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Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography

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Japanese Journal of Radiology Aims and scope Submit manuscript

Abstract

Purpose

To investigate the ability of deep learning (DL) using convolutional neural networks (CNNs) for distinguishing between normal and metastatic axillary lymph nodes on ultrasound images by comparing the diagnostic performance of radiologists.

Materials and methods

We retrospectively gathered 300 images of normal and 328 images of axillary lymph nodes with breast cancer metastases for training. A DL model using the CNN architecture Xception was developed to analyze test data of 50 normal and 50 metastatic lymph nodes. A board-certified radiologist with 12 years’ experience.

(Reader 1) and two residents with 3- and 1-year experience (Readers 2, 3), respectively, scored these test data with and without the assistance of the DL system for the possibility of metastasis. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated.

Results

Our DL model had a sensitivity of 94%, a specificity of 88%, and an AUC of 0.966. The AUC of the DL model was not significantly different from that of Reader 1 (0.969; p = 0.881) and higher than that of Reader 2 (0.913; p = 0.101) and Reader 3 (0.810; p < 0.001). With the DL support, the AUCs of Readers 2 and 3 increased to 0.960 and 0.937, respectively, which were comparable to those of Reader 1 (p = 0.138 and 0.700, respectively).

Conclusion

Our DL model demonstrated great diagnostic performance for differentiating benign from malignant axillary lymph nodes on breast ultrasound and for potentially providing effective diagnostic support to residents.

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Authors and Affiliations

  1. Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan

    Jo Ozaki, Tomoyuki Fujioka, Emi Yamaga, Atsushi Hayashi, Yu Kujiraoka, Tomoki Imokawa, Kanae Takahashi, Sayuri Okawa, Yuka Yashima, Mio Mori & Ukihide Tateishi

  2. Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, 343 - 8555, Japan

    Kazunori Kubota

  3. Department of Surgery, Breast surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan

    Goshi Oda & Tsuyoshi Nakagawa

Authors
  1. Jo Ozaki
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  2. Tomoyuki Fujioka
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  3. Emi Yamaga
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  4. Atsushi Hayashi
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  5. Yu Kujiraoka
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  6. Tomoki Imokawa
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  7. Kanae Takahashi
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  8. Sayuri Okawa
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  9. Yuka Yashima
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  10. Mio Mori
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  11. Kazunori Kubota
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  12. Goshi Oda
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  13. Tsuyoshi Nakagawa
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  14. Ukihide Tateishi
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Corresponding author

Correspondence to Tomoyuki Fujioka.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures used in this research were approved by the Ethics Committee of Tokyo Medical and Dental University.

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Ozaki, J., Fujioka, T., Yamaga, E. et al. Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography. Jpn J Radiol 40, 814–822 (2022). https://doi.org/10.1007/s11604-022-01261-6

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  • DOI: https://doi.org/10.1007/s11604-022-01261-6

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