Prediction of Lung Nodule Progression with an Uncertainty-Aware Hierarchical Probabilistic Network.

ABSTRACT: Predicting whether a lung nodule will grow, remain stable or regress over time, especially early in its follow-up, would help doctors prescribe personalized treatments and better surgical planning. However, the multifactorial nature of lung tumour progression hampers the identification of growth patterns. In this work, we propose a deep hierarchical generative and probabilistic network that, given an initial image of the nodule, predicts whether it will grow, quantifies its future size and provides its expected semantic appearance at a future time. Unlike previous solutions, our approach also estimates the uncertainty in the predictions from the intrinsic noise in medical images and the inter-observer variability in the annotations. The evaluation of this method on an independent test set

SUBMITTER: Rafael-Palou X 

PROVIDER: S-EPMC9689366 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature