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Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study

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Abstract

Objective

To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment.

Methods

We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively.

Results

In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively).

Conclusions

Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors express their appreciation to the staff of Cancer Center of Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation for their kind assistance in retrieving the data of patients with lung cancer. We are also grateful for the grants provided by the Buddhist Tzu Chi Medical Foundation (TCMF-A 107-01-02(112) and TCMF-A 107-01-02(113)) and National Science and Technology Council in Taiwan (NSTC 112-2314-B-303-021).

Funding

This research was funded by the Buddhist Tzu Chi Medical Foundation (Grant number: TCMF-A 107–01-02(112) and TCMF-A 107–01-02(113)) and the National Science and Technology Council in Taiwan (NSTC 112–2314-B-303–021). The funding was involved in the data collection and analysis in this study.

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Authors and Affiliations

  1. Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, No.880, Sec.2, Chien-kuo Rd., Hualien, 970302, Taiwan

    Kun-Han Lue, Yu-Hung Chen & Shu-Hsin Liu

  2. Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No.707, Sec.3, Zhongyang Rd, Hualien, 970473, Taiwan

    Yu-Hung Chen & Shu-Hsin Liu

  3. School of Medicine, College of Medicine, Tzu Chi University, No.701, Sec.3, Zhongyang Rd, Hualien, 970473, Taiwan

    Yu-Hung Chen, Sung-Chao Chu, Chih-Bin Lin & Tso-Fu Wang

  4. Department of Hematology and Oncology, Buddhist Tzu Chi Medical Foundation, Hualien Tzu Chi Hospital, Hualien, Taiwan

    Sung-Chao Chu & Tso-Fu Wang

  5. Department of Internal Medicine, Buddhist Tzu Chi Medical Foundation, Hualien Tzu Chi Hospital, Hualien, Taiwan

    Chih-Bin Lin

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  1. Kun-Han Lue
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Correspondence to Yu-Hung Chen.

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Ethical approval

This study has been approved by the Institutional Review Board and Ethics Committee of Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation (protocol code IRB112-253-B and date of approval: Nov. 11, 2023). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Lue, KH., Chen, YH., Chu, SC. et al. Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study. Ann Nucl Med (2024). https://doi.org/10.1007/s12149-024-01936-2

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