These differences apply even after the doctor has seen the patient
Mathematical prediction models are better than doctors at predicting the outcomes and responses of lung cancer patients to treatment, according to new research presented April 20, 2013, at the 2nd Forum of the European Society for Radiotherapy and Oncology (ESTRO).
These differences apply even after the doctor has seen the patient, which can provide extra information, and knows what the treatment plan and radiation dose will be.
“The number of treatment options available for lung cancer patients are increasing, as well as the amount of information available to the individual patient. It is evident that this will complicate the task of the doctor in the future,” said the presenter, Dr Cary Oberije, a postdoctoral researcher at the MAASTRO Clinic, Maastricht University Medical Center, Maastricht, The Netherlands. “If models based on patient, tumour and treatment characteristics already out-perform the doctors, then it is unethical to make treatment decisions based solely on the doctors’ opinions. We believe models should be implemented in clinical practice to guide decisions.”
Dr Oberije and her colleagues in The Netherlands used mathematical prediction models that had already been tested and published. The models use information from previous patients to create a statistical formula that can be used to predict the probability of outcome and responses to treatment using radiotherapy with or without chemotherapy for future patients.
Having obtained predictions from the mathematical models, the researchers asked experienced radiation oncologists to predict the likelihood of lung cancer patients surviving for two years, or suffering from shortness of breath (dyspnea) and difficulty swallowing (dysphagia) at two points in time: 1) after they had seen the patient for the first time, and 2) after the treatment plan was made. At the first time point, the doctors predicted two-year survival for 121 patients, dyspnea for 139 and dysphagia for 146 patients. At the second time point, predictions were only available for 35, 39 and 41 patients respectively.
For all three predictions and at both time points, the mathematical models substantially outperformed the doctors’ predictions, with the doctors’ predictions being little better than those expected by chance.
The researchers plotted the results on a special graph on which the area below the plotted line is used for measuring the accuracy of predictions; 1 represents a perfect prediction, while 0.5 represents predictions that were right in 50% of cases, i.e. the same as chance. They found that the model predictions at the first time point were 0.71 for two-year survival, 0.76 for dyspnea and 0.72 for dysphagia. In contrast, the doctors’ predictions were 0.56, 0.59 and 0.52 respectively.
The models had a better positive predictive value (PPV) – a measure of the proportion of patients who were correctly assessed as being at risk of dying within two years or suffering from dyspnea and dysphagia – than the doctors. The negative predictive value (NPV) – a measure of the proportion of patients that would not die within two years or suffer from dyspnea and dysphagia – was comparable between the models and the doctors.
“This indicates that the models were better at identifying high risk patients that have a very low chance of surviving or a very high chance of developing severe dyspnea or dysphagia,” said Dr Oberije.
The researchers say that it is important that further research is carried out into how prediction models can be integrated into standard clinical care. In addition, further improvement of the models by incorporating all the latest advances in areas such as genetics, imaging and other factors, is important. This will make it possible to tailor treatment to the individual patient’s biological make-up and tumour type
“In our opinion, individualised treatment can only succeed if prediction models are used in clinical practice. We have shown that current models already outperform doctors. Therefore, this study can be used as a strong argument in favour of using prediction models and changing current clinical practice,” said Dr Oberije.
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