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Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT

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Abstract

Purpose

Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized treatment for patients. Thus, in this study, we propose a computer-aided diagnosis (CAD) system that predicts postoperative recurrence from computed tomography (CT) images acquired before surgery in patients with lung adenocarcinoma using a deep convolutional neural network (DCNN).

Methods

This retrospective study included 150 patients who underwent curative surgery for primary lung adenocarcinoma. To create original images, the tumor part was cropped from the preoperative contrast-enhanced CT images. The number of input images to the DCNN was increased to 3000 using data augmentation. We constructed a CAD system by transfer learning using a pretrained VGG19 model. Tenfold cross-validation was performed five times. Cases with an average identification rate of 0.5 or higher were determined to be a recurrence.

Results

The median duration of follow-up was 73.2 months. The results of the performance evaluation showed that the sensitivity, specificity, and accuracy of the proposed method were 0.75, 0.87, and 0.82, respectively. The area under the receiver operating characteristic curve was 0.86.

Conclusion

We demonstrated the usefulness of DCNN in predicting postoperative recurrence of lung adenocarcinoma using preoperative CT images. Because our proposed method uses only CT images, we believe that it has the advantage of being able to assess postoperative recurrence on an individual patient basis, both preoperatively and noninvasively.

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Code and data availability

The code generated in this study is available from the corresponding author on reasonable request. However, the image datasets presented in this study are not publicly available due to ethical reasons.

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Acknowledgements

This work was supported by Research Grant of Graduate School of Health Sciences, in Niigata University 2020 and 2021.

Author information

Authors and Affiliations

  1. Division of Central Radiology, Niigata Cancer Center Hospital, 2-15-3 Kawagishi-cho, Chuo-ku, Niigata-shi, Niigata, 951-8566, Japan

    Yuki Sasaki

  2. Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, Niigata, Japan

    Yuki Sasaki & Yohan Kondo

  3. Department of Thoracic Surgery, Niigata Cancer Center Hospital, Niigata, Japan

    Tadashi Aoki

  4. Department of Radiology, Niigata Cancer Center Hospital, Niigata, Japan

    Naoya Koizumi, Toshiro Ozaki & Hiroshi Seki

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  1. Yuki Sasaki
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yuki Sasaki, Yohan Kondo and Tadashi Aoki. The results were interpreted by Yuki Sasaki, Tadashi Aoki, Naoya Koizumi, Toshiro Ozaki and Hiroshi Seki. The first draft of the manuscript was written by Yuki Sasaki, and all authors read and approved the final manuscript.

Corresponding author

Correspondence to Yuki Sasaki.

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Conflicts of interest

The authors declare that they have no conflict of interest.

Consent to participate

Informed consent to participant was waived for this study by the Institutional Review Board at Niigata Cancer Center Hospital.

Consent for publication

Consent for publication was waived for this study by the Institutional Review Board at Niigata Cancer Center Hospital.

Ethical approval

This study was approved by the Institutional Review Board at Niigata Cancer Center Hospital (No. 1010). It was performed in accordance with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards and the Ethical Guideline for Clinical Research, issued by the Ministry of Health, Labor and Welfare, Japanese Government (2015).

Informed consent

The need to obtain informed consent was waived by the Institutional Review Board at Niigata Cancer Center Hospital.

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Sasaki, Y., Kondo, Y., Aoki, T. et al. Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT. Int J CARS 17, 1651–1661 (2022). https://doi.org/10.1007/s11548-022-02694-0

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  • DOI: https://doi.org/10.1007/s11548-022-02694-0

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