Oncotarget

Clinical Research Papers:

Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

Tim Lustberg _, Michael Bailey, David I. Thwaites, Alexis Miller, Martin Carolan, Lois Holloway, Emmanuel Rios Velazquez, Frank Hoebers and Andre Dekker

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Oncotarget. 2016; 7:37288-37296. https://doi.org/10.18632/oncotarget.8755

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Abstract

Tim Lustberg1, Michael Bailey2,3,5,6, David I. Thwaites4, Alexis Miller2,3, Martin Carolan2,5,6, Lois Holloway4,6,7,8, Emmanuel Rios Velazquez9, Frank Hoebers1 and Andre Dekker1

1 Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands

2 Illawarra Cancer Care Centre, Illawarra Shoalhaven Local Health District, Wollongong, Australia

3 Centre for Oncology Informatics, University of Wollongong, Wollongong, Australia

4 Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia

5 Illawarra Health and Medical Research Institute, Wollongong, Australia

6 Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia

7 South Western Clinical School, University of New South Wales, Sydney, Australia

8 Ingham Institute and Liverpool and Macarthur Cancer Therapy Centres, Liverpool, Australia

9 Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA

Correspondence to:

Tim Lustberg, email:

Keywords: larynx; survival prediction; rapid learning; model validation

Received: November 26, 2015 Accepted: March 28, 2016 Published: April 15, 2016

Abstract

Background and Purpose:

To improve quality and personalization of oncology health care, decision aid tools are needed to advise physicians and patients. The aim of this work is to demonstrate the clinical relevance of a survival prediction model as a first step to multi institutional rapid learning and compare this to a clinical trial dataset.

Materials and Methods:

Data extraction and mining tools were used to collect uncurated input parameters from Illawarra Cancer Care Centre’s (clinical cohort) oncology information system. Prognosis categories previously established from the Maastricht Radiation Oncology (training cohort) dataset, were applied to the clinical cohort and the radiotherapy only arm of the RTOG-9111 (trial cohort).

Results:

Data mining identified 125 laryngeal carcinoma patients, ending up with 52 patients in the clinical cohort who were eligible to be evaluated by the model to predict 2-year survival and 177 for the trial cohort. The model was able to classify patients and predict survival in the clinical cohort, but for the trial cohort it failed to do so.

Conclusions:

The technical infrastructure and model is able to support the prognosis prediction of laryngeal carcinoma patients in a clinical cohort. The model does not perform well for the highly selective patient population in the trial cohort.


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