Abstract
Head and Neck Squamous Cell Carcinoma (HNSCC) is a group of malignancies arising in the squamous cells of the head and neck region. As a group, HNSCC accounts for around 4.5% of cancer incidences and deaths worldwide. Radiotherapy is part of the standard care for HNSCC cancers and accurate delineation of tumors is important for treatment quality. Imaging features of Computed Tomography (CT) and Positron Emission Tomography (PET) scans have been shown to be correlated with survival of HNSCC patients. In this paper we present our solutions to the segmentation task and recurrence-free survival prediction task of the HECKTOR 2022 challenge. We trained a 3D UNet model for the segmentation of primary tumors and lymph node metastases based on CT images. Three sets of models with different combinations of loss functions were ensembled to generate a more robust model. The softmax output of the ensembled model was fused with co-registered PET scans and post-processed to generate our submission to task 1 of the challenge, which achieved a 0.716 aggregated Dice score on the test data. Our segmentation model outputs were used to extract radiomic features of individual tumors on test data. Clinical variables and location of the tumors were also encoded and concatenated with radiomic features as additional inputs. We trained a multiple instance neural network to aggregate features of individual tumors into patient-level representations and predict recurrence-free survival rates of patients. Our method achieved an AUC of 0.619 for task 2 on the test data (Team name: SMIAL).
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References
Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2022: automatic head and neck tumor segmentation and outcome prediction in PET/CT images. In: Andrearczyk, V., et al. (eds.) HECKTOR 2022. LNCS, vol. 13626, pp. 1–30. Springer, Cham (2023)
Bogowicz, M., et al.: Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma. Acta Oncol. 56(11), 1531–1536 (2017)
Chen, J., Cheung, H.M.C., Milot, L., Martel, A.L.: AMINN: autoencoder-based multiple instance neural network improves outcome prediction in multifocal liver metastases. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 752–761. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_72
Chen, J., Martel, A.L.: Metastatic cancer outcome prediction with injective multiple instance pooling. arXiv preprint arXiv:2203.04964 (2022)
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127–2136. PMLR (2018)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Johnson, D.E., Burtness, B., Leemans, C.R., Lui, V.W.Y., Bauman, J.E., Grandis, J.R.: Head and neck squamous cell carcinoma. Nat. Rev. Dis. Primers. 6(1), 1–22 (2020)
Ma, J., et al.: Loss odyssey in medical image segmentation. Med. Image Anal. 71, 102035 (2021)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Vallieres, M., et al.: Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci. Rep. 7(1), 1–14 (2017)
Van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Can. Res. 77(21), e104–e107 (2017)
Xie, J., Peng, Y.: The head and neck tumor segmentation based on 3D U-Net. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds.) HECKTOR 2021. LNCS, vol. 13209, pp. 92–98. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-98253-9_8
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We acknowledge support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Digital Research Alliance of Canada.
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Chen, J., Martel, A.L. (2023). Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network. 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_22
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DOI: https://doi.org/10.1007/978-3-031-27420-6_22
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