Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients.

Purpose: The goal of this study was to develop a fully convolutional network (FCN) tool to automatedly segment the left-ventricular (LV) myocardium in displacement encoding with stimulated echoes MRI. The segmentation results are used for LV chamber quantification and strain analyses in breast cancer patients susceptible to cancer therapy-related cardiac dysfunction (CTRCD). Approach: A DeepLabV3+ FCN with a ResNet-101 backbone was custom-designed to conduct chamber quantification on 45 female breast cancer datasets (23 training, 11 validation, and 11 test sets). LV structural parameters and LV ejection fraction (LVEF) were measured, and myocardial strains estimated with the radial point interpolation method. Myocardial classification validation was against quantization-based ground-truth with computations of accuracy, Dice score, average perpendicular distance (APD), Hausdorff-distance, and others. Additional validations were conducted with equivalence tests and Cronbach's alpha (C-α) intraclass correlation coefficients between the FCN and a vendor tool on chamber quantification and myocardial strain computations. Results: Myocardial classification results against ground-truth were Dice=0.89, APD=2.4 mm, and accuracy=97% for the validation set and Dice=0.90, APD=2.5 mm, and accuracy=97% for the test set. The confidence intervals (CI) and two one-sided t-test results of equivalence tests between the FCN and vendor-tool were CI=-1.36% to 2.42%, p-value < 0.001 for LVEF (58±5% versus 57±6%), and CI=-0.71% to 0.63%, p-value < 0.001 for longitudinal strain (-15±2% versus -15±3%). Conclusions: The validation results were found equivalent to the vendor tool-based parameter estimates, which show that accurate LV chamber quantification followed by strain analysis for CTRCD investigation can be achieved with our proposed FCN methodology.

Can global longitudinal strain (GLS) with magnetic resonance prognosticate early cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients, a prospective study?

PURPOSE: To determine if Artificial Intelligence-based computation of global longitudinal strain (GLS) from left ventricular (LV) MRI is an early prognostic factor of cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients. The main hypothesis based on the patients receiving antineoplastic chemotherapy treatment was CTRCD risk analysis with GLS that was independent of LV ejection fraction (LVEF). METHODS: Displacement Encoding with Stimulated Echoes (DENSE) MRI was acquired on 32 breast cancer patients at baseline and 3- and 6-month follow-ups after chemotherapy. Two DeepLabV3+ Fully Convolutional Networks (FCNs) were deployed to automate image segmentation for LV chamber quantification and phase-unwrapping for 3D strains, computed with the Radial Point Interpolation Method. CTRCD risk (cardiotoxicity and adverse cardiac events) was analyzed with Cox Proportional Hazards (PH) models with clinical and contractile prognostic factors. RESULTS: GLS worsened from baseline to the 3- and 6-month follow-ups (-19.1 ± 2.1%, -16.0 ± 3.1%, -16.1 ± 3.0%; P < 0.001). Univariable Cox regression showed the 3-month GLS significantly associated as an agonist (hazard ratio [HR]-per-SD: 2.1; 95% CI: 1.4-3.1; P < 0.001) and LVEF as a protector (HR-per-SD: 0.8; 95% CI: 0.7-0.9; P = 0.001) for CTRCD occurrence. Bivariable regression showed the 3-month GLS (HR-per-SD: 2.0; 95% CI: 1.2-3.4; P = 0.01) as a CTRCD prognostic factor independent of other covariates, including LVEF (HR-per-SD: 1.0; 95% CI: 0.9-1.2; P = 0.9). CONCLUSIONS: The end-point analyses proved the hypothesis that GLS is an early, independent prognosticator of incident CTRCD risk. This novel GLS-guided approach to CTRCD risk analysis could improve antineoplastic treatment with further validation in a larger clinical trial.

Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network.

