Dataset Information

A non-invasive, automated diagnosis of Meniere's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study.

ABSTRACT:

Purpose

This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière's disease.

Materials and methods

A retrospective, multicentric diagnostic case-control study was performed. This study included 120 patients with unilateral or bilateral Menière's disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans and used to train a machine learning-based, multi-layer perceptron classification model to distinguish patients with Menière's disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model.

Results

The classification accuracy of the machine learning model on the test set was 82%, with a sensitivity of 83%, and a specificity of 82%. The positive and negative predictive values were 71%, and 90%, respectively.

Conclusion

The multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Menière's disease based on radiomic features extracted from conventional T2-weighted MRI scans. In the future, radiomics might serve as a fast and noninvasive decision support system, next to clinical evaluation in the diagnosis of Menière's disease.

SUBMITTER: van der Lubbe MFJA

PROVIDER: S-EPMC8795017 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study.

van der Lubbe Marly F J A MFJA Vaidyanathan Akshayaa A de Wit Marjolein M van den Burg Elske L EL Postma Alida A AA Bruintjes Tjasse D TD Bilderbeek-Beckers Monique A L MAL Dammeijer Patrick F M PFM Bossche Stephanie Vanden SV Van Rompaey Vincent V Lambin Philippe P van Hoof Marc M van de Berg Raymond R

La Radiologia medica 20211125 1

<h4>Purpose</h4>This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière's disease.<h4>Materials and methods</h4>A retrospective, multicentric diagnostic case-control study was performed. This study included 120 patients with unilateral or bilateral Menière's disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans an ...[more]

PMID: 34822101

Similar Datasets

Project description:ObjectiveTo investigate the feasibility of radiomics in predicting molecular subtype of breast invasive ductal carcinoma (IDC) based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).MethodsA total of 303 cases with pathologically confirmed IDC from January 2018 to March 2021 were enrolled in this study, including 223 cases from Fudan University Shanghai Cancer Center (training/test set) and 80 cases from Shaoxing Central Hospital (validation set). All the cases were classified as HR+/Luminal, HER2-enriched, and TNBC according to immunohistochemistry. DCE-MRI original images were treated by semi-automated segmentation to initially extract original and wavelet-transformed radiomic features. The extended logistic regression with least absolute shrinkage and selection operator (LASSO) penalty was applied to identify the optimal radiomic features, which were then used to establish predictive models combined with significant clinical risk factors. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis were adopted to evaluate the effectiveness and clinical benefit of the models established.ResultsOf the 223 cases from Fudan University Shanghai Cancer Center, HR+/Luminal cancers were diagnosed in 116 cases (52.02%), HER2-enriched in 71 cases (31.84%), and TNBC in 36 cases (16.14%). Based on the training set, 788 radiomic features were extracted in total and 8 optimal features were further identified, including 2 first-order features, 1 gray-level run length matrix (GLRLM), 4 gray-level co-occurrence matrices (GLCM), and 1 3D shape feature. Three multi-class classification models were constructed by extended logistic regression: clinical model (age, menopause, tumor location, Ki-67, histological grade, and lymph node metastasis), radiomic model, and combined model. The macro-average areas under the ROC curve (macro-AUC) for the three models were 0.71, 0.81, and 0.84 in the training set, 0.73, 0.81, and 0.84 in the test set, and 0.76, 0.82, and 0.83 in the validation set, respectively.ConclusionThe DCE-MRI-based radiomic features are significant biomarkers for distinguishing molecular subtypes of breast cancer noninvasively. Notably, the classification performance could be improved with the fusion analysis of multi-modal features.

