Lung Cancer: Models Predict Treatment Outcome

Photo: Doctor viewing X-ray

Mathematical prediction models are better than doctors at predicting the outcomes and responses of lung cancer patients to treatment;
© Gabrysiak

Mathematical prediction models are better than doctors at predicting the outcomes and responses of lung cancer patients to treatment. This applies even after the doctor has seen the patient and knows what 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," says Doctor Cary Oberije, from Maastricht University Medical Center. "If models based on patient, tumor 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."

She 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.

"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," says 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 tumor type.

"In our opinion, individualized 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 favor of using prediction models and changing current clinical practice," says Oberije.; Source: European Society for Radiotherapy and Oncology