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Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy

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Abstract

Purpose

To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [18F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters.

Methods

We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [18F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/or distant recurrence.

Results

In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69).

Conclusion

Radiomic features extracted from pre-SBRT analog and digital [18F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. 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.

References

  1. Nagata Y, Hiraoka M, Shibata T, Onishi H, Kokubo M, Karasawa K, et al. Prospective trial of stereotactic body radiation therapy for both operable and inoperable T1N0M0 non-small cell lung cancer: Japan Clinical Oncology Group study JCOG0403. Int J Radiat Oncol Biol Phys. 2015;93:989–96.

    Article  PubMed  Google Scholar 

  2. Guckenberger M, Allgäuer M, Appold S, Dieckmann K, Ernst I, Ganswindt U, et al. Safety and efficacy of stereotactic body radiotherapy for stage 1 non-small-cell lung cancer in routine clinical practice: a patterns-of-care and outcome analysis. J Thorac Oncol. 2013;8:1050–8.

    Article  CAS  PubMed  Google Scholar 

  3. Ricardi U, Frezza G, Filippi AR, Badellino S, Levis M, Navarria P, et al. Stereotactic ablative radiotherapy for stage I histologically proven non-small cell lung cancer: an Italian multicenter observational study. Lung Cancer. 2014;84:248–53.

    Article  PubMed  Google Scholar 

  4. Timmerman R, Paulus R, Galvin J, Michalski J, Straube W, Bradley J, et al. Stereotactic body radiation therapy for inoperable early stage lung cancer. JAMA. 2010;303:1070–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Eriguchi T, Takeda A, Nemoto T, Tsurugai Y, Sanuki N, Tateishi Y, et al. Relationship between dose prescription methods and local control rate in stereotactic body radiotherapy for early stage non-small-cell lung cancer: systematic review and meta-analysis. Cancers (Basel). 2022;14:3815.

    Article  PubMed  Google Scholar 

  6. Chang JY, Lin SH, Dong W, Liao Z, Gandhi SJ, Gay CM, et al. Stereotactic ablative radiotherapy with or without immunotherapy for early-stage or isolated lung parenchymal recurrent node-negative non-small-cell lung cancer: an open-label, randomised, phase 2 trial. Lancet. 2023;S0140–6736(23):01384–93.

    Google Scholar 

  7. Gao SJ, Jin L, Meadows HW, Shafman TD, Gross CP, Yu JB, et al. Prediction of distant metastases after stereotactic body radiation therapy for early stage NSCLC: development and external validation of a multi-institutional model. J Thorac Oncol. 2023;18:339–49.

    Article  PubMed  Google Scholar 

  8. Vaz SC, Adam JA, Delgado Bolton RC, Vera P, van Elmpt W, Herrmann K, et al. Joint EANM/SNMMI/ESTRO practice recommendations for the use of 2-[18F]FDG PET/CT external beam radiation treatment planning in lung cancer V1.0. Eur J Nucl Med Mol Imaging. 2022;49:1386–406.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Sharma A, Mohan A, Bhalla AS, Sharma MC, Vishnubhatla S, Das CJ, et al. Role of various metabolic parameters derived from baseline 18F-FDG PET/CT as prognostic markers in non-small cell lung cancer patients undergoing platinum-based chemotherapy. Clin Nucl Med. 2018;43:e8-17.

    Article  PubMed  Google Scholar 

  10. Kwon W, Howard BA, Herndon JE, Patz EF. FDG uptake on positron emission tomography correlates with survival and time to recurrence in patients with stage I non-small-cell lung cancer. J Thorac Oncol. 2015;10:897–902.

    Article  PubMed  Google Scholar 

  11. Na F, Wang J, Li C, Deng L, Xue J, Lu Y. Primary tumor standardized uptake value measured on F18-fluorodeoxyglucose positron emission tomography is of prediction value for survival and local control in non-small-cell lung cancer receiving radiotherapy: meta-analysis. J Thorac Oncol. 2014;9:834–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hoang JK, Hoagland LF, Coleman RE, Coan AD, Herndon JE, Patz EF. Prognostic value of fluorine-18 fluorodeoxyglucose positron emission tomography imaging in patients with advanced-stage non-small-cell lung carcinoma. J Clin Oncol. 2008;26:1459–64.

