Radiomics Based Bayesian Inversion Method for Prediction of Cancer and Pathological Stage.

ABSTRACT:

Objective

To develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer.

Methods

While the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. A joint likelihood function incorporating diagnostic radiomic features is formulated which c

SUBMITTER: Shakir H 

PROVIDER: S-EPMC8428789 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature