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

Autor: Woo M; Department of Public Health Sciences, Clemson University, 501 Edwards Hall, Clemson, SC, 29634, USA., Devane AM; Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA., Lowe SC; Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA., Lowther EL; Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA., Gimbel RW; Department of Public Health Sciences, Clemson University, 501 Edwards Hall, Clemson, SC, 29634, USA. rgimbel@clemson.edu.
Jazyk: angličtina
Zdroj: Cancer imaging : the official publication of the International Cancer Imaging Society [Cancer Imaging] 2021 Jun 23; Vol. 21 (1), pp. 43. Date of Electronic Publication: 2021 Jun 23.
DOI: 10.1186/s40644-021-00413-7
Abstrakt: 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 < 0.05.
Results: The DL algorithm yielded ICC score of 0.959 with human radiologist. Bland-Altman plotting suggested 240 (98.4 %) measurements realized within the upper and lower limits of agreement (LOA). Some measurements outside the LOA revealed difference in clinical reasoning between DL algorithm and human radiologist. Overall, the algorithm marginally overestimated the size of lesion by 2.97 % compared to human radiologists. Further investigation indicated tumor characteristics may be associated with the DL algorithm's diagnostic behavior of over or underestimating the lesion size compared to human radiologist.
Conclusions: The DL algorithm for unidirectional measurement of lung tumor size demonstrated excellent agreement with human radiologist.
Databáze: MEDLINE
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