP2: Planning for a Big Dataset (M1-M9)(Lead: FG, Involved: LP-MC-DB, FTE:3.75) T2.1. Database Definition (M1) We will define the database architecture to enable selection of data subsets to adaptively re-train a best classifier for the patient demographic/scanner/pathway. Project partners will test the baseline system and decide what annotations will be included, including making the database multi-use (e.g. including COPD annotations). We will define thresholds for minimum acceptable performance. T2.2. Big Data Acquisition and Testing (M2-M9) We will collect >500 patients containing pulmonary nodules (PN) with ground-truth per site (total: 1500). The PN Clinics at three sites see 90 patients per month and have a database of over 1000 patients. The new database will be used to augment pre-existing datasets. The database creation will require an innovative data science approach, so that the thousands of images represent the millions of possible patient, scanner, and protocol combinations. The clinical research fellow and radiographers will collect, annotate and anonymize data. They will iteratively re-train and test the algorithms in collaboration with Optellum, in order to identify nodule types (e.g. sizes, characteristics, clinical pathways) and scan types (scanner model, protocol, kernel, resolution) to be added in order to create a balanced and unbiased set. Milestone 1: Database defined (M1) Milestone 2: Database ready (M9) D2.1. New PN database with 1500 patients WP3: Product Development (M1-M15)(Lead: VP, Involved: LP-SMB, FTE:4.625) T3.1. Machine Learning on Multi-Center Data (M1-M9, FTE:1.5) The evolving database from WP2 will be integrated into a “lean product development” to improve machine learning and guide data acquisition, so that the DIB handles heterogeneous data. Optellum will productize the LVB prototype so that it can be robustly deployed in a hospital setting. T3.2. Front-end Prototype (M1-M3)(FTE:0.5) User interface prototype defined and developed, with frequent Usability Tests with the end users. T3.3. Back-end Prototype(M3-M9)(FTE:0.5) Back-end server ready for PACS integration developed and tested. T3.4. Final Product Development (M10-M15)(FTE:1.5) T3.5. CE-marking (M1-M15)(FTE:0.625) We determine regulatory classification according to CE-MDD. Quality Management System (QMS) will be introduced and user testing conducted with help of consultant from ScreenPoint Medical. We expect to apply for CE-marking in M12. Milestone 3: Prototype ready for clinical study (M9) Milestone 4: CE-marking (M15) D3.1. Full software D3.2. CE-mark WP4: Texture Risk-model (M1-M12)(Lead: FG, Involved: LP-MC-DB-SMB, FTE:1.0) T4.1. Risk-model development. We will develop a risk model for small pulmonary nodules that incorporates texture using retrospective data. Milestone 5: Texture included in risk model (M9) D4.1. Risk model report (M12) WP5: Clinical Study (M9-M33)(Lead FG, Involved: MC-DB, FTE:10.0) T5.1. Prospective clinical study (M9-M33) Research radiographers at the three sites, under the guidance of the clinical research fellow, will assess patients with PN and enter them into the clinical trial and follow them for up to 12 months with established methods (volumetry, PET) compared against the initial DIB score. Milestone 6: Final patient follow up (M33) D5.1. Study results report (M36) WP6: Analysis & Dissemination (M21-M36)(Lead FG, Involved: MC-DB-VP, FTE:1.25) T6.1. NHS dissemination: Results presented at BTS and BSTI meetings. T6.2. International dissemination LVB launched at the 2018 European Congress of Radiology. Milestone 7: Project finished (M36)

Lay abstract: Large numbers of patients have CT scans, for a variety of reasons, ranging from screening for lung cancer to scanning for infection or even following trauma. Such scans frequently identify nodules, small lumps growing in the lung, less than 1cm in size. Up to 50% of all patients scanned have such small nodules, but up to 98% of them are harmless and will not cause problems to the patient. Unfortunately, radiologists frequently struggle to determine if a nodule is cancerous, leading to millions of wasteful follow-up scans, and delayed diagnoses. Indeed, the current method for deciding if a nodule is harmless is to perform follow-up scans to see whether it grows, wasteful of NHS resources and needlessly worrisome to patients. At present the majority of nodules are followed up for 12 months as per the recently published British Thoracic Society Guidelines. We will develop software that improves doctors’ ability to diagnose the cause of lung nodules, tiny dense lumps seen on CT scans. Our software will develop its own means of deciding if a nodule is significant by extracting patterns from a database of thousands of examples using advanced machine learning. For this to be successful, we aim to collect many examples of different scans containing known cancerous and harmless nodules. These scans are retrospective in nature, but compiling them in a carefully-labelled database is a particularly meticulous task. The output from the machine learning process is a model that can be applied to a new scan in order to gauge the likelihood of a new, unseen nodule being cancerous. Once we have trained this model, and developed a software interface to make it accessible to the clinical world, we will use it on nodules identified on scanning, and compare the results to the models that are used at present.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS