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Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients

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Head and Neck Tumor Segmentation and Outcome Prediction (HECKTOR 2022)

Abstract

Aim: The development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET) and computed tomography (CT) scans and GTV (Gross Tumor Volume) contours of primary tumors and pathological lymph nodes.

Methods: A DL auto-segmentation algorithm generated the GTV contours (task 1) that were used for imaging biomarkers (IBMs) extraction and as input for the DL model. Multivariable cox regression analysis was used to develop radiomics models based on clinical and IBMs features. Clinical features with a significant correlation with the endpoint in a univariable analysis were selected. The most promising IBMs were selected by forward selection in 1000 times bootstrap resampling in five-fold cross validation. To optimize the DL models, different combinations of clinical features, PET/CT imaging, GTV contours, the selected radiomics features and the radiomics model predictions were used as input. The combination with the best average performance in five-fold cross validation was taken as the final input for the DL model. The final prediction in the test set, was an ensemble average of the predictions from the five models for the different folds.

Results: The average C-index in the five-fold cross validation of the radiomics model and the DL model were 0.7069 and 0.7575, respectively. The radiomics and final DL models showed C-indexes of 0.6683 and 0.6455, respectively in the test set.

Conclusion: The radiomics model for recurrence free survival prediction based on clinical, GTV and CT image features showed the best predictive performance in the test set with a C-index of 0.6683.

B. Ma and Y. Li—These authors contributed equally.

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References

  1. World Health Organization: Global cancer observatory. International agency for research on cancer. World Health Organization (2020)

    Google Scholar 

  2. O’Sullivan, B., et al.: Development and validation of a staging system for HPV-related oropharyngeal cancer by the International Collaboration on Oropharyngeal cancer Network for Staging (ICON-S): a multicentre cohort study. Lancet Oncol. 17(4) (2016)

    Google Scholar 

  3. Cramer, J.D., Burtness, B., Le, Q.T., Ferris, R.L.: The changing therapeutic landscape of head and neck cancer. Nat. Rev. Clin. Oncol. 16(11) (2019)

    Google Scholar 

  4. Ma, B., et al.: Self-supervised multi-modality image feature extraction for the progression free survival prediction in head and neck cancer. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds.) HECKTOR 2021. LNCS, vol. 13209, pp. 308–317. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98253-9_29

    Chapter  Google Scholar 

  5. Zhai, T.T., et al.: The prognostic value of CT-based image-biomarkers for head and neck cancer patients treated with definitive (chemo-)radiation. Oral Oncol. 95 (2019)

    Google Scholar 

  6. Zhai, T.T., et al.: Improving the prediction of overall survival for head and neck cancer patients using image biomarkers in combination with clinical parameters. Radiother. Oncol. 124(2) (2017)

    Google Scholar 

  7. Bi, W.L., et al.: Artificial intelligence in cancer imaging: clinical challenges and applications. CA: Cancer J. Clin. (2019)

    Google Scholar 

  8. Ma, B., et al.: MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation. Comput. Med. Imaging Graph. 86 (2020)

    Google Scholar 

  9. Zhao, Y., Ma, B., Jiang, P., Zeng, D., Wang, X., Li, S.: Prediction of Alzheimer’s disease progression with multi-information generative adversarial network. IEEE J. Biomed. Heal. Inform. 25(3) (2021)

    Google Scholar 

  10. Zhang, X., Kelkar, V.A., Granstedt, J., Li, H., Anastasio, M.A.: Impact of deep learning-based image super-resolution on binary signal detection. J. Med. Imaging 8(06) (2021)

    Google Scholar 

  11. Zeng, D., Li, Q., Ma, B., Li,S.: Hippocampus segmentation for preterm and aging brains using 3D densely connected fully convolutional networks. IEEE Access 8 (2020)

    Google Scholar 

  12. Oreiller, V., et al.: Head and neck tumor segmentation in PET/CT: the HECKTOR challenge. Med. Image Anal. 77, 102336 (2022)

    Article  Google Scholar 

  13. Diamant, A., Chatterjee, A., Vallières, M., Shenouda, G., Seuntjens, J.: Deep learning in head & neck cancer outcome prediction. Sci. Rep. 9(1) (2019)

    Google Scholar 

  14. Wang, Y., et al.: Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis. Comput. Methods Programs Biomed. 106948 (2022)

    Google Scholar 

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December (2016)

    Google Scholar 

  16. Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds.) HECKTOR 2021. LNCS, vol. 13209, pp. 1–37. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98253-9_1

    Chapter  Google Scholar 

  17. Van Griethuysen, J.J.M., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21) (2017)

    Google Scholar 

  18. van Dijk, L.V., et al.: 18F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia. Radiother. Oncol. 126(1), 89–95 (2018)

    Article  Google Scholar 

  19. van Dijk, L.V., et al.: CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva. Radiother. Oncol. 122(2), 185–191 (2017)

    Article  Google Scholar 

  20. Van den Bosch, L., et al.: Key challenges in normal tissue complication probability model development and validation: towards a comprehensive strategy. Radiother. Oncol. 148 (2020)

    Google Scholar 

  21. Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1) (2018)

    Google Scholar 

  22. Chamberlain, C., Owen-Smith, A., Donovan, J., Hollingworth, W.: A systematic review of geographical variation in access to chemotherapy. BMC Cancer 16(1) (2015)

    Google Scholar 

  23. Leijenaar, R.T.H., et al.: Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study. Br. J. Radiol. 91(1086), 1–8 (2018)

    Google Scholar 

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Ma, B. et al. (2023). Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022. Lecture Notes in Computer Science, vol 13626. Springer, Cham. https://doi.org/10.1007/978-3-031-27420-6_24

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  • DOI: https://doi.org/10.1007/978-3-031-27420-6_24

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