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Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT Images

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

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13626))

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Abstract

Automatic segmentation of head and neck cancer (HNC) tumors and lymph nodes plays a crucial role in the optimization treatment strategy and prognosis analysis. This study aims to employ nnU-Net for automatic segmentation and radiomics for recurrence-free survival (RFS) prediction using pretreatment PET/CT images in a multi-center HNC cohort of 883 patients (524 patients for training, 359 for testing) provided within the context of the HECKTOR MICCAI challenge 2022. A bounding box of the extended oropharyngeal region was retrieved for each patient with fixed size of 224 \(\times \) 224 \(\times \) 224 mm\(^{3}\). Then the 3D nnU-Net architecture was adopted to carry out automatic segmentation of both primary tumor and lymph nodes. From the predicted segmentation mask, ten conventional features and 346 standardized radiomics features were extracted for each patient. Three prognostic models were constructed containing conventional and radiomics features alone, and their combinations by multivariate CoxPH modelling. The statistical harmonization method, ComBat, was explored towards reducing multicenter variations. Dice score and C-index were used as evaluation metrics for segmentation and prognosis task, respectively. For segmentation task, we achieved a mean dice score of 0.7 for primary tumor and lymph nodes. For recurrence-free survival prediction, conventional and radiomics models obtained C-index values of 0.66 and 0.65 in the test set, respectively, while the combined model did not improve the prognostic performance (0.65).

Team name: RokieLab.

H. Xu and Y. Li—Contributed equally to this work.

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Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Southern Medical University, Guangzhou, China

    Hui Xu & Lijun Lu

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

    Hui Xu, Yihao Li, Gwenolé Quellec & Mathieu Hatt

  3. Department of Colorectal Surgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Wei Zhao

Authors
  1. Hui Xu
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  2. Yihao Li
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  3. Wei Zhao
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  4. Gwenolé Quellec
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  5. Lijun Lu
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  6. Mathieu Hatt
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Corresponding author

Correspondence to Lijun Lu .

Editor information

Editors and Affiliations

  1. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Vincent Andrearczyk

  2. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Valentin Oreiller

  3. LaTIM, INSERM, University of Brest, Brest, France

    Mathieu Hatt

  4. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Adrien Depeursinge

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Xu, H., Li, Y., Zhao, W., Quellec, G., Lu, L., Hatt, M. (2023). Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT Images. 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_16

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  • DOI: https://doi.org/10.1007/978-3-031-27420-6_16

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-27420-6

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