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Japanese Journal of Radiology
Published in: Japanese Journal of Radiology 11/2020

01-11-2020 | Computed Tomography | Original Article

Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography

Authors: Takenori Kozuka, Yuko Matsukubo, Tomoya Kadoba, Teruyoshi Oda, Ayako Suzuki, Tomoko Hyodo, SungWoon Im, Hayato Kaida, Yukinobu Yagyu, Masakatsu Tsurusaki, Mitsuru Matsuki, Kazunari Ishii

Published in: Japanese Journal of Radiology | Issue 11/2020

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Abstract

Purpose

To evaluate the performance of a deep learning-based computer-aided diagnosis (CAD) system at detecting pulmonary nodules on CT by comparing radiologists’ readings with and without CAD.

Materials and methods

A total of 120 chest CT images were randomly selected from patients with suspected lung cancer. The gold standard of nodules ≥ 3 mm was established by a panel of three expert radiologists. Two less experienced radiologists read the images without and afterward with CAD system. Their reading times were recorded.

Results

The radiologists’ sensitivity increased from 20.9% to 38.0% with the introduction of CAD. The positive predictive value (PPV) decreased from 70.5% to 61.8%, and the F1-score increased from 32.2% to 47.0%. The sensitivity significantly increased from 13.7% to 32.4% for small nodules (3–6 mm) and from 33.3% to 47.6% for medium nodules (6–10 mm). CAD alone showed a sensitivity of 70.3%, a PPV of 57.9%, and an F1-score of 63.5%. Reading time decreased by 11.3% with the use of CAD.

Conclusion

CAD improved the less experienced radiologists’ sensitivity in detecting pulmonary nodules of all sizes, especially including a significant improvement in the detection of clinically important-sized medium nodules (6–10 mm) as well as small nodules (3–6 mm) and reduced their reading time.
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Metadata
Title
Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography
Authors
Takenori Kozuka
Yuko Matsukubo
Tomoya Kadoba
Teruyoshi Oda
Ayako Suzuki
Tomoko Hyodo
SungWoon Im
Hayato Kaida
Yukinobu Yagyu
Masakatsu Tsurusaki
Mitsuru Matsuki
Kazunari Ishii
Publication date
01-11-2020
Publisher
Springer Japan
Published in
Japanese Journal of Radiology / Issue 11/2020
Print ISSN: 1867-1071
Electronic ISSN: 1867-108X
DOI
https://doi.org/10.1007/s11604-020-01009-0

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