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

Abstract

Summary 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.

Predicting 2-year survival in stage I-III non-small cell lung cancer: the development and validation of a scoring system from an Australian cohort

“…We plan to develop and validate a survival prediction model in patients with Stage I-III NSCLC patients undergoing radiotherapy in a larger cohort of patients with distributed learning across multiple centres using the AusCAT network [ 45 ]. The factors found to be significant in this work will be considered alongside newer variables.…”

Open-source distributed learning validation for a larynx cancer survival model following radiotherapy

“…All larynx cancer patients receiving radiotherapy from January 2005 to December 2018 were made available by Odense University Hospital, Denmark (Odense) and The Christie NHS Foundation Trust, Manchester, United Kingdom (Christie), for the opensource distributed learning platform developed in AusCAT, the Australian Computer Assisted Theragnostics network [14,15]. These patients were used to externally validate the previously published larynx overall survival model from MAASTRO by Egelmeer et al [12].…”

Minimum data elements for the Australian Particle Therapy Clinical Quality Registry

“…The Australian Computer Assisted Theragnostics (AusCAT) program and network can provide support to develop this work. 15,16 TROG aims to establish the infrastructure in conjunction with national PT groups, whose members have the experience and technical know-how to establish the extended national registry envisioned.…”