A survey of pattern classification-based methods for predicting survival time of lung cancer patients

Cancer prognosis is an important clinical practice in cancer medicine and is an important factor in developing personalized medicine. But till now, researches focus on developing recurrence risk indices that tell poor or good survival for given cancer patients. These indices, however, are insufficient and elusive in the clinic. In this paper, we propose to predict survival time of cancer patients using pattern recognition approach, which is more informative and favorable to clinicians and patients in clinical practice. We conduct an extensive survey of pattern recognition methods for the prognosis based on real-world benchmark microarray data sets. In particular, various types of data preprocessing methods and various types of classification models are introduced and examined for predicting survival time of lung cancer based on gene expression. The experimental results show that pattern recognition method can provide a feasible and efficient way to predict survival time of cancer patients. It is expected that the pattern classification-based strategy opens a new paradigm of cancer prognosis for predicting survival time of cancer patients in the clinic.