Deep learning for semi-automated unidirectional measurement of lung tumor size in CT
Description
Abstract Background Performing Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and consistent lesion measurement. Purpose The aim of this study is to develop and evaluate deep learning (DL) algorithm for semi-automated unidirectional CT measurement of lung lesions. Methods This retrospective study included 1617 lung CT images from 8 publicly open datasets. A convolutional neural network was trained using 1373 training and validation images annotated by two radiologists. Performance of the DL algorithm was evaluated 244 test images annotated by one radiologist. DL algorithm’s measurement consistency with human radiologist was evaluated using Intraclass Correlation Coefficient (ICC) and Bland-Altman plotting. Bonferroni’s method was used to analyze difference in their diagnostic behavior, attributed by tumor characteristics. Statistical significance was set at p
Publication Date
1-1-2021
Publisher
figshare Academic Research System
DOI
10.6084/m9.figshare.c.5480668.v1
Document Type
Data Set
Recommended Citation
Woo, MinJae; Gimbel, Ronald W.; Lowe, Steven C.; Lowther, Ervin L; Devane, A. Michael (2021), "Deep learning for semi-automated unidirectional measurement of lung tumor size in CT", figshare Academic Research System, doi: 10.6084/m9.figshare.c.5480668.v1
https://doi.org/10.6084/m9.figshare.c.5480668.v1
Identifier
10.6084/m9.figshare.c.5480668.v1
Embargo Date
1-1-2021