Deep learning for semi-automated unidirectional measurement of lung tumor size in CT.

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 evaluate

SUBMITTER: Woo M 

PROVIDER: S-EPMC8220702 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature