Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Tang, Zhenyu; Xu, Yuqian; Jin, Lei; Aibaidula, Abudumijiti; Lu, Junfeng; Jiao, Zhicheng; Wu, Jinsong; Zhang, Han; Shen, Dinggang

doi:10.1109/tmi.2020.2964310

Cited by 63 publications

(40 citation statements)

References 52 publications

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“…Several studies have designed radiomics models to predict the survival outcomes of glioblastoma [ 10 , 11 , 12 , 13 , 15 , 16 ]. Lao et al [ 11 ] built a radiomics model with handcrafted and deep features from a dataset describing 75 patients.…”

Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, no studies except one have built deep learning-based models to predict OS as a continuous variable, using both clinical and radiomic data together [ 12 ]. In the exceptional study just referred to, the CNN-based model using image data and clinical/genomic features showed a lower RMSE and higher correlation coefficient (177.0 ± 130.0 and 0.4695, respectively) than the random survival forest-based model (225.0 ± 136.0 and 0.1151, respectively) or the CNN-based model using magnetic resonance imaging (MRI) data only (261.0 ± 175.0 and 0.0587, respectively).…”

Section: Discussionmentioning

confidence: 99%

“…The development and improvement of numerous computational algorithms (such as machine learning) have rendered these “radiomic” studies more feasible. Using machine learning, many recent studies have built prediction models that take radiomic features as inputs and predict several clinical variables for glioblastoma, including survival outcomes [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. However, these studies have several limitations.…”

Section: Introductionmentioning

confidence: 99%

“…Considering that there have been already several studies where clinical metrics were married to radiological measures for the evaluation, this problem becomes more significant [ 19 , 20 , 21 ]. Furthermore, very few studies predicted survival outcomes as continuous—rather than categorical (long- or short-term)—variables in glioblastoma patients [ 10 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients

Yoon

Cheon

Jeong

et al. 2020

Cancers

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This study aimed to investigate the performance of a deep learning-based survival-prediction model, which predicts the overall survival (OS) time of glioblastoma patients who have received surgery followed by concurrent chemoradiotherapy (CCRT). The medical records of glioblastoma patients who had received surgery and CCRT between January 2011 and December 2017 were retrospectively reviewed. Based on our inclusion criteria, 118 patients were selected and semi-randomly allocated to training and test datasets (3:1 ratio, respectively). A convolutional neural network–based deep learning model was trained with magnetic resonance imaging (MRI) data and clinical profiles to predict OS. The MRI was reconstructed by using four pulse sequences (22 slices) and nine images were selected based on the longest slice of glioblastoma by a physician for each pulse sequence. The clinical profiles consist of personal, genetic, and treatment factors. The concordance index (C-index) and integrated area under the curve (iAUC) of the time-dependent area-under-the-curve curves of each model were calculated to evaluate the performance of the survival-prediction models. The model that incorporated clinical and radiomic features showed a higher C-index (0.768 (95% confidence interval (CI): 0.759, 0.776)) and iAUC (0.790 (95% CI: 0.783, 0.797)) than the model using clinical features alone (C-index = 0.693 (95% CI: 0.685, 0.701); iAUC = 0.723 (95% CI: 0.716, 0.731)) and the model using radiomic features alone (C-index = 0.590 (95% CI: 0.579, 0.600); iAUC = 0.614 (95% CI: 0.607, 0.621)). These improvements to the C-indexes and iAUCs were validated using the 1000-times bootstrapping method; all were statistically significant (p < 0.001). This study suggests the synergistic benefits of using both clinical and radiomic parameters. Furthermore, it indicates the potential of multi-parametric deep learning models for the survival prediction of glioblastoma patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients

Yoon

Cheon

Jeong

et al. 2020

Cancers

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“…AI has already proven to be advantageous for computer-aided diagnosis in medical imaging, such as for the differential diagnosis of coronavirus disease 2019 [ 3 ], skin cancer [ 4 ], and diabetic retinopathy [ 5 ]. Moreover, it has been developed to help identify imaging-based biomarkers, leading to an improvement in the prognosis of, for example, lung cancer [ 6 , 7 ], gliomas [ 8 ], and nasopharynx cancer [ 9 ]. Deep learning is an indispensable part of AI and has been reported to be extremely effective in several medical imaging-related tasks, such as image segmentation, registration, fusion, annotation, computer-aided diagnosis and prognosis analyses, lesion and landmark detection, and microscopic imaging analysis.…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

Guo

Song

et al. 2021

Eur J Nucl Med Mol Imaging

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Purpose To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment 18F-FDG PET/CT results. Methods One hundred and sixty-seven patients with ENKTL who underwent pretreatment 18F-FDG PET/CT were retrospectively collected. Eighty-four patients were followed up for at least 2 years (training set = 64, test set = 20). A WSDL method was developed to enable the integration of the remaining 83 patients with incomplete/missing follow-up information in the training set. To test generalization, these data were derived from three types of scanners. Prediction similarity index (PSI) was derived from deep learning features of images. Its discriminative ability was calculated and compared with that of a conventional deep learning (CDL) method. Univariate and multivariate analyses helped explore the significance of PSI and clinical features. Results PSI achieved area under the curve scores of 0.9858 and 0.9946 (training set) and 0.8750 and 0.7344 (test set) in the prediction of progression-free survival (PFS) with the WSDL and CDL methods, respectively. PSI threshold of 1.0 could significantly differentiate the prognosis. In the test set, WSDL and CDL achieved prediction sensitivity, specificity, and accuracy of 87.50% and 62.50%, 83.33% and 83.33%, and 85.00% and 75.00%, respectively. Multivariate analysis confirmed PSI to be an independent significant predictor of PFS in both the methods. Conclusion The WSDL-based framework was more effective for extracting 18F-FDG PET/CT features and predicting the prognosis of ENKTL than the CDL method.

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Deep learning in precision medicine and focus on glioma

Liu

2023

Bioengineering & Transla Med

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Deep learning (DL) has been successfully applied to different fields for a range of tasks. In medicine, DL methods have been also used to improve the efficiency of disease diagnosis. In this review, we first summarize the history of the development of artificial intelligence models, demonstrate the features of the subtypes of machine learning and different DL networks, and then explore their application in the different fields of precision medicine, such as cardiology, gastroenterology, ophthalmology, dermatology, and oncology. By digging more information and extracting multilevel features from medical data, we found that DL helps doctors assess diseases automatically and monitor patients' physical health. In gliomas, research regarding application prospect of DL was mainly shown through magnetic resonance imaging and then by pathological slides. However, multi‐omics data, such as whole exome sequence, RNA sequence, proteomics, and epigenomics, have not been covered thus far. In general, the quality and quantity of DL datasets still need further improvements, and more fruitful multi‐omics characteristics will bring more comprehensive and accurate diagnosis in precision medicine and glioma.

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Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Cited by 63 publications

References 52 publications

Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients

Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients

Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

Deep learning in precision medicine and focus on glioma

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