Journal article
Implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning
Journal of medical imaging and radiation oncology, Vol.65(5), pp.627-636
02/08/2021
PMID: 34331748
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Source: InCites
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.
Details
- Title
- Implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning
- Creators
- Matthew Field - UNSW SydneyShalini Vinod - UNSW SydneyNoel Aherne - University of New South WalesMartin Carolan - Illawarra Cancer Care Centre; Wollongong New South Wales AustraliaAndre Dekker - Maastro ClinicGeoff Delaney - UNSW SydneyStuart Greenham - Mid North Coast Cancer Institute, Coffs HarbourEric Hau - University of SydneyJoerg Lehmann - University of SydneyJoanna Ludbrook - Calvary Mater Newcastle HospitalAndrew Miller - Illawarra Cancer Care Centre; Wollongong New South Wales AustraliaAngela Rezo - Canberra Health ServicesJothybasu Selvaraj - UNSW SydneyJonathan Sykes - University of SydneyLois Holloway - UNSW SydneyDavid Thwaites - University of Sydney
- Publication Details
- Journal of medical imaging and radiation oncology, Vol.65(5), pp.627-636
- Publisher
- Wiley
- Number of pages
- 10
- Grant note
- Liverpool Cancer Therapy Centre RG14/11 / NSW Office of Health and Medical Research (OHMR) Bioinformatics grant Macarthur Cancer Therapy Centre Blacktown Hospital Illawarra Cancer Care Centre (Wollongong Hospital) Westmead Hospital Sydney West Radiation Oncology Network 2019/ECF004 / Cancer Institute NSW Early Career Fellowship Hunter Cancer Alliance grant
- Identifiers
- 991013093609702368
- Copyright
- © 2021 The Royal Australian and New Zealand College of Radiologists.
- Academic Unit
- Coffs Harbour Campus Administration
- Language
- English
- Resource Type
- Journal article