  Early detection and quantification of tumour growth would help clinicians to
prescribe more accurate treatments and provide better surgical planning.
However, the multifactorial and heterogeneous nature of lung tumour progression
hampers identification of growth patterns. In this study, we present a novel
method based on a deep hierarchical generative and probabilistic framework
that, according to radiological guidelines, predicts tumour growth, quantifies
its size and provides a semantic appearance of the future nodule. Unlike
previous deterministic solutions, the generative characteristic of our approach
also allows us to estimate the uncertainty in the predictions, especially
important for complex and doubtful cases. Results of evaluating this method on
an independent test set reported a tumour growth balanced accuracy of 74%, a
tumour growth size MAE of 1.77 mm and a tumour segmentation Dice score of 78%.
These surpassed the performances of equivalent deterministic and alternative
generative solutions (i.e. probabilistic U-Net, Bayesian test dropout and
Pix2Pix GAN) confirming the suitability of our approach.
