A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas

2022 ◽  
pp. 110141
Author(s):  
Jie Wang ◽  
Yue Hu ◽  
Xuejun Zhou ◽  
Shanlei Bao ◽  
Yue Chen ◽  
...  
Keyword(s):  
Idh1 Mutation ◽  
Dce Mri ◽  
Model Based

scholarly journals Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Mingming Ma ◽  
Liangyu Gan ◽  
Yuan Jiang ◽  
Naishan Qin ◽  
Changxin Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Cross Validation ◽  
Triple Negative ◽  
Characteristic Curve ◽  
Three Dimensional ◽  
Training Dataset ◽  
Breast Cancer Patients ◽  
Dce Mri ◽  
Model Based

Purpose. To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non-TNBC). Materials and Methods. This retrospective study included DCE-MRI images of 81 breast cancer patients (44 TNBC and 37 non-TNBC) from August 2018 to October 2019. The MR scans were achieved at a 1.5 T MR scanner. For each patient, the largest tumor mass was selected to analyze. Three-dimensional (3D) images of the regions of interest (ROIs) were automatically segmented on the third DCE phase by a deep learning segmentation model; then, the ROIs were checked and revised by 2 radiologists. DCE-MRI radiomics features were extracted from the 3D tumor volume. The patients were randomly divided into training ( N = 57 ) and test ( N = 24 ) cohorts. The machine learning classifier was built in the training dataset, and 5-fold cross-validation was performed on the training cohort to train and validate. The data of the test cohort were used to investigate the predictive power of the radiomics model in predicting TNBC and non-TNBC. The performance of the model was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results. The radiomics model based on 15 features got the best performance. The AUC achieved 0.741 for the cross-validation, and 0.867 for the independent testing cohort. Conclusion. The radiomics model based on automatic image segmentation of DCE-MRI can be used to distinguish TNBC and non-TNBC.

scholarly journals DCE-MRI of locally-advanced carcinoma of the uterine cervix: Tofts analysis versus non-model-based analyses

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Kjersti V. Lund ◽  
Trude G. Simonsen ◽  
Gunnar B. Kristensen ◽  
Einar K. Rofstad
Keyword(s):  
Uterine Cervix ◽  
Locally Advanced ◽  
Dce Mri ◽  
Advanced Carcinoma ◽  
Model Based

scholarly journals Model-based blind estimation of kinetic parameters in dynamic contrast enhanced (DCE)-MRI

2009 ◽  
Vol 62 (6) ◽  
pp. 1477-1486 ◽  
Author(s):  
Jacob U. Fluckiger ◽  
Matthias C. Schabel ◽  
Edward V.R. DiBella
Keyword(s):  
Kinetic Parameters ◽  
Blind Estimation ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Model Based ◽  
Contrast Enhanced

scholarly journals Non-Invasive Estimation of Glioma IDH1 Mutation and VEGF Expression by Histogram Analysis of Dynamic Contrast-Enhanced MRI

2020 ◽  
Vol 10 ◽  
Author(s):  
Yue Hu ◽  
Yue Chen ◽  
Jie Wang ◽  
Jin Juan Kang ◽  
Dan Dan Shen ◽  
...  
Keyword(s):  
Roc Curve ◽  
Idh1 Mutation ◽  
Curve Analysis ◽  
Histogram Analysis ◽  
Roc Curve Analysis ◽  
Transfer Constant ◽  
Mutant Type ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

