Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Ammari S

PROVIDER: S-EPMC8624566 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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

Ammari Samy S Sallé de Chou Raoul R Balleyguier Corinne C Chouzenoux Emilie E Touat Mehdi M Quillent Arnaud A Dumont Sarah S Bockel Sophie S Garcia Gabriel C T E GCTE Elhaik Mickael M Francois Bidault B Borget Valentin V Lassau Nathalie N Khettab Mohamed M Assi Tarek T

Diagnostics (Basel, Switzerland) 20211104 11

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 diagnos ...[more]

PMID: 34829395

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Project description:ObjectiveTo develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma.Materials and methodsOne hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (Ktrans), fractional volume of vascular plasma space (Vp), and fractional volume of extravascular extracellular space (Ve) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the "radiomics risk score" groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates.Results16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (IDH) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively).ConclusionWe developed and validated the "radiomics risk score" from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.

Project description:ObjectiveTongue squamous cell carcinoma (TSCC) accounts for 43.4% of oral cancers in China and has a poor prognosis. This study aimed to explore whether radiomics features extracted from preoperative magnetic resonance imaging (MRI) could predict overall survival (OS) in patients with TSCC.MethodsThe clinical imaging data of 232 patients with pathologically confirmed TSCC at Xiangyang No. 1 People's Hospital were retrospectively analyzed from February 2010 to October 2022. Based on 2-10 years of follow-up, patients were categorized into two groups: control (healthy survival, n = 148) and research (adverse events: recurrence or metastasis-related death, n = 84). A training and a test set were established using a 7:3 ratio and a time node. Radiomics features were extracted from axial T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging (DWI) sequences. The corresponding radiomics scores were generated using the least absolute shrinkage and selection operator algorithm. Kaplan-Meier and multivariate Cox regression analyses were used to screen for independent factors affecting adverse events in patients with TSCC using clinical and pathological results. A novel nomogram was established to predict the probability of adverse events and OS in patients with TSCC.ResultsThe incidence of adverse events within 2-10 years after surgery was 36.21%. Kaplan-Meier analysis revealed that hot pot consumption, betel nut chewing, platelet-lymphocyte ratio, drug use, neutrophil-lymphocyte ratio, Radscore, and other factors impacted TSCC survival. Multivariate Cox regression analysis revealed that the clinical stage (P < 0.001), hot pot consumption (P < 0.001), Radscore 1 (P = 0.01), and Radscore 2 (P < 0.001) were independent factors affecting TSCC-OS. The same result was validated by the XGBoost algorithm. The nomogram based on the aforementioned factors exhibited good discrimination (C-index 0.86/0.81) and calibration (P > 0.05) in the training and test sets, accurately predicting the risk of adverse events and survival.ConclusionThe nomogram constructed using clinical data and MRI radiomics parameters may accurately predict TSCC-OS noninvasively, thereby assisting clinicians in promptly modifying treatment strategies to improve patient prognosis.

Project description:BackgroundPre-eclampsia (PE) is the main cause of death in maternal and prenatal morbidity. No effective clinical tools could be used for the prediction of PE. A radiomics nomogram based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps was established to predict PE from gestational hypertension (GH).Materials and methodsA total of 138 patients with hypertensive disorders of pregnancy were continuously enrolled in the study prospectively, namely, 58 patients with PE and 80 patients with GH. The patients were randomly divided into a training cohort (n = 97) and a test cohort (n = 41). Radiomics features were extracted from DWI and ADC maps. The radiomics signature was constructed using a least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. A radiomics nomogram was developed by combining the radiomics signature with the selected clinical risk factors. The area under the receiver operating characteristic (ROC) curves (AUC), specificity, sensitivity, accuracy, positive predictive value, and negative predictive values of the radiomics signature, clinical risk factors, and radiomics nomogram were calculated. Decision curve analysis (DCA) was performed to determine the clinical usefulness of the radiomics nomogram.ResultsThe LASSO analysis finally included 11 radiomics features, which were defined as the radiomics signature. The individualized prediction nomogram was constructed by integrating the radiomics signature, maternal age, and body mass index (BMI). The nomogram exhibited a good performance both in the training cohort [AUC of 0.89 (95% CI, 0.82-0.95)] and test cohort [AUC of 0.85 (95% CI, 0.73-0.97)] for predicting PE from GH. The DCA indicated that clinicians and patients could benefit from the use of radiomics nomogram.ConclusionThe radiomics nomogram could individually predict PE from GH. The nomogram could be conveniently used to facilitate the treatment decision for clinicians and patients.

Project description:ObjectivesTo develop and validate a pre-transcatheter arterial chemoembolization (TACE) MRI-based radiomics model for predicting tumor response in intermediate-advanced hepatocellular carcinoma (HCC) patients.MaterialsNinety-nine intermediate-advanced HCC patients (69 for training, 30 for validation) treated with TACE were enrolled. MRI examinations were performed before TACE, and the efficacy was evaluated according to the mRECIST criterion 3 months after TACE. A total of 396 radiomics features were extracted from T2-weighted pre-TACE images, and least absolute shrinkage and selection operator (LASSO) regression was applied to feature selection and model construction. The performance of the model was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curves.ResultsThe AFP value, Child-Pugh score, and BCLC stage showed a significant difference between the TACE response (TR) and non-TACE response (nTR) patients. Six radiomics features were selected by LASSO and the radiomics score (Rad-score) was calculated as the sum of each feature multiplied by the non-zero coefficient from LASSO. The AUCs of the ROC curve based on Rad-score were 0.812 and 0.866 in the training and validation cohorts, respectively. To improve the diagnostic efficiency, the Rad-score was further integrated with the above clinical indicators to form a novel predictive nomogram. Results suggested that the AUC increased to 0.861 and 0.884 in the training and validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful.ConclusionThe radiomics and clinical indicator-based predictive nomogram can well predict TR in intermediate-advanced HCC and can further be applied for auxiliary diagnosis of clinical prognosis.Key points• The therapeutic outcome of TACE varies greatly even for patients with the same clinicopathologic features. • Radiomics showed excellent performance in predicting the TACE response. • Decision curves demonstrated that the novel predictive model based on the radiomics signature and clinical indicators has great clinical utility.

Dataset Information

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

Publications

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

Similar Datasets

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