Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas

Yutao Wang; Qian Shao; Shuying Luo; Randi Fu; Yutao Wang; Qian Shao; Shuying Luo; Randi Fu

doi:10.3934/mbe.2021401

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 8084-8095. doi: 10.3934/mbe.2021401

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Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas

1.
Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
2.
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

Received: 21 July 2021 Accepted: 09 September 2021 Published: 16 September 2021

The purpose of this study was to assess the overall survival of patients with HGG using a nomogram which combines the optimized radiomics with deep signatures extracted from 3D Magnetic Resonance Images (MRI) as well as clinical predictors. One training cohort of 168 HGG patients and one validation cohort of 42 HGG patients were enrolled in this study. From each patient's 3D MRI, 1284 radiomics features were extracted, and 8192 deep features were extracted via transfer learning. By using Least Absolute Shrinkage and Selection Operator (LASSO) regression to select features, the radiomics signatures and deep signatures were generated. The radiomics and deep features were then analyzed synthetically to generate a combined signature. Finally, the nomogram was developed by integrating the combined signature and clinical predictors. The radiomics and deep signatures were significantly associated with HGG patients' survival time. The signature derived from the synthesized radiomics and deep features showed a better prognostic performance than those from radiomics or deep features alone. The nomogram we developed takes the advantages of both radiomics and deep signatures, and also integrates the predictive ability of clinical indicators. The calibration curve shows our predicted survival time by the nomogram was very close to the actual time.
- high grade gliomas,
- radiomics,
- transfer learning,
- Magnetic Resonance Imaging,
- nomogram
Citation: Yutao Wang, Qian Shao, Shuying Luo, Randi Fu. Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 8084-8095. doi: 10.3934/mbe.2021401

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Abstract

The purpose of this study was to assess the overall survival of patients with HGG using a nomogram which combines the optimized radiomics with deep signatures extracted from 3D Magnetic Resonance Images (MRI) as well as clinical predictors. One training cohort of 168 HGG patients and one validation cohort of 42 HGG patients were enrolled in this study. From each patient's 3D MRI, 1284 radiomics features were extracted, and 8192 deep features were extracted via transfer learning. By using Least Absolute Shrinkage and Selection Operator (LASSO) regression to select features, the radiomics signatures and deep signatures were generated. The radiomics and deep features were then analyzed synthetically to generate a combined signature. Finally, the nomogram was developed by integrating the combined signature and clinical predictors. The radiomics and deep signatures were significantly associated with HGG patients' survival time. The signature derived from the synthesized radiomics and deep features showed a better prognostic performance than those from radiomics or deep features alone. The nomogram we developed takes the advantages of both radiomics and deep signatures, and also integrates the predictive ability of clinical indicators. The calibration curve shows our predicted survival time by the nomogram was very close to the actual time.

