Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography.

ABSTRACT:

Objectives

The subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT) images.

Methods

A dataset of 1222 patients with lung adenocarcinoma were retrospectively enrolled from three medical institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, invasive adenocarcinoma (IAC) with five predominant components were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category and 8-category. We mod

SUBMITTER: Wang C 

PROVIDER: S-EPMC8184655 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature