A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT.

ABSTRACT: Tumor staging in non-small cell lung cancer (NSCLC) is important for treatment and prognosis. Staging involves expert interpretation of imaging, which we aim to automate with deep learning (DL). We proposed a cascaded DL method comprised of two steps to classification between early- and advanced-stage NSCLC using pretreatment computed tomography. We developed and tested a DL model to classify between early- and advanced-stage using training (n = 90), validation (n = 8), and two test (n = 37, n = 26) cohorts obtained from the public domain. The first step adopted an autoencoder network to compress the imaging data into latent variables and the second step used the latent variable to classify the stages using the convolutional neural network (CNN). Other DL and machine learning-based approac

SUBMITTER: Choi J 

PROVIDER: S-EPMC8229025 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature