A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme
Open accessAbstract
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.
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Deep learning is applied in radiomics.
“Deep learning is applied in radiotherapy [89], in PET-MRI attenuation correction [90, 91], in radiomics [92, 93]”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [92]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Ostrom, Q. T., Gittleman, H. R., Farah, P., Ondracek, A., Chen, Y., Wolinsky, Y., Stroup, N. E., Kruchko, C. A., & Barnholtz‐Sloan, J. S. (2013) · Neuro-Oncology · doi:10.1093/neuonc/not151 · Cited by 12,049 · see citing works
Self-citationCaffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
3D Slicer as an image computing platform for the Quantitative Imaging Network
Fedorov, A., Beichel, R., Kalpathy–Cramer, J., Finet, J., Fillion‐Robin, J., Pujol, S., Bauer, C., Jennings, D. L., Fennessy, F., Milan Sonka, A., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.05.001 · Cited by 9,095 · see citing works
The cancer genome atlas pan-cancer analysis project
The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092 · see citing works
Radiomics: Images Are More than Pictures, They Are Data
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421 · see citing works
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354 · see citing works
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084 · see citing works
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147 · see citing works
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132 · see citing works
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213 · see citing works
Camp, R. L., Dolled-Filhart, M. P., & Rimm, D. L. (2004) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-04-0713 · Cited by 3,748 · see citing works
Fleiss, J. L., & Cohen, J. (1973) · Educational and Psychological Measurement · doi:10.1177/001316447303300309 · Cited by 3,354 · see citing works
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335 · see citing works
Dolecek, T. A., Propp, J. M., Stroup, N. E., & Kruchko, C. (2012) · Neuro-Oncology · doi:10.1093/neuonc/nos218 · Cited by 1,710 · see citing works
Pencina, M. J., & D’Agostino, R. B. (2004) · Statistics in Medicine · doi:10.1002/sim.1802 · Cited by 1,446 · see citing works
Huang, Y., Liu, Z., He, L., Chen, X., Pan, D., Ma, Z., Liang, C., Tian, J., & Liang, C. (2016) · Radiology · doi:10.1148/radiol.2016152234 · Cited by 741 · see citing works
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
Coroller, T. P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R. T. H., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R. H., & Aerts, H. J. W. L. (2015) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2015.02.015 · Cited by 695 · see citing works
Return of the Devil in the Details: Delving Deep into Convolutional NetsPreprint
Chatfield, K., Simonyan, K., Vedaldi, A., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.3531 · Cited by 663 · see citing works
Recent advances in the molecular understanding of glioblastoma
Bleeker, F. E., Molenaar, R. J., & Leenstra, S. (2012) · Journal of Neuro-Oncology · doi:10.1007/s11060-011-0793-0 · Cited by 453 · see citing works
Current trends in the surgical management and treatment of adult glioblastoma.
