Use of a Commercially Available Deep Learning Algorithm to Measure the Solid Portions of Lung Cancer Manifesting as Subsolid Lesions at CT: Comparisons with Radiologists and Invasive Component Size at Pathologic Examination-Reference-Cited by-同舟云学术

Use of a Commercially Available Deep Learning Algorithm to Measure the Solid Portions of Lung Cancer Manifesting as Subsolid Lesions at CT: Comparisons with Radiologists and Invasive Component Size at Pathologic Examination

Published:2021-04 Issue:1 Volume:299 Page:202-210
ISSN:0033-8419
Container-title:Radiology
language:en
Short-container-title:Radiology

Author:

Ahn Yura¹^ORCID,Lee Sang Min¹^ORCID,Noh Han Na¹^ORCID,Kim Wooil¹^ORCID,Choe Jooae¹^ORCID,Do Kyung-Hyun¹^ORCID,Seo Joon Beom¹^ORCID

Affiliation:

1. From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea.

Funder

Korea Health Industry Development Institute

Publisher

Radiological Society of North America (RSNA)

Subject

Radiology Nuclear Medicine and imaging

Link

http://pubs.rsna.org/doi/pdf/10.1148/radiol.2021202803

Reference15 articles.

1. The IASLC Lung Cancer Staging Project: Proposals for Coding T Categories for Subsolid Nodules and Assessment of Tumor Size in Part-Solid Tumors in the Forthcoming Eighth Edition of the TNM Classification of Lung Cancer

2. Correlation between the Size of the Solid Component on Thin-Section CT and the Invasive Component on Pathology in Small Lung Adenocarcinomas Manifesting as Ground-Glass Nodules

3. Distinct Clinicopathologic Characteristics and Prognosis Based on the Presence of Ground Glass Opacity Component in Clinical Stage IA Lung Adenocarcinoma