Title: Decision-assist software for management of acute ischaemic stroke using brain-imaging machine-learning
Funder: Department of Health and Social Care

Abstract: 1. We shall acquire a retrospective dataset of clinical and CT data (n~10,000; of which ~1000 SICHs). This step is facilitated by ethical approval being in place; adoption by the NIHR network; collaborations with 11 UK centres to date; and relevant clinical parameters already collected by centres for audit. We have currently acquired 1700 subjects data from 2 centres (Supporting Information 1). We shall additionally acquire ~4000 non-stroke, and follow-up stroke, images, to facilitate automated anomaly detection (of which we have already acquired 1750). Finally, we have agreed in principle to acquire data from the IST-3 thrombolysis trial9 (n=3000), this being comprised exclusively of cases where there was treatment-decision uncertainty, and includes patients not thrombolysed. 2. Images will be pre-processed using an automated, in-house pipe-line of joining, normalization, smoothing, and segmentation, that we have already partly developed (Supporting Information 2). 3. We shall optimize our existing model by exploring CT image features most relevant to SICH/outcome prediction, using a range of machine-learning methods our group are experienced with11. This will involve purely data-driven approaches (e.g. support-vector machines), and hypothesis-assisted methods (e.g. delineation of acute ischaemia extent - this being the strongest currently-recognised imaging predictor of SICH13, yet poorly quantified). Our group have, to date, demonstrated that whole-brain high-dimensional information can be one useful predictor10; used feature-reduction to identify novel imaging patterns that predict SICH; and begun development of an automated anomaly detector (Supporting Information 3,4). Current SICH-predictive methods have validation accuracies of ~65%5,6, whereas our provisional model achieved accuracy of 74%. 4. The optimized model will be validated, and compared with current prognostic scores, e.g. SEDAN, HAT5,6. For this we shall take a cohort of 1000 subjects, from several centres that did not contribute data for model optimization. This step will entail review of scans by three neuroradiologists, since many prognostic scores utilise radiology measures related to size of infarction (ASPECTS score) and middle cerebral artery density. Inter-rater reliability will be ascertained. 5. The model will be incorporated into a user-friendly, clinically-relevant decision-assist tool by a software engineer. The tool will be designed for doctors to input patient data, and combine automatically with CT data on existing CT viewing platforms (PACS). The software will output probability of SICH risk, and good functional outcome, with confidence intervals (Supporting Information 5); and will be compared to those of stroke patients, not receiving tPA, calculated from models based upon thrombolysis trials8,9. This will be displayed in a relevant way for doctors making treatment decisions, and will be transferred schematically to show patients on a tablet. A patient representative will help with the design of these aspects. 6. We shall conduct a prospective feasibility study of the software. We shall install our software in 4-5 collaborating centres, and encourage its use by stroke-clinicians and radiologists to assist thrombolysis-decisions over 10 months. The primary outcome will be rate of thrombolysis. We hypothesise that the software will increase, or at least maintain, thrombolysis rates compared to previous rates in the same centres. Secondary outcomes will be: 1) SICH rate; 2) SICH and outcome predictive accuracy compared to existing scores; 3) speed of thrombolysis decision-making; 4) clinician and patient/family feedback. 7. A sub-study will explore whether incorporation of hyperacute CT-perfusion data, using multimodal machine-learning methods, can enhance SICH and function prediction. We expect to acquire ~1000 CT-perfusion data from our current collaborating centres. Increasing evidence suggests that advanced imaging can enhance prediction of outcomes after thrombolysis14, and estimate time of onset when this is unknown2. Methods developed in the earlier part of the study will be transferred to develop novel, assumption-free analyses of CT-perfusion images.

Lay abstract: The most important current treatment for stroke is an emergency, clot-busting drug called thrombolysis, that reduces dependency by a third, yet is only administered to roughly 10% of stroke patients. One reason why it is not used more commonly, is because of doctors' concerns over the main side-effect of thrombolysis - brain haemorrhage - that often results in significant deterioration or death, and occurs in 6% of treated patients. If we can develop a technique that helps doctors select patients for treatment, then this will encourage doctors to treat more patients with thrombolysis, whilst reducing harm. We are currently developing software that can predict brain haemorrhage, or good outcome, in emergency stroke patients receiving thrombolysis. A preliminary study showed that our method outperformed existing prognostic methods, increasing accuracy from 63% to 74%. Our group has also been successful in developing a similar software for Alzheimer s disease prediction. We now intend to enhance our technique, pushing accuracy higher, while testing for reliability across one of the largest stroke-thrombolysis databases to date. We shall incorporate our optimized algorithm into intuitive and accessible software that will help with both emergency-treatment decisions, and communication of these decisions to patients and family. The software utilises novel &ldquo;machine learning&rdquo; - by which a computer identifies complex patterns of brain scans, that are more strongly related to haemorrhage risk, or outcomes, than currently recognized. Existing prediction methods for stroke thrombolysis either do not take into account brain scan information; or where they do, rely upon a doctor s interpretation of brain scans. By contrast, our software can extract patterns from brain scans that are not observable to the human eye, yet are optimal for risk prediction, and perform this automatically without inaccuracies. The software will learn over time &ndash; data from new patients refining predictions for future patients. It will help also determine the safety of treating patients when they present with uncertain times of stroke onset (25% of cases), who are not currently treated. The software will also use methods that can be adapted to advanced brain scans, which although not used routinely at present, are likely to be so over the coming decade. The project s stages are: 1. Discovering those features of CT brain scans, that in combination with patient characteristics, best predict haemorrhage, and good outcome, after thrombolysis, using a dataset of ~9000. 2. Validating the model in 1000 further patients; and comparing this with best-available methods. 3. Integrating our software with NHS computers, so that brain scans and clinical data can be rapidly combined, before displaying risk estimates of potential treatment decisions to doctors. These numbers will also be depicted graphically, allowing for presentation to patients and family at the bedside, with a computer tablet. A patient representative will gain feedback from patients, family and doctors, as to how stroke emergency-treatment decisions are currently conducted, and their preferences for software design. 4. Trialling the software in 4-5 stroke centres over 10 months, to assess whether it increases thrombolysis use, haemorrhage prediction, and patient-family experience.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: STROKE