    Article  PubMed  Google Scholar 

  13. Agarwal M, Brahmanday G, Bajaj SK, Ravikrishnan KP, Wong C-YO. Revisiting the prognostic value of preoperative (18)F-fluoro-2-deoxyglucose ( (18)F-FDG) positron emission tomography (PET) in early-stage (I & II) non-small cell lung cancers (NSCLC). Eur J Nucl Med Mol Imaging. 2010;37:691–8.

    Article  PubMed  Google Scholar 

  14. Lovinfosse P, Janvary ZL, Coucke P, Jodogne S, Bernard C, Hatt M, et al. FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging. 2016;43:1453–60.

    Article  CAS  PubMed  Google Scholar 

  15. Dissaux G, Visvikis D, Da-Ano R, Pradier O, Chajon E, Barillot I, et al. Pretreatment 18F-FDG PET/CT radiomics predict local recurrence in patients treated with stereotactic body radiotherapy for early-stage non-small cell lung cancer: a multicentric study. J Nucl Med. 2020;61:814–20.

    Article  CAS  PubMed  Google Scholar 

  16. Wu J, Aguilera T, Shultz D, Gudur M, Rubin DL, Loo BW, et al. Early-stage non-small cell lung cancer: quantitative imaging characteristics of (18)F fluorodeoxyglucose PET/CT allow prediction of distant metastasis. Radiology. 2016;281:270–8.

    Article  PubMed  Google Scholar 

  17. Oikonomou A, Khalvati F, Tyrrell PN, Haider MA, Tarique U, Jimenez-Juan L, et al. Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Sci Rep. 2018;8:4003.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  18. Pyka T, Bundschuh RA, Andratschke N, Mayer B, Specht HM, Papp L, et al. Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. Radiat Oncol. 2015;10:100.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Opitz D, Maclin R. Popular ensemble methods: an empirical study. JAIR. 1999;11:169–98.

    Article  Google Scholar 

  20. Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology. 2018;286:887–96.

    Article  PubMed  Google Scholar 

  21. Bourbonne V, Lucia F, Jaouen V, Bert J, Rehn M, Pradier O, et al. Development and prospective validation of a spatial dose pattern based model predicting acute pulmonary toxicity in patients treated with volumetric arc-therapy for locally advanced lung cancer. Radiother Oncol. 2021;164:43–9.

    Article  PubMed  Google Scholar 

  22. Janvary ZL, Jansen N, Baart V, Devillers M, Dechambre D, Lenaerts E, et al. Clinical outcomes of 130 patients with primary and secondary lung tumors treated with Cyberknife robotic stereotactic body radiotherapy. Radiol Oncol. 2017;51:178–86.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Senthi S, Lagerwaard FJ, Haasbeek CJA, Slotman BJ, Senan S. Patterns of disease recurrence after stereotactic ablative radiotherapy for early stage non-small-cell lung cancer: a retrospective analysis. Lancet Oncol. 2012;13:802–9.

    Article  PubMed  Google Scholar 

  24. Schemper M, Smith TL. A note on quantifying follow-up in studies of failure time. Control Clin Trials. 1996;17:343–6.

    Article  CAS  PubMed  Google Scholar 

  25. Belli ML, Mori M, Broggi S, Cattaneo GM, Bettinardi V, Dell’Oca I, et al. Quantifying the robustness of [18F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients. Phys Med. 2018;49:105–11.

    Article  PubMed  Google Scholar 

  26. Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep. 2013;3:3529.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Radiomic features — PyRadiomics v3.1.0rc2.post5+g6a761c4 documentation. https://pyradiomics.readthedocs.io/en/latest/features.html. Accessed 29 Jun 2023.

  28. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328–38.

    Article  PubMed  Google Scholar 

  29. Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage. 2018;167:104–20.

    Article  PubMed  Google Scholar 

  30. Caetano SJ, Sonpavde G, Pond GR. C-statistic: a brief explanation of its construction, interpretation and limitations. Eur J Cancer. 2018;90:130–2.

    Article  CAS  PubMed  Google Scholar 

  31. Ernani V, Appiah AK, Marr A, Zhang C, Zhen W, Smith LM, et al. Adjuvant systemic therapy in patients with early-stage NSCLC treated with stereotactic body radiation therapy. J Thorac Oncol. 2019;14:475–81.

    Article  PubMed  Google Scholar 

  32. Zhou Z, Folkert M, Cannon N, Iyengar P, Westover K, Zhang Y, et al. Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters. Radiother Oncol. 2016;119:501–4.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, et al. [18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: technical aspects and potential clinical applications. Semin Nucl Med. 2022;52:759–80.

