Repeatability of Multiparametric Prostate MRI Radiomics Features.
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ABSTRACT:
SUBMITTER: Schwier M
PROVIDER: S-EPMC6602944 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective is to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.
2016-06-07 | GSE80683 | GEO
Project description:Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal-Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm-Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859-0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.
| S-EPMC8699138 | biostudies-literature
Project description:PurposeTo develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2).MethodsT2w, DWI (12 b values, 0-2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS.ResultsIn total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82-0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments.ConclusionTexture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.
| S-EPMC6613688 | biostudies-literature
Project description:BackgroundRenal multiparametric magnetic resonance imaging (MRI) is a promising tool for diagnosis, prognosis, and treatment monitoring in kidney disease.PurposeTo determine intrasubject test-retest repeatability of renal MRI measurements.Study typeProspective.PopulationNineteen healthy subjects aged over 40 years.Field strength/sequencesT1 and T2 mapping, R2 * mapping or blood oxygenation level-dependent (BOLD) MRI, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI), 2D phase contrast, arterial spin labelling (ASL), dynamic contrast enhanced (DCE) MRI, and quantitative Dixon for fat quantification at 3T.AssessmentSubjects were scanned twice with ~1 week between visits. Total scan time was ~1 hour. Postprocessing included motion correction, semiautomated segmentation of cortex and medulla, and fitting of the appropriate signal model.Statistical testTo assess the repeatability, a Bland-Altman analysis was performed and coefficients of variation (CoVs), repeatability coefficients, and intraclass correlation coefficients were calculated.ResultsCoVs for relaxometry (T1 , T2 , R2 */BOLD) were below 6.1%, with the lowest CoVs for T2 maps and highest for R2 */BOLD. CoVs for all diffusion analyses were below 7.2%, except for perfusion fraction (FP ), with CoVs ranging from 18-24%. The CoV for renal sinus fat volume and percentage were both around 9%. Perfusion measurements were most repeatable with ASL (cortical perfusion only) and 2D phase contrast with CoVs of 10% and 13%, respectively. DCE perfusion had a CoV of 16%, while single kidney glomerular filtration rate (GFR) had a CoV of 13%. Repeatability coefficients (RCs) ranged from 7.7-87% (lowest/highest values for medullary mean diffusivity and cortical FP , respectively) and intraclass correlation coefficients (ICCs) ranged from -0.01 to 0.98 (lowest/highest values for cortical FP and renal sinus fat volume, respectively).Data conclusionCoVs of most MRI measures of renal function and structure (with the exception of FP and perfusion as measured by DCE) were below 13%, which is comparable to standard clinical tests in nephrology.Level of evidence2 TECHNICAL EFFICACY: Stage 1.
| S-EPMC7891585 | biostudies-literature
Project description:Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.Global gene expression profiles were generated from 17 mpMRI-directed diagnostic prostate biopsies using an Affimetrix platform. Spatially distinct imaging areas ('habitats') were identified on MRI/3D-Ultrasound fusion. Radiomic features were extracted from biopsy regions and normal appearing tissues. We correlated 49 radiomic features with three clinically available gene signatures associated with adverse outcome. The signatures contain genes that are over-expressed in aggressive prostate cancers and genes that are under-expressed in aggressive prostate cancers. There were significant correlations between these genes and quantitative imaging features, indicating the presence of prostate cancer prognostic signal in the radiomic features. Strong associations were also found between the radiomic features and significantly expressed genes. Gene ontology analysis identified specific radiomic features associated with immune/inflammatory response, metabolism, cell and biological adhesion. To our knowledge, this is the first study to correlate radiogenomic parameters with prostate cancer in men with MRI-guided biopsy.
| S-EPMC5288193 | biostudies-literature
Project description:Background: To identify multiparametric magnetic resonance imaging (mp-MRI)-based radiomics features as prognostic factors in patients with localized prostate cancer after radiotherapy. Methods:From 2011 to 2016, a total of 91 consecutive patients with T1-4N0M0 prostate cancer were identified and divided into two cohorts for an adaptive boosting (Adaboost) model (training cohort: n = 73; test cohort: n = 18). All patients were treated with neoadjuvant endocrine therapy followed by radiotherapy. The optimal feature set, identified through an Inception-Resnet v2 network, consisted of a combination of T1, T2, and diffusion-weighted imaging (DWI) MR series. Through a Wilcoxon sign rank test, a total of 45 distinct signatures were extracted from 1,536 radiomics features and used in our Adaboost model. Results:Among 91 patients, 29 (32%) were classified as biochemical recurrence (BCR) and 62 (68%) as non-BCR. Once trained, the model demonstrated a predictive classification accuracy of 50.0 and 86.1% respectively for BCR and non-BCR groups on our test samples. The overall classification accuracy of the test cohort was 74.1%. The highest classification accuracy was 77.8% between three-fold cross-validation. The areas under the curve (AUC) of receiver operating characteristic curve (ROC) indices for the training and test cohorts were 0.99 and 0.73, respectively. Conclusion:The potential of multiparametric MRI-based radiomics to predict the BCR of localized prostate cancer patients was demonstrated in this manuscript. This analysis provided additional prognostic factors based on routine MR images and holds the potential to contribute to precision medicine and inform treatment management.
