A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

By Jiangwei Lao, Yinsheng Chen, Zhicheng Li, Qihua Li, Ji Zhang, Jing Liu, Guangtao Zhai. 2017

553 citations recorded by the source corpus.

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.

Subfields: Genetics, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, neutral): “Deep learning is applied in radiotherapy [88], in PET-MRI attenuation correction [89,90], in radiomics [91,92] (see [93] for a review of radiomics related to radiooncology and medical physics), and for theranostics in neurosurgical imaging, combining confocal laser endomicroscopy with deep learning ”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, unknown): “…A and other molecular subtypes for breast cancer [241], predicting bladder cancer treatment response [242], IDH1 mutation status for LGG [243], [244], and MGMT methylation status for GBM [245], as well as predicting overall survival for GBM patients [246] and non-disease speciﬁc subjects [247].”
Deep learning in medical imaging and radiation therapy. (2018, background): “Lao et al. 218 investigated MRI radiomic features and DL as a means to predict survival in glioblastoma multiforme.”
A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, background): “In recent years, machine learning approaches, including deep learning, have been proposed to augment or replace current engineered radiomic features (43).”
Deep Learning in Medical Imaging (2019, background): “24,25 These deep features which are evaluated not by feature engineering (handcrafted) but by feature learning could contain more abstract information of medical images and provide more predictive patterns compared with the handcrafted features.”