Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans

The survival rate of cancer patients depends on the type of cancer, the treatments that the patient has undergone, and the severity of the cancer when the treatment was initiated. In this study, we consider adenocarcinoma, a type of lung cancer detected in chest Computed Tomography (CT) scans on the entire lung, and images that are “sliced” versions of the scans as one progresses along the thoracic region. Typically, one extracts 2D features from the “sliced” images to achieve various types of classification. In this paper, we show that the 2D features, in and of themselves, can be used to also yield fairly reasonable predictions of the patients’ survival rates if the underlying problem is treated as a regression problem. However, the fundamental contribution of this paper is that we have discovered that there is a strong correlation between the shapes of the 2D images at successive layers of the scans and these survival rates. One can extract features from these successive images and augment the basic features used in a 2D classification system. These features involve the area at the level, and the mean area along the z-axis. By incorporating additional shape-based features, the error involved in the prediction decreases drastically – by almost an order of magnitude. The results we have obtained deal with the cancer treatments done on 60 patients (Understandably, it is extremely difficult to obtain training and testing data for this problem domain! Thus, both authors gratefully acknowledge the help given by Drs. Thornhill and Inacio, from the University of Ottawa, in providing us with domain knowledge and expertise for understanding and analyzing the publicly-available dataset.) at varying levels of severity, and with a spectrum of survival rates. For patients who survived up to 24 months, the average relative error is as low as 9%, which is very significant.