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Convolutional neural network-based program to predict lymph node metastasis of non-small cell lung cancer using 18F-FDG PET

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Abstract

Purpose

To develop a convolutional neural network (CNN)-based program to analyze maximum intensity projection (MIP) images of 2-deoxy-2-[F-18]fluoro-d-glucose (FDG) positron emission tomography (PET) scans, aimed at predicting lymph node metastasis of non-small cell lung cancer (NSCLC), and to evaluate its effectiveness in providing diagnostic assistance to radiologists.

Methods

We obtained PET images of NSCLC from public datasets, including those of 435 patients with available N-stage information, which were divided into a training set (n = 304) and a test set (n = 131). We generated 36 maximum intensity projection (MIP) images for each patient. A residual network (ResNet-50)-based CNN was trained using the MIP images of the training set to predict lymph node metastasis. Lymph node metastasis in the test set was predicted by the trained CNN as well as by seven radiologists twice: first without and second with CNN assistance. Diagnostic performance metrics, including accuracy and prediction error (the difference between the truth and the predictions), were calculated, and reading times were recorded.

Results

In the test set, 67 (51%) patients exhibited lymph node metastases and the CNN yielded 0.748 predictive accuracy. With the assistance of the CNN, the prediction error was significantly reduced for six of the seven radiologists although the accuracy did not change significantly. The prediction time was significantly reduced for five of the seven radiologists with the median reduction ratio 38.0%.

Conclusion

The CNN-based program could potentially assist radiologists in predicting lymph node metastasis by increasing diagnostic confidence and reducing reading time without affecting diagnostic accuracy, at least in the limited situations using MIP images.

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Data availability

Raw images and clinical data are available in The Cancer Imaging Archive. Processed images and CNN models are available from the corresponding author on request.

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Acknowledgements

The results published here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. We would like to express our sincere gratitude to the five readers, Kanae K. Miake, Tomomi W. Nobashi, Mahoto Juuo, Toshiya Takamura, Daiki Toda, Department of Diagnostic Radiology, Kyoto University Hospital, for their invaluable contributions to this research as participants in the reading experiment.

Funding

This study was financially supported by JSPS KAKENHI (Grant number 22K15879).

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Authors and Affiliations

  1. Department of Radiology, Kishiwada City Hospital, Kishiwada, Japan

    Eitaro Kidera

  2. Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan

    Eitaro Kidera, Sho Koyasu, Ryusuke Nakamoto & Yuji Nakamoto

  3. Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan

    Kenji Hirata

  4. Department of Thoracic Surgery, Kyoto University Hospital, Kyoto University, Kyoto, Japan

    Masatsugu Hamaji

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Contributions

EK: data collection, statistical analysis, drafting of the article. SK: data collection, conception and design, interpretation of data, drafting of this article. KH: conception and design, interpretation of data, revision for important intellectual content. MH: revision for important intellectual content. RN: data collection, revision for important intellectual content. YN: conception and design, final approval of this article. All authors have read and approved the manuscript before submission.

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Correspondence to Sho Koyasu.

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Kidera, E., Koyasu, S., Hirata, K. et al. Convolutional neural network-based program to predict lymph node metastasis of non-small cell lung cancer using 18F-FDG PET. Ann Nucl Med 38, 71–80 (2024). https://doi.org/10.1007/s12149-023-01866-5

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