Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images

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.

Early Response Assessment in Lung Cancer Patients using Spatio-temporal CBCT Images

We report a model to predict patient's radiological response to curative radiation therapy (RT) for non-small-cell lung cancer (NSCLC). Cone-Beam Computed Tomography images acquired weekly during the six-week course of RT were contoured with the Gross Tumor Volume (GTV) by senior radiation oncologists for 53 patients (7 images per patient). Deformable registration of the images yielded six deformation fields for each pair of consecutive images per patient. Jacobian of a field provides a measure of local expansion/contraction and is used in our model. Delineations were compared post-registration to compute unchanged ($U$), newly grown ($G$), and reduced ($R$) regions within GTV. The mean Jacobian of these regions $\mu_U$, $\mu_G$ and $\mu_R$ are statistically compared and a response assessment model is proposed. A good response is hypothesized if $\mu_R < 1.0$, $\mu_R < \mu_U$, and $\mu_G < \mu_U$. For early prediction of post-treatment response, first, three weeks' images are used. Our model predicted clinical response with a precision of $74\%$. Using reduction in CT numbers (CTN) and percentage GTV reduction as features in logistic regression, yielded an area-under-curve of 0.65 with p=0.005. Combining logistic regression model with the proposed hypothesis yielded an odds ratio of 20.0 (p=0.0).