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.

2. 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.

3. 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.

4. 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.

5. 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
– …..

6. 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.

7. 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

8. 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

9. Distributed Learning Network

10. Simulation Results
Centralized
Distributed
• Example: Synthetic binary classification.
• Training time per 100 iterations.
– Distributed: 394s
– Centralized: 8s

11. 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.