Implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning

M. Field*, S. Vinod, N. Aherne, M. Carolan, A. Dekker, G. Delaney, S. Greenham, E. Hau, J. Lehmann, J. Ludbrook, A. Miller, A. Rezo, J. Selvaraj, J. Sykes, L. Holloway, D. Thwaites

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Web of Science)
35 Downloads (Pure)

Abstract

Introduction There is significant potential to analyse and model routinely collected data for radiotherapy patients to provide evidence to support clinical decisions, particularly where clinical trials evidence is limited or non-existent. However, in practice there are administrative, ethical, technical, logistical and legislative barriers to having coordinated data analysis platforms across radiation oncology centres. Methods A distributed learning network of computer systems is presented, with software tools to extract and report on oncology data and to enable statistical model development. A distributed or federated learning approach keeps data in the local centre, but models are developed from the entire cohort. Results The feasibility of this approach is demonstrated across six Australian oncology centres, using routinely collected lung cancer data from oncology information systems. The infrastructure was used to validate and develop machine learning for model-based clinical decision support and for one centre to assess patient eligibility criteria for two major lung cancer radiotherapy clinical trials (RTOG-9410, RTOG-0617). External validation of a 2-year overall survival model for non-small cell lung cancer (NSCLC) gave an AUC of 0.65 and C-index of 0.62 across the network. For one centre, 65% of Stage III NSCLC patients did not meet eligibility criteria for either of the two practice-changing clinical trials, and these patients had poorer survival than eligible patients (10.6 m vs. 15.8 m, P = 0.024). Conclusion Population-based studies on routine data are possible using a distributed learning approach. This has the potential for decision support models for patients for whom supporting clinical trial evidence is not applicable.
Original languageEnglish
Pages (from-to)627-636
Number of pages10
JournalJournal of Medical Imaging and Radiation Oncology
Volume65
Issue number5
DOIs
Publication statusPublished - 1 Aug 2021

Keywords

  • artificial intelligence
  • decision support systems
  • distributed learning
  • federated learning
  • radiation oncology
  • LUNG-CANCER
  • SURVIVAL PREDICTION
  • 2-YEAR SURVIVAL
  • CLINICAL-TRIALS
  • MODEL
  • RADIOTHERAPY
  • CONCURRENT
  • CARE

Cite this