Young, R. M., Jamshidi, A. M., Davis, G., & Sherman, J. H. (2015) · Europe PMC (PubMed Central) · doi:10.3978/j.issn.2305-5839.2015.05.10 · Cited by 315 · see citing works
Kickingereder, P., Bonekamp, D., Nowosielski, M., Kratz, A., Sill, M., Burth, S., Wick, A., Eidel, O., Schlemmer, H., Radbruch, A., et al. (2016) · Radiology · doi:10.1148/radiol.2016161382 · Cited by 304 · see citing works
The mean and median absolute deviations
Pham-Gia, T., & Hùng, T. L. (2001) · Mathematical and Computer Modelling · doi:10.1016/s0895-7177(01)00109-1 · Cited by 299 · see citing works
Tibshirani (1997) · doi:10.1002/(sici)1097-0258(19970228)16:4
Flagged first, then by citations
Cited by
Sorted by citations
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00560-7 · Cited by 819 · see citing works
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579 · see citing works
Deep learning with convolutional neural network in radiology
Yasaka, K., Akai, H., Kunimatsu, A., Kiryu, S., & Abe, O. (2018) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0726-3 · Cited by 368 · see citing works
Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.009 · Cited by 226 · see citing works
Park, J. E., Kim, H. S., Kim, D., Park, S. Y., Kim, J. Y., Cho, S. J., & Kim, J. H. (2020) · BMC Cancer · doi:10.1186/s12885-019-6504-5 · Cited by 126 · see citing works
Tasci, E., Zhuge, Y., Camphausen, K., & Krauze, A. V. (2022) · Cancers · doi:10.3390/cancers14122897 · Cited by 117 · see citing works
Radiomics in radiooncology – challenging the medical physicist
Peeken, J. C., Bernhofer, M., Wiestler, B., Goldberg, T., Cremers, D., Rost, B., Wilkens, J. J., Combs, S. E., & Nüsslin, F. (2018) · Physica Medica · doi:10.1016/j.ejmp.2018.03.012 · Cited by 98 · see citing works
Kaissis, G., Ziegelmayer, S., Lohöfer, F., Steiger, K., Algül, H., Muckenhuber, A., Yen, H.-Y., Rummeny, E., Friess, H., Schmid, R., et al. (2019) · PLOS ONE · doi:10.1371/journal.pone.0218642 · Cited by 76 · see citing works
He, T., Fong, J. N., Moore, L. W., Ezeana, C. F., Victor, D., Divatia, M., Vasquez, M., Ghobrial, R. M., & Wong, S. T. C. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101894 · Cited by 63 · see citing works
Torrents-Barrena, J., Monill, N., Piella, G., Gratacós, E., Eixarch, E., Ceresa, M., & González Ballester, M. A. (2021) · Academic Radiology · doi:10.1016/j.acra.2019.11.006 · Cited by 42 · see citing works
Kang, H., Witanto, J. N., Pratama, K., Lee, D., Choi, K. S., Choi, S. H., Kim, K., Kim, M., Kim, J. W., Kim, Y. H., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28332 · Cited by 40 · see citing works
Wang, Y., Tu, D., Du, J., Han, X., Sun, Y., Xu, Q., Zhai, G., & Zhou, Y. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00627 · Cited by 25 · see citing works
Self-citationLee, H. G., Sholle, E., Beecy, A., Al’Aref, S., & Peng, Y. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.358 · Cited by 7 · see citing works
- Authors
- Lao, J., Chen, Y., Li, Z.-C., Li, Q., Zhang, J., Liu, J., & Zhai, G.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Sun Yat-sen University Cancer Center (CN)
- Chinese Academy of Sciences (CN)
- Tianjin University (CN)
- Corresponding author
- Zhai, Guangtao
- Venue
- Scientific Reports
- Published
- 2017-09-04
- Volume
- 7
- Issue
- 1
- Pages
- 10353-10353
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5583361
- OpenAlex
- W2751538714
- Cited by
- 630
- References
- 28
- Citations by year
- 2026: 23 · 2025: 51 · 2024: 58 · 2023: 95 · 2022: 95 · 2021: 122 · 2020: 78 · 2019: 73 · 2018: 33 · 2014: 1
- FWCI
- 38.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (973 Program, 6152106 2, 61371146, 2015CB755500, 61571432, 61422112)
- National High-tech Research and Development Program
- National Key Research and Development Program of China (61521062, 2015CB755500)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- glioblastoma, radiomics, deep learning, transfer learning, deep features, overall survival prediction, multimodal MRI, LASSO Cox regression, radiomics nomogram, patient stratification, preoperative imaging biomarkers
- MeSH terms
- Deep Learning, Adolescent, Adult, Aged, Aged, 80 and over, Child, Female, Glioblastoma, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Models, Theoretical, Prognosis, ROC Curve, Reproducibility of Results, Nomograms, Kaplan-Meier Estimate, Young Adult
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