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Diagnostic performance of a deep-learning model using 18F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer

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Abstract

Objective

We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.

Methods

We retrospectively recruited 308 patients with lung cancer with RT-related changes observed on 18F-fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG PET/CT) performed after RT. Patients were labelled as positive or negative for tumour recurrence through histologic diagnosis or clinical follow-up after 18F-FDG PET/CT. A two-dimensional (2D) slice-based convolutional neural network (CNN) model was created with a total of 3329 slices as input, and performance was evaluated with five independent test sets.

Results

For the five independent test sets, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity were in the range of 0.98–0.99, 95–98%, and 87–95%, respectively. The region determined by the model was confirmed as an actual recurred tumour through the explainable artificial intelligence (AI) using gradient-weighted class activation mapping (Grad-CAM).

Conclusion

The 2D slice-based CNN model using 18F-FDG PET imaging was able to distinguish well between RT-related changes and tumour recurrence in patients with lung cancer.

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Data availability

Data generated or analysed during the study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT; No. NRF-2020M2D9A1094074; 2021R1A2C3009056) and by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI18C2383).

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Author notes

  1. Changhwan Sung and Jungsu S. Oh are co-first authors.

Authors and Affiliations

  1. Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea

    Changhwan Sung, Jungsu S. Oh, Byung Soo Park & Jong Jin Lee

  2. Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

    Su Ssan Kim & Si Yeol Song

Authors
  1. Changhwan Sung
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  2. Jungsu S. Oh
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Contributions

Changhwan Sung, Jungsu S. Oh, and Jong Jin Lee contributed to the study conceptualization, data acquisition, data analysis, data interpretation, writing, and editing of the manuscript. Byung Soo Park contributed to data analysis and writing of the manuscript. Su Ssan Kim and Si Yeol Song contributed to the study conceptualization, data interpretation, and editing of the manuscript.

Corresponding author

Correspondence to Jong Jin Lee.

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Conflict of interest

The authors declare no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the principles of the 1964 Declaration of Helsinki and its subsequent amendments or comparable ethical standards.

Informed consent

This retrospective study was approved by the local Institutional Review Board (IRB No. 2022-1078). The need for informed consent was waived by the committee.

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Supplementary Information

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12149_2024_1925_MOESM1_ESM.pptx

Supplementary Figure 1. Representative images where both the 18F-FDG PET/CT formal report and the deep learning model were false negative. A 67-year-old woman diagnosed with squamous cell carcinoma in the apex of the right lung. (a) On the 18F-FDG PET/CT imaging performed three months after the completion of CCRT, a nodular and ground glass opacity (GGO) lesion with mildly hypermetabolic activity was observed at the apex of the right lung. Both the formal report and the deep learning model predicted this lesion as an RT-related change. (b) On the follow-up 18F-FDG PET/CT imaging after four months, the lesion progressed, with an increase of SUVmax from 3.7 to 8.0. Although no additional pathological confirmation was performed, the patient exhibited rapid progression and died four months later. Supplementary file1 (PPTX 1219 KB)

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Sung, C., Oh, J.S., Park, B.S. et al. Diagnostic performance of a deep-learning model using 18F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer. Ann Nucl Med (2024). https://doi.org/10.1007/s12149-024-01925-5

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