Cardiac magnetic resonance radiomics: basic principles and clinical perspectives.
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ABSTRACT:
SUBMITTER: Raisi-Estabragh Z
PROVIDER: S-EPMC7082724 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
Cardiac magnetic resonance radiomics: basic principles and clinical perspectives.
Raisi-Estabragh Zahra Z Izquierdo Cristian C Campello Victor M VM Martin-Isla Carlos C Jaggi Akshay A Harvey Nicholas C NC Lekadir Karim K Petersen Steffen E SE
European heart journal. Cardiovascular Imaging 20200401 4
Radiomics is a novel image analysis technique, whereby voxel-level information is extracted from digital images and used to derive multiple numerical quantifiers of shape and tissue character. Cardiac magnetic resonance (CMR) is the reference imaging modality for assessment of cardiac structure and function. Conventional analysis of CMR scans is mostly reliant on qualitative image analysis and basic geometric quantifiers. Small proof-of-concept studies have demonstrated the feasibility and super ...[more]
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Project description:Aims: To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix. Methods and Results: The sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50). Conclusion: CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.
| S-EPMC7738466 | biostudies-literature
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.
| S-EPMC10585548 | biostudies-literature
Project description:Background and purposeMR-guided radiotherapy adds the precision of magnetic resonance imaging (MRI) to the therapeutic benefits of a linear accelerator. Prior to each therapeutic session, an MRI generates a significant volume of imaging data ripe for analysis. Radiomics stands at the forefront of medical imaging and oncology research, dedicated to mining quantitative imaging attributes to forge predictive models. However, the robustness of these models is often challenged.Materials and methodsTo assess the robustness of feature extraction, we conducted reproducibility studies using a 0.35 T MR-linac system, employing both a specialized phantom and patient-derived images, focusing on cases of pancreatic cancer. We extracted shape-based, first-order and textural features from patient-derived images and only first-order and textural features from phantom-derived images. The impact of the delay between simulation and first fraction images was also assessed with an equivalence test.ResultsFrom 107 features evaluated, 58 (54 %) were considered as non-reproducible: 18 were uniformly inconsistent across both phantom and patient images, 9 were specific to phantom-based analysis, and 31 to patient-derived data.ConclusionOur findings show that a significant proportion of radiomic features extracted from this dual dataset were unreliable. It is essential to discard these non-reproducible elements to refine and enhance radiomic model development, particularly for MR-guided radiotherapy in pancreatic cancer.
| S-EPMC11320460 | biostudies-literature
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| S-EPMC9191721 | biostudies-literature
Project description:Background: Cardiovascular magnetic resonance (CMR) radiomics analysis provides multiple quantifiers of ventricular shape and myocardial texture, which may be used for detailed cardiovascular phenotyping. Objectives: We studied variation in CMR radiomics phenotypes by age and sex in healthy UK Biobank participants. Then, we examined independent associations of classical vascular risk factors (VRFs: smoking, diabetes, hypertension, high cholesterol) with CMR radiomics features, considering potential sex and age differential relationships. Design: Image acquisition was with 1.5 Tesla scanners (MAGNETOM Aera, Siemens). Three regions of interest were segmented from short axis stack images using an automated pipeline: right ventricle, left ventricle, myocardium. We extracted 237 radiomics features from each study using Pyradiomics. In a healthy subset of participants (n = 14,902) without cardiovascular disease or VRFs, we estimated independent associations of age and sex with each radiomics feature using linear regression models adjusted for body size. We then created a sample comprising individuals with at least one VRF matched to an equal number of healthy participants (n = 27,400). We linearly modelled each radiomics feature against age, sex, body size, and all the VRFs. Bonferroni adjustment for multiple testing was applied to all p-values. To aid interpretation, we organised the results into six feature clusters. Results: Amongst the healthy subset, men had larger ventricles with dimmer and less texturally complex myocardium than women. Increasing age was associated with smaller ventricles and greater variation in myocardial intensities. Broadly, all the VRFs were associated with dimmer, less varied signal intensities, greater uniformity of local intensity levels, and greater relative presence of low signal intensity areas within the myocardium. Diabetes and high cholesterol were also associated with smaller ventricular size, this association was of greater magnitude in men than women. The pattern of alteration of radiomics features with the VRFs was broadly consistent in men and women. However, the associations between intensity based radiomics features with both diabetes and hypertension were more prominent in women than men. Conclusions: We demonstrate novel independent associations of sex, age, and major VRFs with CMR radiomics phenotypes. Further studies into the nature and clinical significance of these phenotypes are needed.
| S-EPMC8727756 | biostudies-literature
Project description:BackgroundHuman papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to HPV-negative OPSCC. This study aims to noninvasively predict HPV status of OPSCC using clinical and/or radiological variables.MethodsSeventy-seven magnetic resonance radiomic features were extracted from T1-weighted postcontrast images of the primary tumor of 153 patients. Logistic regression models were created to predict HPV status, determined with immunohistochemistry, based on clinical variables, radiomic features, and its combination. Model performance was evaluated using area under the curve (AUC).ResultsModel performance showed AUCs of 0.794, 0.764, and 0.871 for the clinical, radiomic, and combined models, respectively. Smoking, higher T-classification (T3 and T4), larger, less round, and heterogeneous tumors were associated with HPV-negative tumors.ConclusionModels based on clinical variables and/or radiomic tumor features can predict HPV status in OPSCC patients with good performance and can be considered when HPV testing is not available.