    Article  PubMed  Google Scholar 

  34. Fornacon-Wood I, Faivre-Finn C, O’Connor JPB, Price GJ. Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer. 2020;146:197–208.

    Article  PubMed  Google Scholar 

  35. Hao H, Zhou Z, Wang J. Distant failure prediction for early stage NSCLC by analyzing PET with sparse representation. In: Medical Imaging 2017: Computer-Aided Diagnosis. SPIE; 2017. p. 1008–14.

  36. Li H, Galperin-Aizenberg M, Pryma D, Simone CB, Fan Y. Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol. 2018;129:218–26.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Li S, Yang N, Li B, Zhou Z, Hao H, Folkert MR, et al. A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT. Med Image Anal. 2018;50:106–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yu W, Tang C, Hobbs BP, Li X, Koay EJ, Wistuba II, et al. Development and validation of a predictive radiomics model for clinical outcomes in stage I non-small cell lung cancer. Int J Radiat Oncol Biol Phys. 2018;102:1090–7.

    Article  PubMed  Google Scholar 

  39. Sawayanagi S, Yamashita H, Nozawa Y, Takenaka R, Miki Y, Morishima K, et al. Establishment of a prediction model for overall survival after stereotactic body radiation therapy for primary non-small cell lung cancer using radiomics analysis. Cancers (Basel). 2022;14:3859.

    Article  CAS  PubMed  Google Scholar 

  40. Lucia F, Bourbonne V, Pleyers C, Dupré P-F, Miranda O, Visvikis D, 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. 2023. https://doi.org/10.1007/s00259-023-06180-w.

    Article  PubMed  Google Scholar 

  41. Lee TH, Shin H, Ahn YC, Kang MK, Song C, Kim WC, et al. Regional lymph node recurrence after stereotactic body radiation therapy for lung cancer: patterns of recurrence, treatment approaches, and clinical outcomes (KROG 21–09). Radiother Oncol. 2023;183: 109572.

    Article  PubMed  Google Scholar 

  42. Klement RJ, Sonke J-J, Allgäuer M, Andratschke N, Appold S, Belderbos J, et al. Correlating dose variables with local tumor control in stereotactic body radiation therapy for early-stage non-small cell lung cancer: a modeling study on 1500 individual treatments. Int J Radiat Oncol Biol Phys. 2020;107:579–86.

    Article  PubMed  Google Scholar 

  43. Hatt M, Cheze Le Rest C, Antonorsi N, Tixier F, Tankyevych O, Jaouen V, et al. Radiomics in PET/CT: current status and future AI-based evolutions. Semin Nucl Med. 2021;51:126–33.

    Article  PubMed  Google Scholar 

  44. Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging. 2017;44:151–65.

    Article  PubMed  Google Scholar 

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Author information

Author notes

  1. François Lucia, Thomas Louis, Roland Hustinx, and Pierre Lovinfosse equally contributed.

Authors and Affiliations

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

    François Lucia, Vincent Bourbonne, Malik Nebbache, Martin Rehn, Mohamed Hamya & Ulrike Schick

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

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

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

    François Lucia, Thomas Louis, François Cousin, Roland Hustinx & Pierre Lovinfosse

  4. Service de Radiothérapie, CHRU Morvan, 2 Avenue Foch, 29609 Cedex, Brest, France

    François Lucia

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

    Carole Mievis, Nicolas Jansen & Philippe Coucke

  6. Division of Pulmonology, CHU Liège, Liège, Belgium

    Bernard Duysinx

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

    Romain Le Pennec & Pierre-Yves Salaun

  8. GETBO, INSERM, UMR 1304, University of Brest, UBO, Brest, France

    Romain Le Pennec & Pierre-Yves Salaun

  9. Medical Oncology Department, University Hospital, Brest, France

    Margaux Geier

  10. GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium

    Roland Hustinx

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  1. François Lucia
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Corresponding author

Correspondence to François Lucia.

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Ethics approval

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 Boards of Liege University Hospital (2022/285) and Brest University Hospital (29BRC16.0147).

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The authors declare no competing interests.

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Lucia, F., Louis, T., Cousin, F. et al. Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging 51, 1097–1108 (2024). https://doi.org/10.1007/s00259-023-06510-y

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