A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making.
BACKGROUND
Decision Support Systems, based on statistical prediction models, have the potential to change the way medicine is being practiced, but their application is currently hampered by the astonishing lack of impact studies. Showing the theoretical benefit of using these models could stimulate conductance of such studies. In addition, it would pave the way for developing more advanced models, based on genomics, proteomics and imaging information, to further improve the performance of the models.


OBJECTIVE
In this prospective single-center study, previously developed and validated statistical models were used to predict the two-year survival (2yrS), dyspnea (DPN), and dysphagia (DPH) outcomes for lung cancer patients treated with chemo radiation. These predictions were compared to probabilities provided by doctors and guideline-based recommendations currently used. We hypothesized that model predictions would significantly outperform predictions from doctors.


METHODS
Experienced radiation oncologists (ROs) predicted all outcomes at two timepoints: (1) after the first consultation of the patient, and (2) after the radiation treatment plan was made. Differences in the performances of doctors and models were assessed using Area Under the Curve (AUC) analysis.


RESULTS
A total number of 155 patients were included. At timepoint #1 the differences in AUCs between the ROs and the models were 0.15, 0.17, and 0.20 (for 2yrS, DPN, and DPH, respectively), with p-values of 0.02, 0.07, and 0.03. Comparable differences at timepoint #2 were not statistically significant due to the limited number of patients. Comparison to guideline-based recommendations also favored the models.


CONCLUSIONS
The models substantially outperformed ROs' predictions and guideline-based recommendations currently used in clinical practice. Identification of risk groups on the basis of the models facilitates individualized treatment, and should be further investigated in clinical impact studies.
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands. Electronic address: cary.oberije@maastro.nl.
Cary
C
Oberije
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Georgi
G
Nalbantov
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Andre
A
Dekker
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Liesbeth
L
Boersma
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Jacques
J
Borger
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Bart
B
Reymen
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Angela
A
van Baardwijk
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Rinus
R
Wanders
Department of Radiation Oncology, University Hospital Leuven/KU Leuven, Belgium.
Dirk
D
De Ruysscher
Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.
Ewout
E
Steyerberg
Department of Pulmonology, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Anne-Marie
AM
Dingemans
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Philippe
P
Lambin
D000368:Aged; D019540:Area Under Curve; D059248:Chemoradiotherapy / Q000009:adverse effects*; D002983:Clinical Competence; D003657:Decision Making; D003661:Decision Support Techniques; D003680:Deglutition Disorders / Q000209:etiology*; D004417:Dyspnea / Q000209:etiology*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D057285:Precision Medicine; D011336:Probability; D011446:Prospective Studies; D011832:Radiation Injuries / Q000209:etiology*; D016896:Treatment Outcome
Lung cancer; Prediction models