Survival prediction of squamous cell head and neck cancer patients based on radiomic features selected from lung cancer patients using artificial neural network

H. Kamezawa, H. Arimura, M. Soufi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The goal of this study was to investigate the survival prediction of squamous cell head and neck cancer (SCHNC) patients by using radiomic features that were selected using an artificial neural network (ANN). We employed computed tomography (CT) images of 86 squamous cell lung cancer (SCLC) patients for the feature selection, and 30 SCHNC patients for a test of the selected features. 486 radiomic features, i.e., statistic, texture, wavelet-based features, were extracted from the tumor regions in the CT images. The ANN was constructed for selecting 10 features that could classify the SCLC patients into shorter and longer survival groups than 2 years. The features were selected based on weights with strong links between the features and predicted survival in ANN. The survival times of the SCHNC patients, who were divided into two groups with respect to the median of each of the top 10 ranked features, were estimated using a Kaplan-Meier method. The statistical significant differences between survival curves of the two groups were assessed for the 10 features using a log-rank test. The homogeneity feature of the wavelet-based HHL image (HHL-Homogeneity) demonstrated a statistically significant difference (p < 0.01) between the two groups of SCHNC, but the other 9 features did not. Our results suggest that the 2-year survival of the SCHNC patients could be predicted by using at least the radiomic feature selected among the features for SCLC patients using the ANN-based feature selection approach.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImaging Informatics for Healthcare, Research, and Applications
EditorsPo-Hao Chen, Jianguo Zhang
PublisherSPIE
ISBN (Electronic)9781510616479
DOIs
Publication statusPublished - Jan 1 2018
EventMedical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications - Houston, United States
Duration: Feb 13 2018Feb 15 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10579
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
CountryUnited States
CityHouston
Period2/13/182/15/18

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Kamezawa, H., Arimura, H., & Soufi, M. (2018). Survival prediction of squamous cell head and neck cancer patients based on radiomic features selected from lung cancer patients using artificial neural network. In P-H. Chen, & J. Zhang (Eds.), Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications [1057918] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10579). SPIE. https://doi.org/10.1117/12.2293415