Texture Analysis of Native T1 Images as a Novel Method for Noninvasive Assessment of Uremic Cardiomyopathy

Identifying etiologies of heart failure using non-contrast cardiac magnetic resonance imaging: cine imaging, T1 and T2 mapping, and texture analysis for T1 mapping

Objective

The aim of this retrospective study was to evaluate the usefulness of non-contrast cardiac magnetic resonance imaging, including cine imaging, T1 and T2 mapping, and texture analysis for T1 mapping, for identifying etiologies of heart failure (HF).

Methods

Forty-seven patients with HF were examined using a 1.5 T scanner. Cine imaging parameters and native T1 and T2 values at the mid-septal segment were measured. Vertical run length nonuniformity, vertical gray level nonuniformity (vGLNU), wavelet energy LL(3) and HH (4) on T1 mapping were estimated at the mid-septal segment using open-access software. Late gadolinium enhancement was investigated to help diagnose the etiologies of HF. We used Kruscal-Wallis' with a post-hoc Steel-Dwass' test, Wilcoxon signed-ranked test, Pearson's chai square test and receiver operator curve analysis (ROC) to assess the usefulness of non-contrast CMR for identifying etiologies of HF.

Results

There were significant differences in left ventricular end-diastolic volume (LVEDV) indexed to body surface area (LVEDVi), left ventricular myocardial mass/LVEDV, native T1, and vGLNU between dilated cardiomyopathy (DCM), hypertensive cardiomyopathy (HC) and tachycardia-induced cardiomyopathies (TIC). DCM had higher T1 and lower vGLNU than HC. When compared with TIC, DCM showed significantly higher LVEDV and LVEDVi. ROC analysis revealed that LVEDV and vGLNU provided high specificity for differentiating DCM from the other etiologies.

Conclusion

Native T1 mapping and its texture analysis may be valuable for differentiating between DCM and HC. Cine imaging can be useful for differentiating between DCM and TIC.

Left Ventricular Vertical Run-Length Nonuniformity MRI Adds Prognostic Value to MACE in Patients with End-Stage Renal Disease

BACKGROUND

Vertical run-length nonuniformity (VRLN) is a texture feature representing heterogeneity within native T1 images and reflects the extent of cardiac fibrosis. In uremic cardiomyopathy, interstitial fibrosis was the major histological alteration. The prognostic value of VRLN in patients with end-stage renal disease (ESRD) remains unclear.

PURPOSE

To evaluate the prognostic value of VRLN MRI in patients with ESRD.

STUDY TYPE

Prospective.

POPULATION

A total of 127 ESRD patients (30 participants in the major adverse cardiac events, MACE group).

FIELD STRENGTH/SEQUENCE

3.0 T/steady-state free precession sequence, modified Look-Locker imaging.

ASSESSMENT

MRI image qualities were assessed by three independent radiologists. VRLN values were measured in the myocardium on the mid-ventricular short-axis slice of T1 mapping. Left ventricular (LV) mass, LV end-diastolic and end-systolic volume, as well as LV global strain cardiac parameters were measured.

STATISTICAL TESTS

The primary endpoint was the incident of MACE from enrollment time to January 2023. MACE is a composite endpoint consisting of all-cause mortality, acute myocardial infarction, stroke, heart failure hospitalization, and life-threatening arrhythmia. Cox proportional-hazards regression was performed to test whether VRLN independently correlated with MACE. The intraclass correlation coefficients of VRLN were calculated to evaluate intraobserver and interobserver reproducibility. The C-index was computed to examine the prognostic value of VRLN. P-value <0.05 were considered statistically significant.

RESULTS

Participants were followed for a median of 26 months. VRLN, age, LV end-systolic volume index, and global longitudinal strain remained significantly associated with MACE in the multivariable model. Adding VRLN to a baseline model containing clinical and conventional cardiac MRI parameters significantly improved the accuracy of the predictive model (C-index of the baseline model: 0.781 vs. the model added VRLN: 0.814).

DATA CONCLUSION

VRLN is a novel marker for risk stratification toward MACE in patients with ESRD, superior to native T1 mapping and LV ejection fraction.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY STAGE: 2.

