Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Chou, Raoul Sallé de; Assi, Tarek; Touat, Mehdi; Chouzenoux, Émilie; Quillent, Arnaud; Limkin, Elaine Johanna; Dercle, Laurent; Hadchiti, Joya; Elhaik, Mickael; Moalla, Salma; Khettab, Mohamed; Balleyguier, Corinne; Lassau, Nathalie; Dumont, Sarah; Smolenschi, Cristina

doi:10.3390/diagnostics11071263

Cited by 9 publications

(7 citation statements)

References 46 publications

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“…Strength and sphericity were also important parameters in our model. These features were among those selected for the successful prediction of outcomes in GBM patients treated with bevacizumab in a previous publication [32]. Adding age as a clinical variable, which also appeared in several reported survival models, appeared to be highly relevant in both our cohorts.…”

Section: Discussionmentioning

confidence: 85%

A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

et al. 2021

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Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan–Meier method showed significant OS difference between groups (log-rank p = 0.05). These results suggest that radiomic signatures may improve survival outcome predictions in GBM thus creating a solid clinical tool for tailoring therapy in this population.

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Section: Discussionmentioning

confidence: 85%

A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

et al. 2021

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“…In a study by Bae et al [23], the authors found that adding radiomic features to patients' genetic and clinical profiles can improve survival prediction. Other studies reached a similar conclusion as MRI-based radiomics and machine learning (ML) algorithms could predict the OS of patients with decent statistics [24][25][26][27]. While several studies reported on the use of radiomics in neurodegenerative disorders or GBM, most have utilized ML to binary (high vs. low risk or short vs. long survival time) or multiple (high, intermediate, and low risk or short, intermediate, and long survival time) classification of OS in GBM patients.…”

Section: Introductionmentioning

confidence: 63%

“…They computed C-index using eight traditional and three As mentioned earlier, we used only radiomic features and clinical variables for OS prediction. Similar to our approach, Ammari et al [24] utilized radiomics and clinical data to stratify patients based on their survival. Using ML regression algorithms, they achieved a C-index of 0.64 for the prediction of OS.…”

Section: Discussionmentioning

confidence: 99%

Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics

Hajianfar,

Haddadi Avval,

Hosseini

et al. 2023

Radiol med

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Purpose Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms. Materials and methods One hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML algorithms were utilized. The combination of preprocessing, FS, and ML algorithms (12 × 7 × 6 = 504 models) was evaluated by multivariate analysis. Results Our multivariate analysis showed that the best prognostic FS/ML combinations are the Mutual Information (MI)/Cox Boost, MI/Generalized Linear Model Boosting (GLMB) and MI/Generalized Linear Model Network (GLMN), all of which were done via the LOG (Sigma = 1 mm) preprocessing method (C-index = 0.77). The LOG filter with Sigma = 1 mm preprocessing method, MI, GLMB and GLMN achieved significantly higher C-indices than other preprocessing, FS, and ML methods (all p values < 0.05, mean C-indices of 0.65, 0.70, and 0.64, respectively). Conclusion ML algorithms are capable of predicting the time-to-event OS of patients using MRI-based radiomic and clinical features. MRI-based radiomics analysis in combination with clinical variables might appear promising in assisting clinicians in the survival prediction of patients with GBM. Further research is needed to establish the applicability of radiomics in the management of GBM in the clinic.

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“…The advent of radiomics presented a novel non-invasive strategy to build survival risk models as well as to monitor response to various treatments, including ICIs [13,14,37,38]. Despite the growing field of biomarker research in immuno-oncology, none of these prognostic and predictive biomarkers have been translated to routine clinical use in the context of predicting patient outcomes to ICIs in a metastatic setting or to survival.…”

Section: Discussionmentioning

confidence: 99%

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study

Yolchuyeva

Giacomazzi

Tonneau

et al. 2023

Cancers

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Background: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. Methods: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. Results: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. Conclusions: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.

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Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Cited by 9 publications

References 46 publications

A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study

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