| S-EPMC7235325 | biostudies-literature
Project description:In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.
| S-EPMC8773614 | biostudies-literature
Project description:Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist's evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.
| S-EPMC7407326 | biostudies-literature
Project description:PURPOSE:In this study, we aimed to analyze the repeatability of quantitative multiparametric rectal magnetic resonance imaging (MRI) parameters with different measurement techniques. METHODS:All examinations were performed with 3 T MRI system. In addition to routine sequences for rectal cancer imaging protocol, small field-of-view diffusion-weighted imaging and perfusion sequences were acquired in each patient. Apparent diffusion coefficient (ADC) was used for diffusion analysis and ktrans was used for perfusion analysis. Three different methods were used in measurement of these parameters; measurements were performed twice by one radiologist for intraobserver and separately by three radiologists for interobserver variability analysis. ADC was measured by the lowest value, the value at maximum wall thickness, and freehand techniques. Ktrans was measured at the slice with maximum wall thickness, by freehand drawn region of interest (ROI), and at the dark red spot with maximum value. RESULTS:A total of 30 patients with biopsy-proven rectal adenocarcinoma were included in the study. The mean values of the parameters measured by the first radiologist on the first and second measurements were as follows: mean lowest ADC, 721.31±147.18 mm2/s and 718.96±135.71 mm2/s; mean ADC value on the slice with maximum wall thickness, 829.90±144.24 mm2/s and 829.48±149.23 mm2/s; mean ADC value measured by freehand ROI on the slice with maximum wall thickness, 846.56±136.31 mm2/s and 848.23±144.15 mm2/s; mean ktrans value on the slice with maximum wall thickness, 0.219±0.080 and 0.214±0.074; mean ktrans by freehand ROI technique (including as much tumoral tissue as possible), 0.208±0.074 and 0.207±0.069; mean ktrans measured from the dark red foci, 0.308±0.109 and 0.311±0.105. Intraobserver agreement was very good among diffusion and perfusion parameters obtained with all three measurement techniques. Interobserver agreement was very good, except for one of the measurement techniques. As far as interobserver variability is considered, only ADC value measured on the slice with maximum wall thickness differed significantly. CONCLUSION:Multiparametric MRI of rectum, using ADC as the diffusion and ktrans as the perfusion parameter is a repeatable technique. This technique may potentially be used in prediction and evaluation of neoadjuvant treatment response. New studies with larger patient groups are needed to validate the role of multiparametric MRI.
| S-EPMC7051263 | biostudies-literature
Project description:ObjectivesTo explore the feasibility of predicting overall survival (OS) of patients with midline glioma using multi-parameter magnetic resonance imaging (MRI) features.MethodsData of 84 patients with midline gliomas were retrospectively collected, including 40 patients with OS > 12 months (28 cases were adults, 14 cases were H3 K27M-mutation) and 44 patients with OS < 12 months (29 cases were adults, 31 cases were H3 K27M-mutation). Features were extracted from the largest slice of tumors, which were manually segmented on T2-weighted (T2w), T2 fluid-attenuated inversion recovery (T2 FLAIR), and contrast-enhanced T1-weighted (T1c) images. Data were randomly divided into training (70%) and test cohorts (30%) and normalized and standardized using Z-scores. Feature dimensionality reduction was performed using the variance method and maximum relevance and minimum redundancy (mRMR) algorithm. We used the logistic regression algorithm to construct three models for T2w, T2 FLAIR, and T1c images as well as one combined model. The test cohort was used to evaluate the models, and receiver operating characteristic (ROC) curves, areas under the curve (AUCs), sensitivity, specificity, and accuracy were calculated. The nomogram of the combined model was built and evaluated using a calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical application value of the four models.ResultsA total of 1,316 features were extracted from T2w, T2 FLAIR, and T1c images, respectively. And then the best non-redundant features were selected from the extracted features using the variance method and mRMR. Finally, five features were extracted each from T2w, T2 FLAIR, and T1c images, and 12 features were extracted for the combined model. Four models were established using the optimal features. In the test cohort, the combined model performed the best out of all models. The AUCs of the T2w, T2 FLAIR, T1c, and combined models were 0.73, 0.78, 0.74, and 0.87, respectively, and accuracies were 0.72, 0.76, 0.72, and 0.84, respectively. The ROC curves and DCA showed that the combined model had the highest efficiency and most favorable clinical benefits.ConclusionThe combined radiomics model based on multi-parameter MRI features provided a reliable non-invasive method for the prognostic prediction of midline gliomas.
| S-EPMC9108285 | biostudies-literature