This study's purpose was to develop a direct MRI-based, deep-learning semantic segmentation approach for computing global longitudinal strain (GLS), a known metric for detecting left-ventricular (LV) cardiotoxicity in breast cancer. Displacement Encoding with Stimulated Echoes cardiac image phases acquired from 30 breast cancer patients and 30 healthy females were unwrapped via a DeepLabV3 + fully convolutional network (FCN). Myocardial strains were directly computed from the unwrapped phases with the Radial Point Interpolation Method. FCN-unwrapped phases of a phantom's rotating gel were validated against quality-guided phase-unwrapping (QGPU) and robust transport of intensity equation (RTIE) phase-unwrapping. FCN performance on unwrapping human LV data was measured with F1 and Dice scores versus QGPU ground-truth. The reliability of FCN-based strains was assessed against RTIE-based strains with Cronbach's alpha (C-α) intraclass correlation coefficient. Mean squared error (MSE) of unwrapping the phantom experiment data at 0 dB signal-to-noise ratio were 1.6, 2.7 and 6.1 with FCN, QGPU and RTIE techniques. Human data classification accuracies were F1 = 0.95 (Dice = 0.96) with FCN and F1 = 0.94 (Dice = 0.95) with RTIE. GLS results from FCN and RTIE were -16 ± 3% vs. -16 ± 3% (C-α = 0.9) for patients and -20 ± 3% vs. -20 ± 3% (C-α = 0.9) for healthy subjects. The low MSE from the phantom validation demonstrates accuracy of phase-unwrapping with the FCN and comparable human subject results versus RTIE demonstrate GLS analysis accuracy. A deep-learning methodology for phase-unwrapping in medical images and GLS computation was developed and validated in a heterogeneous cohort.

A deep-learning semantic segmentation approach to fully automated MRI-based left-ventricular deformation analysis in cardiotoxicity.

Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence provide accurate estimates of cardiotoxicity damage related to breast cancer chemotherapy. This study investigated an automated LV chamber quantification tool via segmentation with a supervised deep convolutional neural network (DCNN) before strain analysis with DENSE images. Segmentation for chamber quantification analysis was conducted with a custom DeepLabV3+ DCNN with ResNet-50 backbone on 42 female breast cancer datasets (22 training-sets, eight validation-sets and 12 independent test-sets). Parameters such as LV end-diastolic diameter (LVEDD) and ejection fraction (LVEF) were quantified, and myocardial strains analyzed with the Radial Point Interpolation Method (RPIM). Myocardial classification was validated against ground-truth with sensitivity-specificity analysis, the metrics of Dice, average perpendicular distance (APD) and Hausdorff-distance. Following segmentation, validation was conducted with the Cronbach's Alpha (C-Alpha) intraclass correlation coefficient between LV chamber quantification results with DENSE and Steady State Free Precession (SSFP) acquisitions and a vendor tool-based method to segment the DENSE data, and similarly for myocardial strain analysis in the chambers. The results of myocardial classification from segmentation of the DENSE data were accuracy = 97%, Dice = 0.89 and APD = 2.4 mm in the test-set. The C-Alpha correlations from comparing chamber quantification results between the segmented DENSE and SSFP data and vendor tool-based method were 0.97 for LVEF (56 ± 7% vs 55 ± 7% vs 55 ± 6%, p = 0.6) and 0.77 for LVEDD (4.6 ± 0.4 cm vs 4.5 ± 0.3 cm vs 4.5 ± 0.3 cm, p = 0.8). The validation metrics against ground-truth and equivalent parameters obtained from the SSFP segmentation and vendor tool-based comparisons show that the DCNN approach is applicable for automated LV chamber quantification and subsequent strain analysis in cardiotoxicity.

Comprehensive enhanced methodology of an MRI-based automated left-ventricular chamber quantification algorithm and validation in chemotherapy-related cardiotoxicity.

Purpose: To comprehensively outline the methodology of a fully automated, MRI motion-guided, left-ventricular (LV) chamber quantification algorithm that enhances a similar, existing semi-automated approach. Additionally, to validate the motion-guided technique in comparison to chamber quantification with a vendor tool in post-chemotherapy breast cancer patients susceptible to cardiotoxicity. Approach: LV deformation data were acquired with the displacement encoding with stimulated echoes (DENSE) sequence on N = 21 post-chemotherapy female patients and N = 21 age-matched healthy females. The new chamber quantification algorithm consists of detecting LV boundary motion via a combination of image quantization and DENSE phase-encoded displacements. LV contractility was analyzed via chamber quantification and computations of 3D strains and torsion. For validation, estimates of chamber quantification with the motion-guided algorithm on DENSE and steady-state free precession (SSFP) acquisitions, and similar estimates with an existing vendor tool on DENSE acquisitions were compared via repeated measures analysis. Patient results were compared to healthy subjects for observing abnormalities. Results: Repeated measures analysis showed similar LV ejection fractions (LVEF), 59 % ± 6 % , 58 % ± 6 % , and 58 % ± 6 % , p = 0.2 , by applying the motion-guided algorithm on DENSE and SSFP and vendor tool on DENSE acquisitions, respectively. Differences found between patients and healthy subjects included enlarged basal diameters ( 5.0 ± 0.5 cm versus 4.4 ± 0.5 cm , p < 0.01 ), torsions ( p < 0.001 ), and longitudinal strains ( p < 0.001 ), but not LVEF ( p = 0.1 ). Conclusions: Measurement similarities between new and existing tools, and between DENSE and SSFP validated the motion-guided algorithm and differences found between subpopulations demonstrate the ability to detect contractile abnormalities.

