Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Comparison and optimization of deep learning-based radiosensitivity prediction models using gene expression profiling in National Cancer Institute-60 cancer cell line

  • Kim, Euidam (Department of Nuclear Engineering, Hanyang University) ;
  • Chung, Yoonsun (Department of Nuclear Engineering, Hanyang University)
  • Received : 2021.10.25
  • Accepted : 2022.03.14
  • Published : 2022.08.25
Download PDF
⟨ Previous Next ⟩

Abstract

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

Keywords

Acknowledgement

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Education (NRF-2018R1D1A1B07049228).

References

  1. S.D. Bouffler, Evidence for variation in human radiosensitivity and its potential impact on radiological protection, Ann. ICRP 45 (2016) 280-289. https://doi.org/10.1177/0146645315623158
  2. D.G. Hirst, T. Robson, Molecular biology: the key to personalised treatment in radiation oncology? Br. J. Radiol. 83 (2010) 723-728. https://doi.org/10.1259/bjr/91488645
  3. H.S. Kim, S.C. Kim, S.J. Kim, C.H. Park, H.-C. Jeung, Y.B. Kim, et al., Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells, BMC Genom. 13 (2012) 348. https://doi.org/10.1186/1471-2164-13-348
  4. Q.E. He, Y.F. Tong, Z. Ye, L.X. Gao, Y.Z. Zhang, L. Wang, et al., A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy, Technol. Cancer Res. Treat. 19 (2020), 1533033820909112.
  5. J.F. Torres-Roca, S. Eschrich, H. Zhao, G. Bloom, J. Sung, S. McCarthy, et al., Prediction of radiation sensitivity using a gene expression classifier, Cancer Res 65 (2005) 7169-7176. https://doi.org/10.1158/0008-5472.CAN-05-0656
  6. C. Zhang, L. Girard, A. Das, S. Chen, G. Zheng, K. Song, Nonlinear quantitative radiation sensitivity prediction model based on NCI-60 cancer cell lines, Sci. World J. 2014 (2014) 903602.
  7. E. Kim, Y. Chung, Feasibility study of deep learning based radiosensitivity prediction model of National Cancer Institute-60 cell lines using gene expression, Nucl. Eng. Technol. (2021).
  8. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst. 25 (2012) 1097-1105.
  9. F. Murtagh, Multilayer perceptrons for classification and regression, Neurocomputing 2 (1991) 183-197. https://doi.org/10.1016/0925-2312(91)90023-5
  10. H. Sak, A.W. Senior, F. Beaufays, Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling, 2014.
  11. K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014 arXiv preprint arXiv:1409.1556.
  12. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going deeper with convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1-9.
  13. R.G. Bristow, P.A. Hardy, R.P. Hill, Comparison between in vitro radiosensitivity and in vivo radioresponse of murine tumor cell lines. I: parameters of in vitro radiosensitivity and endogenous cellular glutathione levels, Int. J. Radiat. Oncol. Biol. Phys. 18 (1990) 133-145. https://doi.org/10.1016/0360-3016(90)90277-q
  14. C.M. West, Invited review: intrinsic radiosensitivity as a predictor of patient response to radiotherapy, Br. J. Radiol. 68 (1995) 827-837. https://doi.org/10.1259/0007-1285-68-812-827
  15. S. Eschrich, H. Zhang, H. Zhao, D. Boulware, J.-H. Lee, G. Bloom, et al., Systems biology modeling of the radiation sensitivity network: a biomarker discovery platform, Int. J. Radiat. Oncol. Biol. Phys. 75 (2009) 497-505. https://doi.org/10.1016/j.ijrobp.2009.05.056
  16. T.D. Pfister, W.C. Reinhold, K. Agama, S. Gupta, S.A. Khin, R.J. Kinders, et al., Topoisomerase I levels in the NCI-60 cancer cell line panel determined by validated ELISA and microarray analysis and correlation with indenoisoquinoline sensitivity, Mol. Cancer Therapeut. 8 (2009) 1878-1884. https://doi.org/10.1158/1535-7163.MCT-09-0016
  17. M.W. Gardner, S.R. Dorling, Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences, Atmos. Environ. 32 (1998) 2627-2636. https://doi.org/10.1016/S1352-2310(97)00447-0
  18. J.M. Johnson, T.M. Khoshgoftaar, Survey on deep learning with class imbalance, J. Big Data 6 (2019) 27. https://doi.org/10.1186/s40537-019-0192-5
  19. H. Yu, D.C. Samuels, Y-y Zhao, Y. Guo, Architectures and accuracy of artificial neural network for disease classification from omics data, BMC Genom. 20 (2019) 167. https://doi.org/10.1186/s12864-019-5546-z
  20. A. Sharma, E. Vans, D. Shigemizu, K.A. Boroevich, T. Tsunoda, DeepInsight: a methodology to transform a non-image data to an image for convolution neural network architecture, Sci. Rep. 9 (2019) 11399. https://doi.org/10.1038/s41598-019-47765-6
  21. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.
  22. S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, D.J. Inman, 1D convolutional neural networks and applications: a survey, Mech. Syst. Signal Process. 151 (2021) 107398. https://doi.org/10.1016/j.ymssp.2020.107398
  23. T. Fushiki, Estimation of prediction error by using K-fold cross-validation, Stat. Comput. 21 (2011) 137-146. https://doi.org/10.1007/s11222-009-9153-8
  24. L.J. Peters, The ESTRO Regaud lecture. Inherent radiosensitivity of tumor and normal tissue cells as a predictor of human tumor response, Radiother. Oncol. 17 (1990) 177-190. https://doi.org/10.1016/0167-8140(90)90202-8
  25. S. Min, B. Lee, S. Yoon, Deep learning in bioinformatics, Briefings Bioinf. 18 (2016) 851-869. https://doi.org/10.1093/bib/bbw068
  26. G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700-4708.