References

[1]	J. M. Fahey, A. W. Girotti, Nitric Oxide antagonism to anti-glioblastoma photodynamic therapy: Mitigation by inhibitors of nitric oxide generation, Cancers, 11 (2019), 1-15.
[2]	G. Chi, F. Yang, D. Xu, W. Liu, Silencing hsa_circ_PVT1 (circPVT1) suppresses the growth and metastasis of glioblastoma multiforme cells by up-regulation of miR-199a-5p, Artif. Cell. Nanomed. B, 48 (2020), 188-196. doi: 10.1080/21691401.2019.1699825
[3]	T. A. Dolecek, J. M. Propp, N. E. Stroup, C. Kruchko, CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005-2009, Neuro-Oncology, 14 (2012), 1-49. doi: 10.1093/neuonc/nor225
[4]	M. Li, B. Li, J. Luo, J. Liang, F. Pan, Y. Zheng, et al., Ultrasound-based radiomics model in predicting efficacy of neoadjuvant chemotherapy in breast cancer, Chin. J. Med. Imag. Tech., 35 (2019), 1331-1335.
[5]	H. J. W. L. Aerts, E. R. Velazquez, R. T. H. Leijenaar, C. Parmar, P. Grossmann, S. Carvalho, et al., Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat. Commun., 5 (2014), 4006. doi: 10.1038/ncomms5006
[6]	C. Parmar, P. Grossmann, D. Rietveld, M. M. Rietbergen, P. Lambin, H. J. W. L. Aerts, Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer, Front. Oncol., 5 (2015), 1-10.
[7]	L. He, Y. Q. Huang, Z. L. Ma, C. S. Liang, C. H. Liang, Z. Y. Liu, Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule, Rep, 6 (2016), 34921.
[8]	T. P. Coroller, P. Grossmann, Y. Hou, E. R. Velazquez, R. T. H. Leijenaar, G. Hermann, et al., CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma, Radiother. Oncol., 114 (2015), 345-350. doi: 10.1016/j.radonc.2015.02.015
[9]	P. Kickingereder, D. Bonekamp, M. Nowosielski, A. Kratz, M. Sill, S. Burth, et al., Radiogenomics of glioblastoma: Machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features, Radiology, 281 (2016), 907-918. doi: 10.1148/radiol.2016161382
[10]	A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, F. F. Li, Large-scale video classification with convolutional neural networks, IEEE Conf. Comput. Vis. Pattern Recog., (2014), 1725-1732.
[11]	G. E. Hinton, R. R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, 313 (2006), 504-507. doi: 10.1126/science.1127647
[12]	Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE, 86 (1998), 2278-2324. doi: 10.1109/5.726791
[13]	K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun, Deep residual learning for image recognition, IEEE Conf. Comput. Vis. Pattern Recog., (2016), 770-778.
[14]	L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, Semantic image segmentation with deep convolutional nets and fully connected CRFs, OALib J., 4 (2014), 357-361.
[15]	S. J. Pan, Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Engin., 22 (2010), 1345-1359. doi: 10.1109/TKDE.2009.191
[16]	A. Kensert, P. J Harrison, O. Spjuth, Transfer learning with deep convolutional neural networks for classifying cellular morphological changes, Slas Discov., 24 (2019), 466-475.
[17]	S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kerby, et al., Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features, Sci. Data, 4 (2017), 170117. doi: 10.1038/sdata.2017.117
[18]	S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., Identifying the best machine learning algorithms for brain tumor segmentation, Prog. Assess. Overall Surv. Predic. BRATS Chall., (2018), 1-49.
[19]	B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., The Multimodal Brain TumorImage Segmentation Benchmark (BRATS), IEEE Trans. Med. Imag., 10 (2015), 1993-2024.
[20]	J. J. M. van Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, et al., Computational radiomics system to decode the radiographic phenotype, Cancer Res., 77 (2017), 104-107. doi: 10.1158/0008-5472.CAN-17-0339
[21]	S. Chen, K. Ma, Y. Zheng, Med3D: Transfer learning for 3D medical image analysis, preprint, arXiv: 1904.00625.
[22]	N. Meinshausen, P. Bühlmann, High-dimensional graphs and variable selection with the Lasso, Ann. Statist., 34 (2006), 1436-1462.
[23]	L. Yang, J. Yang, X. Zhou, L. Huang, W. Zhao, T. Wang, et al., Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients, Eur. Radiol., 29 (2019), 2196-2206. doi: 10.1007/s00330-018-5770-y
[24]	T. Sato, G. Berry, A comparison of two simple hazard ratio estimators based on the logrank test, Stats. Med., 10 (2010), 749-755.
[25]	J. R. Figueira, S. Greco, B. Roy, ELECTRE methods with interaction between criteria: An extension of the concordance index, Eur. J. Oper. Res., 199 (2009), 478-495. doi: 10.1016/j.ejor.2008.11.025
[26]	T. M. Therneau, P. M. Grambsch, Modeling survival data: Extending the Cox Model, Springer Science & Business Media, Des Moines, 2013.
[27]	K. G M Moons, D. G Altman, J. B Reitsma, J. P. A. Ioannidis, P. Macaskill, E. W Steyerberg, et al., Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration, Ann. Intern. Med., 162 (2015), 1-73. doi: 10.7326/M13-2729

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