Fully automated and comprehensive MRI-based left-ventricular contractility analysis in post-chemotherapy breast cancer patients.

OBJECTIVE: This study investigated the occurrence of cardiotoxicity-related left-ventricular (LV) contractile dysfunction in breast cancer patients following treatment with antineoplastic chemotherapy agents. METHODS: A validated and automated MRI-based LV contractility analysis tool consisting of quantization-based boundary detection, unwrapping of image phases and the meshfree Radial Point Interpolation Method was used toward measuring LV chamber quantifications (LVCQ), three-dimensional strains and torsions in patients and healthy subjects. Data were acquired with the Displacement Encoding with Stimulated Echoes (DENSE) sequence on 21 female patients and 21 age-matched healthy females. Estimates of patient LVCQs from DENSE acquisitions were validated in comparison to similar steady-state free precession measurements and their strain results validated via Bland-Altman interobserver agreements. The occurrence of LV abnormalities was investigated via significant differences in contractility measurements (LVCQs, strains and torsions) between patients and healthy subjects. RESULTS: Repeated measures analysis showed similarities between LVCQ measurements from DENSE and steady-state free precession, including cardiac output (4.7 ± 0.4 L, 4.6 ± 0.4 L, p = 0.8), and LV ejection fractions (59±6%, 58±5%, p = 0.2). Differences found between patients and healthy subjects included enlarged basal diameter (5.0 ± 0.5 cm vs 4.4 ± 0.5 cm, p < 0.01), apical torsion (6.0 ± 1.1° vs 9.7 ± 1.4°, p < 0.001) and global longitudinal strain (-0.15 ± 0.02 vs. -0.21 ± 0.04, p < 0.001), but not LV ejection fraction (59±6% vs. 63±6%, p = 0.1). CONCLUSION: The results from the statistical analysis reveal the possibility of LV abnormalities in the post-chemotherapy patients via enlarged basal diameter and reduced longitudinal strain and torsion, in comparison to healthy subjects. ADVANCES IN KNOWLEDGE: This study shows that subclinical LV abnormalities in post-chemotherapy breast cancer patients can be detected with an automated technique for the comprehensive analysis of contractile parameters.

Can post-chemotherapy cardiotoxicity be detected in long-term survivors of breast cancer via comprehensive 3D left-ventricular contractility (strain) analysis?

PURPOSE: This study applied a novel and automated contractility analysis tool to investigate possible cardiotoxicity-related left-ventricular (LV) dysfunction in breast cancer patients following treatment with anti-neoplastic chemotherapy agents (CTA). Subclinical dysfunction otherwise undetected via LV ejection fraction (LVEF) was determined. METHODS: Deformation data were acquired with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence on 16 female patients who had CTA-based treatment. The contractility analysis tool consisting of image quantization-based boundary detection and the meshfree Radial Point Interpolation Method was used to compare chamber quantifications, 3D regional strains and torsion between patients and healthy subjects (N = 26 females with N = 14 age-matched). Quantifications of patient LVEFs from DENSE and Steady-State Free Precession (SSFP) acquisitions were compared, Bland-Altman interobserver agreements measured on their strain results and differences in contractile parameters with healthy subjects determined via Student's t-tests. RESULTS: A significant difference was not found between DENSE and SSFP-based patient LVEFs at 58 ±â€¯7% vs 57 ±â€¯9%, p = 0.6. Bland-Altman agreements were - 0.01 ±â€¯0.05 for longitudinal strain and 0.1 ±â€¯1.3° for torsion. Differences in basal diameter indicating enlargement, 5.2 ±â€¯0.5 cm vs 4.5 ±â€¯0.5 cm, p < 0.01, and torsion, 4.7 ±â€¯1.0° vs 8.1 ±â€¯1.1°, p < 0.001 in the mid-ventricle and 5.9 ±â€¯1.2° vs 10.2 ±â€¯0.9°, p < 0.001 apically, were seen between patients and age-matched healthy subjects and similarly in longitudinal strain, but not in LVEF. CONCLUSIONS: Results from the statistical analysis reveal the likelihood of LV remodeling in this patient subpopulation otherwise not indicated by LVEF measurements.