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A distributed data mining network infrastructure for Australian radiotherapy decision support

Routine electronic storage of medical records and imaging is becoming standard practice in radiotherapy. There is immense potential to utilise this increasingly diverse data resource as an evidence base for decision support systems for cancer prognosis and subsequent personalised treatment decisions.

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A distributed data mining network infrastructure for Australian radiotherapy decision support

  1. 1. A distributed data mining network infrastructure for Australian radiotherapy decision support Matthew Field; Mohamed Samir Barakat; Michael Bailey; Martin Carolan; Andre Dekker; Geoff Delaney; Gary Goozee; Lois Holloway; Joerg Lehmann; Tim Lustberg; Johan van Soest; Jonathan Sykes; Sean Walsh; David Thwaites.
  2. 2. Introduction • There is a need for decision support systems (DSS) in radiation oncology. • Technology is setting a rapid pace and decisions are becoming more complex. • Are clinical trials an accurate representation of clinical outcomes in practice? • Routine clinical and imaging data are clinic specific. Source: Philippe Lambin et. al. (2015): Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine, Acta Oncologica.
  3. 3. Introduction • Prospective study showed high prognostic accuracy (survival and toxicity) from DSS model when compared to a radiation oncologist prediction1. • Challenges: – Patient confidentiality. – Model over-fitting. – Data standardization. • Solution: Distributed learning network between clinics. – Share only prediction model parameters. – Model training and validation on broader sources of data. – Semantic web nomenclature transform. 1Cary Oberije et. al., (2014). A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: A step toward individualized care and shared decision making, Radiotherapy and Oncology.
  4. 4. Computer Assisted Theragnostics • Collaboration with MAASTRO clinic (Maastricht, Netherlands). • Constructing a de-identified and standardized research database at each participating NSW clinic (Liverpool, Illawarra, Westmead, Newcastle). Updating over time. • Treatment site specific, eg. Lung cancer prognosis of 2 year survival. – Tumour volume – FEV1 – WHO-PS – Gender – Age – …..
  5. 5. Example: Liverpool data set • 4686 patients with lung cancer diagnosis. • → 1750 with cancer Stage I-IIB. • → 522 with curative dose (>45gy). • → 282 with tumour volume.
  6. 6. Prediction model • Example: Using a SVM model (linear decision boundary). • Training data set: Liverpool, Testing data set: Illawarra. – AUC: 0.78 • Training data set: Illawarra, Testing data set: Liverpool. – AUC: 0.63 • Balance of data sets. Illawarra had 82% of cases in one class, Liverpool 50%. • In distributed learning we can achieve the same prediction performance as a centralised data set. Note: Randomly predicting survival would achieve an area under curve (AUC) of 0.50
  7. 7. Example of decision support • Potential for personalised treatment regime decisions to be based on the weight of evidence from routine clinical practice. 0 1 2 3 4 5 6 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 31% 16% p = 0.098 Survival time in years Probabilityofsurvival Kaplan Meier curve for poor prognosis group (<10% chance) Radical dose N=29 Non-radical dose N=69 0 1 2 3 4 5 6 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 71% 34% p < 0.001 Survival time in years Probabilityofsurvival Kaplan Meier curve for good prognosis group (>50% chance) Radical dose N=74 Non-radical dose N=29
  8. 8. Distributed Learning Network
  9. 9. Simulation Results Centralized Distributed • Example: Synthetic binary classification. • Training time per 100 iterations. – Distributed: 394s – Centralized: 8s
  10. 10. Conclusion • Preparing decision support systems based on routine clinical data (similar to www.predictcancer.org). • Tested a prediction model sharing system between three clinics. • Data QA: Births/deaths registry queries. • Adding tumour image features to prediction model. • Composing additional prediction models for comparison, eg. Decision trees and Bayesian networks.

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Routine electronic storage of medical records and imaging is becoming standard practice in radiotherapy. There is immense potential to utilise this increasingly diverse data resource as an evidence base for decision support systems for cancer prognosis and subsequent personalised treatment decisions.

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