ObjectivesTo investigate whether glioma isocitrate dehydrogenase (IDH) 1 mutation and vascular endothelial growth factor (VEGF) expression can be estimated by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).MethodsChinese Glioma Genome Atlas (CGGA) database was wined for differential expression of VEGF in gliomas with different IDH genotypes. The VEGF expression and IDH1 genotypes of 56 glioma samples in our hospital were assessed by immunohistochemistry. Preoperative DCE-MRI data of glioma samples were reviewed. Regions of interest (ROIs) covering tumor parenchyma were delineated. Histogram parameters of volume transfer constant (Ktrans) and volume of extravascular extracellular space per unit volume of tissue (Ve) derived from DCE-MRI were obtained. Histogram parameters of Ktrans, Ve and VEGF expression of IDH1 mutant type (IDH1mut) gliomas were compared with the IDH1 wildtype (IDH1wt) gliomas. Receiver operating characteristic (ROC) curve analysis was performed to differentiate IDH1mut from IDH1wt gliomas. The correlation coefficients were determined between histogram parameters of Ktrans, Ve and VEGF expression in gliomas.ResultsIn CGGA database, VEGF expression in IDHmut gliomas was lower as compared to wildtype counterpart. The immunohistochemistry of glioma samples in our hospital also confirmed the results. Comparisons demonstrated statistically significant differences in histogram parameters of Ktransand Ve [mean, standard deviation (SD), 50th, 75th, 90th. and 95th percentile] between IDH1mutand IDH1wtgliomas (P < 0.05, respectively). ROC curve analysis revealed that 50th percentile of Ktrans (0.019 min−1) and Ve (0.039) provided the perfect combination of sensitivity and specificity in differentiating gliomas with IDH1mutfrom IDH1wt. Irrespective of IDH1 mutation, histogram parameters of Ktransand Ve were correlated with VEGF expression in gliomas (P < 0.05, respectively).ConclusionsVEGF expression is significantly lower in IDH1mut gliomas as compared to the wildtype counterpart, and it is non-invasively predictable with histogram analysis of DCE-MRI.

scholarly journals Principal component analysis of breast DCE-MRI adjusted with a model-based method

2009 ◽  
Vol 30 (5) ◽  
pp. 989-998 ◽  
Author(s):  
Erez Eyal ◽  
Daria Badikhi ◽  
Edna Furman-Haran ◽  
Fredrick Kelcz ◽  
Kevin J. Kirshenbaum ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Dce Mri ◽  
Model Based

A Novel Model-Based Measure for Quality Evaluation of Image Registration Techniques in DCE-MRI

2014 ◽  
Author(s):  
Stefano Marrone ◽  
Gabriele Piantadosi ◽  
Roberta Fusco ◽  
Antonella Petrillo ◽  
Mario Sansone ◽  
...  
Keyword(s):  
Image Registration ◽  
Quality Evaluation ◽  
Dce Mri ◽  
Model Based ◽  
Novel Model

scholarly journals The Role of Patient- and Treatment-Related Factors and Early Functional Imaging in Late Radiation-Induced Xerostomia in Oropharyngeal Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6296
Author(s):  
Simona Marzi ◽  
Alessia Farneti ◽  
Laura Marucci ◽  
Pasqualina D’Urso ◽  
Antonello Vidiri ◽  
...  
Keyword(s):  
Prediction Models ◽  
Quantitative Imaging ◽  
Locally Advanced ◽  
Radiation Therapy Oncology Group ◽  
Related Factors ◽  
Dce Mri ◽  
Model Based ◽  
Intrinsic Radiosensitivity ◽  
Magnetic Resonance Imaging Mri

The advent of quantitative imaging in personalized radiotherapy (RT) has offered the opportunity for a better understanding of individual variations in intrinsic radiosensitivity. We aimed to assess the role of magnetic resonance imaging (MRI) biomarkers, patient-related factors, and treatment-related factors in predicting xerostomia 12 months after RT (XER12) in patients affected by oropharyngeal squamous cell carcinoma (OSCC). Patients with locally advanced OSCC underwent diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline; DWI was repeated at the 10th fraction of RT. The Radiation Therapy Oncology Group (RTOG) toxicity scale was used to evaluate salivary gland toxicity. Xerostomia-related questionnaires (XQs) were administered weekly during and after RT. RTOG toxicity ≥ grade 2 at XER12 was considered as endpoint to build prediction models. A Decision Tree classification learner was applied to build the prediction models following a five-fold cross-validation. Of the 89 patients enrolled, 63 were eligible for analysis. Thirty-six (57.1%) and 21 (33.3%) patients developed grade 1 and grade 2 XER12, respectively. Including only baseline variables, the model based on DCE-MRI and V65 (%) (volume of both glands receiving doses ≥ 65 Gy) had a fair accuracy (77%, 95% CI: 66.5–85.4%). The model based on V65 (%) and XQ-Intmid (integral of acute XQ scores from the start to the middle of RT) reached the best accuracy (81%, 95% CI: 71–88.7%). In conclusion, non-invasive biomarkers from DCE-MRI, in combination with dosimetric variables and self-assessed acute XQ scores during treatment may help predict grade 2 XER12 with a fair to good accuracy.

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