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Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer

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Abstract

Purpose

To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18F-FDG PET/CT and MRI radiomics combined with clinical parameters.

Methods

We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared.

Results

In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively.

Conclusions

Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.

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Data availability

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

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Radiation Oncology Department, University Hospital, Brest, France

    François Lucia, Vincent Bourbonne, Omar Miranda, Olivier Pradier & Ulrike Schick

  2. LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France

    François Lucia, Vincent Bourbonne, Dimitris Visvikis, Olivier Pradier, Ulrike Schick & Mathieu Hatt

  3. Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium

    François Lucia, Roland Hustinx & Pierre Lovinfosse

  4. Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium

    Clémence Pleyers & Johanne Hermesse

  5. Department of Gynecology and Surgery, University Hospital, Brest, France

    Pierre-François Dupré

  6. Nuclear Medicine Department, University Hospital, Brest, France

    Ronan Abgral

  7. EA GETBO 3878, IFR 148, University of Brest, UBO, Brest, France

    Ronan Abgral

  8. Department of Radiation Oncology, Institut de Cancérologie de l’Ouest Centre René Gauducheau, Saint Herblain, France

    Augustin Mervoyer

  9. Department of Surgical Oncology, Institut de Cancérologie de l’Ouest Centre René Gauducheau, Saint Herblain, France

    Jean-Marc Classe

  10. Université de Nantes, CNRS, Inserm, CRCINA, F-44000, Nantes, France

    Caroline Rousseau

  11. ICO René Gauducheau, F-44800, Saint-Herblain, France

    Caroline Rousseau

  12. Radiomics SA, Liège, Belgium

    Wim Vos

  13. Department of Medical Oncology, University Hospital of Liège, Liège, Belgium

    Christine Gennigens

  14. Department of Gynecology, University Hospital of Liège, Liège, Belgium

    Marjolein De Cuypere & Frédéric Kridelka

Authors
  1. François Lucia
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by François Lucia, Vincent Bourbonne, Clémence Pleyers, Mathieu Hatt, Roland Hustinx, and Pierre Lovinfosse. The first draft of the manuscript was written by François Lucia and Pierre Lovinfosse and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to François Lucia.

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All procedures were performed in accordance with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The study design and exemption from informed consent were approved by the Institutional Review Board of Liege University Hospital.

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The study design and exemption from informed consent were approved by the Institutional Review Board of Liege University Hospital.

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The authors declare that they have no competing interests.

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Lucia, F., Bourbonne, V., Pleyers, C. et al. Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer. Eur J Nucl Med Mol Imaging 50, 2514–2528 (2023). https://doi.org/10.1007/s00259-023-06180-w

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