Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network
Autor: | Hajime Nagahara, Noriko Kikuchi, Tomo Miyata, Jun Miyake, Shohei Yamasaki, Akinori Hata, Yuriko Yoshida, Kazuo Awai, Mitsuko Tsubamoto, Shoji Kido, Masahiko Kusumoto, Hirohiko Niioka, Noriyuki Tomiyama, Masahiro Yanagawa, Yukihisa Satoh |
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Rok vydání: | 2020 |
Předmět: |
medicine.medical_specialty
Lung Neoplasms medicine.diagnostic_test business.industry Ultrasound Significant difference Pulmonary adenocarcinoma Adenocarcinoma of Lung Interventional radiology General Medicine medicine.disease Convolutional neural network Radiologists medicine Humans Adenocarcinoma Radiology Nuclear Medicine and imaging Neural Networks Computer Radiology Tomography X-Ray Computed business Lung cancer Neuroradiology |
Zdroj: | European Radiology. 31:1978-1986 |
ISSN: | 1432-1084 0938-7994 |
Popis: | 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. • 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. |
Databáze: | OpenAIRE |
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