An Overview of Real-World Data Infrastructure for Cancer Research

AIMS

There is increasing interest in the opportunities offered by Real World Data (RWD) to provide evidence where clinical trial data does not exist, but access to appropriate data sources is frequently cited as a barrier to RWD research. This paper discusses current RWD resources and how they can be accessed for cancer research.

MATERIALS AND METHODS

There has been significant progress on facilitating RWD access in the last few years across a range of scales, from local hospital research databases, through regional care records and national repositories, to the impact of federated learning approaches on internationally collaborative studies. We use a series of case studies, principally from the UK, to illustrate how RWD can be accessed for research and healthcare improvement at each of these scales.

RESULTS

For each example we discuss infrastructure and governance requirements with the aim of encouraging further work in this space that will help to fill evidence gaps in oncology.

CONCLUSION

There are challenges, but real-world data research across a range of scales is already a reality. Taking advantage of the current generation of data sources requires researchers to carefully define their research question and the scale at which it would be best addressed.

Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non-small Cell Lung Cancer Using Real-World Data

AIMS

The objective of this study was to develop a two-year overall survival model for inoperable stage I-III non-small cell lung cancer (NSCLC) patients using routine radiation oncology data over a federated (distributed) learning network and evaluate the potential of decision support for curative versus palliative radiotherapy.

METHODS

A federated infrastructure of data extraction, de-identification, standardisation, image analysis, and modelling was installed for seven clinics to obtain clinical and imaging features and survival information for patients treated in 2011-2019. A logistic regression model was trained for the 2011-2016 curative patient cohort and validated for the 2017-2019 cohort. Features were selected with univariate and model-based analysis and optimised using bootstrapping. System performance was assessed by the receiver operating characteristic (ROC) and corresponding area under curve (AUC), C-index, calibration metrics and Kaplan-Meier survival curves, with risk groups defined by model probability quartiles. Decision support was evaluated using a case-control analysis using propensity matching between treatment groups.

RESULTS

1655 patient datasets were included. The overall model AUC was 0.68. Fifty-eight percent of patients treated with palliative radiotherapy had a low-to-moderate risk prediction according to the model, with survival times not significantly different (p = 0.87 and 0.061) from patients treated with curative radiotherapy classified as high-risk by the model. When survival was simulated by risk group and model-indicated treatment, there was an estimated 11% increase in survival rate at two years (p < 0.01).

CONCLUSION

Federated learning over multiple institution data can be used to develop and validate decision support systems for lung cancer while quantifying the potential impact of their use in practice. This paves the way for personalised medicine, where decisions can be based more closely on individual patient details from routine care.

A big data-driven eHealth approach to prevent, detect, and reduce atherosclerotic cardiovascular disease burden

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Netherlands Organisation for Scientific Research (NWO)

eHealth is a promising tool to support citizens and patients to reduce their risk of future atherosclerotic vascular disease (ASCVD). Currently, many eHealth initiatives are developed, however their use is limited. To improve the adoption of eHealth initiatives, personalization is needed according to preferences and understanding of the individual. Together with stakeholders, we used the design thinking method to create a dream user-scenario for an eHealth solution to improve cardiovascular outcomes. This point-of-view shows the essential concepts of such an eHealth initiative and focuses on scientific solutions and the most important challenges faced during its development.

In the user-scenario a federated data infrastructure collects distributed medical and non-medical data from different organizations, while ensuring maximal privacy. This data, that is currently not available, is used in a screening model to identify citizens with an increased risk for ASCVD. Citizens at risk are offered the use of an eCoach, which is connected to a counterfactual prediction model to calculate a residual risk for hypothetical interventions. This risk is visualized in a personalized way to support the shared decision making process of personalized lifestyle and healthcare goals. eHealth modules, integrated in the eCoach, encourages the patient in achieving these goals. Relevant outcomes are monitored and are used to adjust the intensity of the modules and follow-up accordingly. To realize this federated data infrastructure, an ethical and legal framework should be developed to ensue responsible data handling.

Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs)

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Prediction of survival is crucial for guiding patient-tailored treatment.

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Radiomics can be described as the next era of possibilities in precision medicine.

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Radiomics model had an inferior performance with no added predictive power to the clinical predictive model.

Study design

Retrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute.

Objective

This is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM).

Background data

Extracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression.

Methods

A study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model.

A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient.

Results

The clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability.