DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-...

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Bibliographic Details
Main Authors: Won Sang Shim, Kwangil Yim, Tae-Jung Kim, Yeoun Eun Sung, Gyeongyun Lee, Ji Hyung Hong, Sang Hoon Chun, Seoree Kim, Ho Jung An, Sae Jung Na, Jae Jun Kim, Mi Hyoung Moon, Seok Whan Moon, Sungsoo Park, Soon Auck Hong, Yoon Ho Ko
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Cancers
Subjects:
deep learning
lung adenocarcinoma
pathology image
prognosis
Online Access:https://www.mdpi.com/2072-6694/13/13/3308

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https://www.mdpi.com/2072-6694/13/13/3308

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