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


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.
Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Tim
T
Lustberg
Illawarra Cancer Care Centre, Illawarra Shoalhaven Local Health District, Wollongong, Australia.|Centre for Oncology Informatics, University of Wollongong, Wollongong, Australia.|Illawarra Health and Medical Research Institute, Wollongong, Australia.|Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.
Michael
M
Bailey
Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia.
David I
DI
Thwaites
Illawarra Cancer Care Centre, Illawarra Shoalhaven Local Health District, Wollongong, Australia.|Centre for Oncology Informatics, University of Wollongong, Wollongong, Australia.
Alexis
A
Miller
Illawarra Cancer Care Centre, Illawarra Shoalhaven Local Health District, Wollongong, Australia.|Illawarra Health and Medical Research Institute, Wollongong, Australia.|Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.
Martin
M
Carolan
Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia.|Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.|South Western Clinical School, University of New South Wales, Sydney, Australia.|Ingham Institute and Liverpool and Macarthur Cancer Therapy Centres, Liverpool, Australia.
Lois
L
Holloway
Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA.
Emmanuel
E
Rios Velazquez
Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Frank
F
Hoebers
Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Andre
A
Dekker
D000066491:Clinical Decision-Making / Q000379:methods*; D015331:Cohort Studies; D066264:Datasets as Topic; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D007822:Laryngeal Neoplasms / Q000401:mortality* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D011379:Prognosis
larynx; model validation; rapid learning; survival prediction