Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images
This work addresses survival prediction for lung cancer patients, offering a domain-specific improvement in medical imaging analysis.
The authors tackled survival prediction for lung cancer patients by introducing a novel radiomic feature, Tumor Mass within GTV (TMG), which incorporates tumor heterogeneity from low-dose CBCT images, resulting in an improvement in the average concordance index from 0.47 to 0.64 compared to using GTV alone.
Prediction of survivability in a patient for tumor progression is useful to estimate the effectiveness of a treatment protocol. In our work, we present a model to take into account the heterogeneous nature of a tumor to predict survival. The tumor heterogeneity is measured in terms of its mass by combining information regarding the radiodensity obtained in images with the gross tumor volume (GTV). We propose a novel feature called Tumor Mass within a GTV (TMG), that improves the prediction of survivability, compared to existing models which use GTV. Weekly variation in TMG of a patient is computed from the image data and also estimated from a cell survivability model. The parameters obtained from the cell survivability model are indicatives of changes in TMG over the treatment period. We use these parameters along with other patient metadata to perform survival analysis and regression. Cox's Proportional Hazard survival regression was performed using these data. Significant improvement in the average concordance index from 0.47 to 0.64 was observed when TMG is used in the model instead of GTV. The experiments show that there is a difference in the treatment response in responsive and non-responsive patients and that the proposed method can be used to predict patient survivability.