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A deep learning-based integrative model for survival time prediction of head and neck squamous cell carcinoma patients

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Abstract

Oral Cancer is one of the prevailing diseases worldwide. Taking previous studies into account, observations have shown that oral cancer has a poor prognosis due to the delay in the detection of the disease. The outcomes of cancer detection and prevention are ineffective unless the mutation of genetic factors is thoroughly understood. Nevertheless, understanding and identifying genetic mutation is a challenging issue for researchers. Determining the survival time is one of the essential outcomes in cancer detection. The existing survival time-based studies introduced models that use one type of genomic data or on clinical data, which do not consider the structural and biological relationships of genomic data in cancer. However, the current work is being carried out by integrating different types of genomic and clinical data to get a better understanding of cancer characterization. The key component to understand the complex molecular mechanisms of cancer is data integration. However, the integration of multi-genomic data poses significant challenges due to the existence of high dimensions and diverse approaches in it. The focus of this study is to create an integrative model for improved prediction accuracy of clinical outcomes in the survivability of oral cancer. The proposed model initially uses dimensionality reduction and feature selection techniques for the identification and elimination of features with insignificant and meaningless values from the Head and Neck Squamous Cell Carcinoma (HNSC) dataset taken from The Cancer Genome Atlas (TCGA). The integrative model's predictive performance is then compared to the performance of the model based on clinical features only. The proposed model performed well on the training and testing sets, achieving a c-index of 0.9439 and 0.916, respectively. It can be concluded from the results that the integrative model can effectively differentiate the interaction of genomic data types and it can be beneficial for the patients having oral cancer in terms of significant diagnostics and treatment plans.

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Authors and Affiliations

  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, 160014, India

    Diksha Sharma,  Deepali & Neelam Goel

  2. Department of Medical Lab Technology, University Institute of Applied Health Sciences, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India

    Vivek Kumar Garg

  3. Department of Histopathology, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India

    Dharambir Kashyap

  4. Guru Nanak College, Budhlada, Punjab, 151502, India

    Deepali

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  1. Diksha Sharma
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Sharma, D., Deepali, Garg, V.K. et al. A deep learning-based integrative model for survival time prediction of head and neck squamous cell carcinoma patients. Neural Comput & Applic 34, 21353–21365 (2022). https://doi.org/10.1007/s00521-022-07615-5

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