Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional
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Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network Masahiro Yanagawa 1 & Hirohiko Niioka 2 & Masahiko Kusumoto 3 & Kazuo Awai 4 & Mitsuko Tsubamoto 5 & Yukihisa Satoh 1 & Tomo Miyata 1 & Yuriko Yoshida 1 & Noriko Kikuchi 1 & Akinori Hata 1 & Shohei Yamasaki 6 & Shoji Kido 7 & Hajime Nagahara 2 & Jun Miyake 8 & Noriyuki Tomiyama 1 Received: 2 June 2020 / Revised: 2 September 2020 / Accepted: 22 September 2020 # European Society of Radiology 2020
Abstract Objectives To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN). Methods Enrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared. Results Without the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01). Conclusions The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07339-x) contains supplementary material, which is available to authorized users. * Masahiro Yanagawa [email protected] 1
2
Department of Radiology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita City, Osaka 565-0871, Japan Institute for Datability Science, Osaka University, 2-8 Yamadaoka, Suita City, Osaka 565-0871, Japan
3
Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, Japan
4
Department of Diagnostic Radiology, Graduate School of Biomedical and Health Scien
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