Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.

ABSTRACT:

Background

Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.

Methods and findings

We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range

SUBMITTER: Hosny A 

PROVIDER: S-EPMC6269088 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature