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An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data

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Head and Neck Tumor Segmentation and Outcome Prediction (HECKTOR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13209))

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Abstract

Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many . Traditional machine learning algorithms have been eminently useful in crafting prognostic models in the last few decades. Recently, deep learning algorithms have shown significant improvement when developing diagnosis and prognosis solutions to different healthcare problems. However, most of these solutions rely solely on either imaging or clinical data. Utilizing patient tabular data such as demographics and patient medical history alongside imaging data in a multimodal approach to solve a prognosis task has started to gain more interest recently and has the potential to create more accurate solutions. The main issue when using clinical and imaging data to train a deep learning model is to decide on how to combine the information from these sources. We propose a multimodal network that ensembles deep multi-task logistic regression (MTLR), Cox proportional hazard (CoxPH) and CNN models to predict prognostic outcomes for patients with head and neck tumors using patients’ clinical and imaging (CT and PET) data. Features from CT and PET scans are fused and then combined with patients’ electronic health records for the prediction. The proposed model is trained and tested on 224 and 101 patient records respectively. Experimental results show that our proposed ensemble solution achieves a C-index of 0.72 on The HECKTOR test set that saved us the first place in prognosis task of the HECKTOR challenge. The full implementation based on PyTorch is available on https://github.com/numanai/BioMedIA-Hecktor2021.

Team name: MBZUAI-BioMedIA

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References

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

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. CoRR abs/1907.10902 (2019). http://arxiv.org/abs/1907.10902

  4. Allende, A.S.: Concordance index as an evaluation metric (October 2019). https://medium.com/analytics-vidhya/concordance-index-72298c11eac7

  5. Cox, D.R.: Regression models and life-tables. J. Roy. Stat. Soc. Ser. B (Methodol.) 34(2), 187–202 (1972). https://doi.org/10.1111/j.2517-6161.1972.tb00899.x

    Article  MathSciNet  MATH  Google Scholar 

  6. Fotso, S.: Deep neural networks for survival analysis based on a multi-task framework (2018)

    Google Scholar 

  7. Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures, they are data. Radiology 278(2), 563–577 (2016). https://doi.org/10.1148/radiol.2015151169

    Article  Google Scholar 

  8. Jin, P.: Using survival prediction techniques to learn consumer-specific reservation price distributions (2015)

    Google Scholar 

  9. Kazmierski, M., et al.: A machine learning challenge for prognostic modelling in head and neck cancer using multi-modal data (2021)

    Google Scholar 

  10. Kim, S., Kazmierski, M., Haibe-Kains, B.: Deep-CR MTLR: a multi-modal approach for cancer survival prediction with competing risks (2021)

    Google Scholar 

  11. Mackillop, W.J.: The Importance of Prognosis in Cancer Medicine. American Cancer Society (2006). https://doi.org/10.1002/0471463736.tnmp01.pub2

  12. Parmar, C., Grossmann, P., Rietveld, D., Rietbergen, M.M., Lambin, P., Aerts, H.J.W.L.: Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front. Oncol. 5, 272 (2015). https://doi.org/10.3389/fonc.2015.00272

    Article  Google Scholar 

  13. Shboul, Z.A., Alam, M., Vidyaratne, L., Pei, L., Elbakary, M.I., Iftekharuddin, K.M.: Feature-guided deep radiomics for glioblastoma patient survival prediction. Front. Neurosci. 13, 966 (2019). https://doi.org/10.3389/fnins.2019.00966

    Article  Google Scholar 

  14. Sun, L., Zhang, S., Chen, H., Luo, L.: Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Front. Neurosci. 13, 810 (2019). https://doi.org/10.3389/fnins.2019.00810

    Article  Google Scholar 

  15. Tseng, W.T., Chiang, W.F., Liu, S.Y., Roan, J., Lin, C.N.: The application of data mining techniques to oral cancer prognosis. J. Med. Syst. 39(5), 1–7 (2015). https://doi.org/10.1007/s10916-015-0241-3

    Article  Google Scholar 

  16. Wang, X., Li, B.: Deep learning in head and neck tumor multiomics diagnosis and analysis: review of the literature. Front. Genet. 12, 42 (2021). https://doi.org/10.3389/fgene.2021.624820. https://www.frontiersin.org/article/10.3389/fgene.2021.624820

  17. Yu, C.N., Greiner, R., Lin, H.C., Baracos, V.: Learning patient-specific cancer survival distributions as a sequence of dependent regressors. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems. vol. 24. Curran Associates, Inc. (2011). https://proceedings.neurips.cc/paper/2011/file/1019c8091693ef5c5f55970346633f92-Paper.pdf

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Saeed, N., Al Majzoub, R., Sobirov, I., Yaqub, M. (2022). An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021. Lecture Notes in Computer Science, vol 13209. Springer, Cham. https://doi.org/10.1007/978-3-030-98253-9_26

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  • DOI: https://doi.org/10.1007/978-3-030-98253-9_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98252-2

  • Online ISBN: 978-3-030-98253-9

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