Diagnosis of cardiovascular disease in patients with chronic kidney disease

Patients with chronic kidney disease (CKD) are at high risk of cardiovascular disease (CVD) and cardiovascular death. Identifying and monitoring cardiovascular complications and hypertension is important for managing patients with CKD or kidney failure and transplant recipients. Biomarkers of myocardial ischaemia, such as troponins and electrocardiography (ECG), have limited utility for diagnosing cardiac ischaemia in patients with advanced CKD. Dobutamine stress echocardiography, myocardial perfusion scintigraphy and dipyridamole stress testing can be used to detect coronary disease in these patients. Left ventricular hypertrophy and left ventricular dysfunction can be detected and monitored using various techniques with differing complexity and cost, including ECG, echocardiography, nuclear magnetic resonance, CT and myocardial scintigraphy. Atrial fibrillation and other major arrhythmias are common in all stages of CKD, and ambulatory heart rhythm monitoring enables precise time profiling of these disorders. Screening for cerebrovascular disease is only indicated in asymptomatic patients with autosomal dominant polycystic kidney disease. Standardized blood pressure is recommended for hypertension diagnosis and treatment monitoring and can be complemented by ambulatory blood pressure monitoring. Judicious use of these diagnostic techniques may assist clinicians in detecting the whole range of cardiovascular alterations in patients with CKD and enable timely treatment of CVD in this high-risk population.

Texture analysis of native T1 images as a novel method for non-invasive assessment of heart failure with preserved ejection fraction in end-stage renal disease patients

OBJECTIVES

To explore the diagnostic potential of texture analysis applied to native T1 maps obtained from cardiac magnetic resonance (CMR) images for the assessment of heart failure with preserved ejection fraction (HFpEF) among patients with end-stage renal disease (ESRD).

METHODS

This study, conducted from June 2018 to November 2020, included 119 patients (35 on hemodialysis, 55 on peritoneal dialysis, and 29 with kidney transplants) in Renji Hospital. Native T1 maps were assessed with texture analysis, using a freely available software package, in participants who underwent cardiac MRI at 3.0 T. Four texture features, selected by dimension reduction specific to the diagnosis of HFpEF, were analyzed. Multivariate logistic regression was performed to examine the independent association between the selected features and HFpEF in ESRD patients.

RESULTS

Seventy-six of 119 patients were diagnosed with HFpEF. Demographic, laboratory, cardiac MRI, and echocardiogram characteristics were compared between HFpEF and non-HFpEF groups. The four texture features that were analyzed showed statistically significant differences between groups. In multivariate analysis, age, left atrial volume index (LAVI), and sum average 4 (SA4) turned out to be independent predictors for HFpEF in ESRD patients. Combining the texture feature, SA4, with typical predictive factors resulted in higher C-index (0.923 vs. 0.898, p = 0.045) and a sensitivity and specificity of 79.2% and 95.2%, respectively.

CONCLUSIONS

Texture analysis of T1 maps adds diagnostic value to typical clinical parameters for the assessment of heart failure with preserved ejection fraction in patients with end-stage renal disease.

KEY POINTS

• Non-invasive assessment of HFpEF can help predict prognosis in ESRD patients and help them take timely preventative measures. • Texture analysis of native T1 maps adds diagnostic value to the typical clinical parameters for the assessment of HFpEF in patients with ESRD.

Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review

OBJECTIVES

To evaluate the quality of radiomics studies using cardiac magnetic resonance imaging (CMR) according to the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, and the standards defined by the Image Biomarker Standardization Initiative (IBSI) and identify areas needing improvement.

MATERIALS AND METHODS

PubMed and Embase were searched to identify radiomics studies using CMR until March 10, 2021. Of the 259 identified articles, 32 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and IBSI standards by two cardiac radiologists.

RESULTS

The mean RQS was 14.3% of the maximum (5.16 out of 36). RQS were low for the demonstration of validation (-60.6%), calibration statistics (1.6%), potential clinical utility (3.1%), and open science (3.1%) items. No study conducted a phantom study or cost-effectiveness analysis. The adherence to TRIPOD guidelines was 55.9%. Studies were deficient in reporting title (3.1%), stating objective in abstract and introduction (6.3% and 9.4%), missing data (0%), discrimination/calibration (3.1%), and how to use the prediction model (3.1%). According to the IBSI standards, non-uniformity correction, image interpolation, grey-level discretization, and signal intensity normalization were performed in two (6.3%), four (12.5%), six (18.8%), and twelve (37.5%) studies, respectively.

CONCLUSION

The quality of radiomics studies using CMR is suboptimal. Improvements are needed in the areas of validation, calibration, clinical utility, and open science. Complete reporting of study objectives, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps are necessary.

KEY POINTS

• The quality of science in radiomics studies using CMR is currently inadequate. • RQS were low for validation, calibration, clinical utility, and open science; no study conducted a phantom study or cost-effectiveness analysis. • In stating the study objective, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps, improvements are needed.