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Title: SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

Abstract

Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction and mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. Formore » example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.« less

Authors:
 [1];  [2]; ; ;  [3]; ;  [4]; ;  [5];  [6]; ; ;  [7]
  1. University of Sydney, Camperdown, Sydney (Australia)
  2. Ingham Institute, Sydney, NSW (Australia)
  3. Illawarra Cancer Care Centre, Wollongong, NSW (Australia)
  4. University of Sydney, Sydney, NSW (Australia)
  5. Liverpool Hospital, Liverpool, NSW (Australia)
  6. Maastro Clinic, Maastricht (Netherlands)
  7. MAASTRO Clinic, Maastricht (Netherlands)
Publication Date:
OSTI Identifier:
22545158
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; DATASETS; LARYNX; NEOPLASMS; PATIENTS; RADIOTHERAPY; SIMULATION; TRAINING

Citation Formats

Thwaites, D, Holloway, L, Bailey, M, Carolan, M, Miller, A, Barakat, S, Field, M, Delaney, G, Vinod, S, Dekker, A, Lustberg, T, Soest, J van, and Walsh, S. SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support. United States: N. p., 2015. Web. doi:10.1118/1.4924384.
Thwaites, D, Holloway, L, Bailey, M, Carolan, M, Miller, A, Barakat, S, Field, M, Delaney, G, Vinod, S, Dekker, A, Lustberg, T, Soest, J van, & Walsh, S. SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support. United States. doi:10.1118/1.4924384.
Thwaites, D, Holloway, L, Bailey, M, Carolan, M, Miller, A, Barakat, S, Field, M, Delaney, G, Vinod, S, Dekker, A, Lustberg, T, Soest, J van, and Walsh, S. Mon . "SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support". United States. doi:10.1118/1.4924384.
@article{osti_22545158,
title = {SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support},
author = {Thwaites, D and Holloway, L and Bailey, M and Carolan, M and Miller, A and Barakat, S and Field, M and Delaney, G and Vinod, S and Dekker, A and Lustberg, T and Soest, J van and Walsh, S},
abstractNote = {Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction and mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.},
doi = {10.1118/1.4924384},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2015},
month = {Mon Jun 15 00:00:00 EDT 2015}
}