Project description:BackgroundAlthough there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC.MethodsIn total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared.ResultsThe machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003).ConclusionsThe machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

Project description:Background: Pathologically, Meniere's disease symptoms are considered to be associated with endolymphatic hydrops. Examinations revealing endolymphatic hydrops can be useful for accurate Meniere's disease diagnosis. We previously reported a quantitative method for evaluating endolymphatic hydrops, i.e., by measuring the volume of the endolymphatic space using three-dimensional magnetic resonance imaging (MRI) of the inner ear. This study aimed to confirm the usefulness of our methods for diagnosing Meniere's disease. Here, we extracted new explanatory factors for diagnosing Meniere's disease by comparing the volume of the endolymphatic space between healthy volunteers and patients with Meniere's disease. Additionally, we validated our method by comparing its diagnostic accuracy with that of the conventional method. Methods and Findings: This is a prospective diagnostic accuracy study performed at vertigo/dizziness centre of our university hospital, a tertiary hospital. Eighty-six patients with definite unilateral Meniere's disease and 47 healthy volunteers (25 and 33 males, and 22 and 53 females in the control and patient groups, respectively) were enrolled. All participants underwent 3-Tesla MRI 4 h after intravenous injection of gadolinium to reveal the endolymphatic space. The volume of the endolymphatic space was measured and a model for Meniere's disease diagnosis was constructed and compared with models using conventional criteria to confirm the effectiveness of the methods used. The area under the receiver operating characteristic curve of the method proposed in this study was excellent (0.924), and significantly higher than that derived using the conventional criteria (0.877). The four indices, sensitivity, specificity, positive predictive value, and negative predictive value, were given at the threshold; all of these indices achieved higher scores for the 3D model compared to the 2D model. Cross-validation of the models revealed that the improvement was due to the incorporation of the semi-circular canals. Conclusions: Our method showed high diagnostic accuracy for Meniere's disease. Additionally, we revealed the importance of observing the semi-circular canals for Meniere's disease diagnosis. The proposed method can contribute toward providing effective symptomatic relief in Meniere's disease.

Project description:Background: Greater red and processed meat consumption has been linked to adverse cardiovascular outcomes. However, the impact of these exposures on cardiovascular magnetic resonance (CMR) phenotypes has not been adequately studied. Objective: We describe novel associations of meat intake with cardiovascular phenotypes and investigate underlying mechanisms through consideration of a range of covariates. Design: We studied 19,408 UK Biobank participants with CMR data available. Average daily red and processed meat consumption was determined through food frequency questionnaires and expressed as a continuous variable. Oily fish was studied as a comparator, previously associated with favourable cardiac outcomes. We considered associations with conventional CMR indices (ventricular volumes, ejection fraction, stroke volume, left ventricular mass), novel CMR radiomics features (shape, first-order, texture), and arterial compliance measures (arterial stiffness index, aortic distensibility). We used multivariable linear regression to investigate relationships between meat intake and cardiovascular phenotypes, adjusting for confounders (age, sex, deprivation, educational level, smoking, alcohol intake, exercise) and potential covariates on the causal pathway (hypertension, hypercholesterolaemia, diabetes, body mass index). Results: Greater red and processed meat consumption was associated with an unhealthy pattern of biventricular remodelling, worse cardiac function, and poorer arterial compliance. In contrast, greater oily fish consumption was associated with a healthier cardiovascular phenotype and better arterial compliance. There was partial attenuation of associations between red meat and conventional CMR indices with addition of covariates potentially on the causal pathway, indicating a possible mechanistic role for these cardiometabolic morbidities. However, other associations were not altered with inclusion of these covariates, suggesting importance of alternative biological mechanisms underlying these relationships. Radiomics analysis provided deeper phenotyping, demonstrating association of the different dietary habits with distinct ventricular geometry and left ventricular myocardial texture patterns. Conclusions: Greater red and processed meat consumption is associated with impaired cardiovascular health, both in terms of markers of arterial disease and of cardiac structure and function. Cardiometabolic morbidities appeared to have a mechanistic role in the associations of red meat with ventricular phenotypes, but less so for other associations suggesting importance of alternative mechanism for these relationships.