| S-EPMC7821378 | biostudies-literature
Project description:Background Cardiac magnetic resonance imaging ( CMR ) provides useful information for characterizing cardiac masses, but there are limited data on whether CMR can accurately distinguish benign from malignant lesions. We aimed to describe the distribution and imaging characteristics of cardiac masses identified by CMR and to determine the diagnostic accuracy of CMR for distinguishing benign from malignant tumors. Methods and Results We examined consecutive patients referred for CMR between May 2008 and August 2013 to identify those with a cardiac mass. In patients for whom there was histological correlation, 2 investigators blinded to all data analyzed the CMR images to categorize the mass as benign or malignant. For benign masses, readers were also asked to specify the most likely diagnosis. Benign masses were defined as benign neoplastic or non-neoplastic. Malignant masses were defined as primary cardiac or metastatic. Of 8069 patients (mean age: 58±16 years; 55% female) undergoing CMR , 145 (1.8%) had a cardiac mass. In most cases (142, 98%), there was a known cardiac mass before the CMR study. Among 145 patients with a cardiac mass, 93 (64%) had a known history of malignancy. Among 53 cases that had histological correlation, 25 (47%) were benign, 26 (49%) were metastatic, and 2 (4%) were malignant primary cardiac masses. Blinded readers correctly diagnosed 89% to 94% of the cases as benign versus malignant, with a 95% agreement rate (?=0.83). Conclusions Although C MR can be highly effective in distinguishing benign from malignant lesions, pathology remains the gold standard in accurately determining the type of mass.
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Project description:In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required. The use of multiparametric MRI (mpMRI) is rapidly gaining momentum in the management of prostate cancer because of its improved diagnostic potential and its ability to combine functional and anatomical information. Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. However, PIRADS was not designed for 3D tumor volume delineation; there is a large degree of subjectivity and PIRADS does not accurately and reproducibly elucidate inter- and intra-lesional spatial heterogeneity. "Radiomics", as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine to effectively sift through the multiple series of prostate mpMRI sequences and quantify regions of interest. The radiomic efforts can be summarized in two main areas: (I) detection/segmentation of the suspicious lesion; and (II) assessment of the aggressiveness of prostate cancer. As related to RT, the goal of the latter is in particular to identify patients at high risk for metastatic disease; and the aim of the former is to identify and segment cancerous lesions and thus provide targets for radiation boost. The article is structured as follows: first, we describe the radiomic approach; and second, we discuss the radiomic pipeline as tailored for RT of prostate cancer. In this process we summarize the current efforts and progress in integrating mpMRI radiomics into the radiotherapeutic management of prostate cancer with emphasis placed on its role in treatment target definition, treatment plan strategizing, and prognostic assessment. The described concepts, methods and tools are not currently applicable to the radiation oncology practice outside of the research setting. More data are required in the form of clinical trials to assess the robustness of radiomics-based predictive models, and to maximize the efficacy of these models.
| S-EPMC6043736 | biostudies-literature
Project description:We developed a novel interpretable biological heart age estimation model using cardiovascular magnetic resonance radiomics measures of ventricular shape and myocardial character. We included 29,996 UK Biobank participants without cardiovascular disease. Images were segmented using an automated analysis pipeline. We extracted 254 radiomics features from the left ventricle, right ventricle, and myocardium of each study. We then used Bayesian ridge regression with tenfold cross-validation to develop a heart age estimation model using the radiomics features as the model input and chronological age as the model output. We examined associations of radiomics features with heart age in men and women, observing sex-differential patterns. We subtracted actual age from model estimated heart age to calculate a "heart age delta", which we considered as a measure of heart aging. We performed a phenome-wide association study of 701 exposures with heart age delta. The strongest correlates of heart aging were measures of obesity, adverse serum lipid markers, hypertension, diabetes, heart rate, income, multimorbidity, musculoskeletal health, and respiratory health. This technique provides a new method for phenotypic assessment relating to cardiovascular aging; further studies are required to assess whether it provides incremental risk information over current approaches.
| S-EPMC9329281 | biostudies-literature
Project description:ObjectivesThis study aimed to determine whether texture analysis (TA) and machine learning-based classifications can be applied in differential diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) using non-contrast cine cardiac magnetic resonance (CMR) images.MethodsIn this institutional review board-approved study, we consecutively enrolled 167 patients with CA (n = 85), HCM (n = 82), and 84 patients with normal CMR served as controls. All cases were randomized into training [119 patients (70%)] and validation [48 patients (30%)] groups. A total of 275 texture features were extracted from cine images. Based on regression analysis with the least absolute shrinkage and selection operator (LASSO), nine machine learning models were established and their diagnostic performance determined.ResultsNineteen radiomics texture features derived from cine images were used to differentiate CA and HCM. In the validation cohort, the support vector machine (SVM), which had an accuracy of 0.85, showed the best performance (MCC = 0.637). Gray level non-uniformity (GLevNonU) was the single most effective feature. The combined model of radiomics texture features and conventional MR metrics had superior discriminatory performance (AUC = 0.89) over conventional MR metrics model (AUC = 0.79). Moreover, results showed that GLevNonU levels in HCM patients were significantly higher compared with levels in CA patients and control groups (P < 0.001). A cut-off of GLevNonU ≥ 25 was shown to differentiate between CA and HCM patients, with an area under the curve (AUC) of 0.86 (CI:0.804-0.920). Multiple comparisons tests showed that GLevNonU was significantly greater in LGE+, relative to LGE-patient groups (CA+ vs. CA- and HCM+ vs. HCM-, P = 0.01, 0.001, respectively).ConclusionMachine learning-based classifiers can accurately differentiate between CA and HCM on non-contrast cine images. The radiomics-MR combined model can be used to improve the discriminatory performance. TA may be used to assess myocardial microstructure changes that occur during different stages of cardiomyopathies.
| S-EPMC9643770 | biostudies-literature