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Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network



To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN).


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.


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).


The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances.

Key Points

• The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity.

• Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model.

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Adenocarcinoma in situ


Area under the curve


Confidence interval


Convolutional neural network


Ground-glass nodule


Invasive adenocarcinoma


Minimally invasive adenocarcinoma


World Health Organization


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This work was supported by a Grant-in-Aid for Scientific Research (C) (Grant Number JP18K07713). Masahiro Yanagawa, Hirohiko Niioka, Noriyuki Tomiyama, and Jun Miyake received a Grant-in-Aid for Scientific Research (C).

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Correspondence to Masahiro Yanagawa.

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The scientific guarantor of this publication is Noriyuki Tomiyama.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Institutional review board approval was obtained.


• retrospective

• diagnostic or prognostic study

• multicenter study

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Supplementary Fig. 1

How to separate data in nested 10-fold cross validation. Predictions from nine models were assigned to the same test data. The average of the predictions from the nine models was calculated and a prediction label was given to each test datum (DOCX 461 kb)

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Yanagawa, M., Niioka, H., Kusumoto, M. et al. Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network. Eur Radiol 31, 1978–1986 (2021).

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  • Lung cancer
  • Artificial intelligence
  • Deep learning