Dataset Information

Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging.

ABSTRACT:

Background

Cardiac magnetic resonance (MR) images are often collected with different imaging parameters, which may impact the calculated values of myocardial radiomic features.

Purpose

To investigate the sensitivity of myocardial radiomic features to changes in imaging parameters in cardiac MR images.

Study type

Prospective.

Population

A total of 11 healthy participants/five patients.

Field strength/ sequence

A 3 T/cine balanced steady-state free-precession, T₁ -weighted spoiled gradient-echo, T₂ -weighted turbo spin-echo, and quantitative T₁ and T₂ mapping. For each sequence, the flip angle, in-plane resolution, slice thickness, and parallel imaging technique were varied to study the sensitivity of radiomic features to alterations in imaging parameters.

Assessment

Myocardial contours were manually delineated by experienced readers, and a total of 1023 radiomic features were extracted using PyRadiomics with 11 image filters and six feature families.

Statistical tests

Sensitivity was defined as the standardized mean difference (D effect size), and the robust features were defined at sensitivity < 0.2. Sensitivity analysis was performed on predefined sets of reproducible features. The analysis was performed using the entire cohort of 16 subejcts.

Results

64% of radiomic features were robust (sensitivity < 0.2) to changes in any imaging parameter. In qualitative sequences, radiomic features were most sensitive to changes in in-plane spatial resolution (spatial resolution: 0.6 vs. flip angle: 0.19, parallel imaging: 0.18, slice thickness: 0.07; P < 0.01 for all); in quantitative sequences, radiomic features were least sensitive to changes in spatial resolution (spatial resolution: 0.07 vs. slice thickness: 0.16, flip angle: 0.24; P < 0.01 for all). In an individual feature level, no singular feature family/image filter was identified as robust (sensitivity < 0.2) across sequences; however, highly sensitive features were predominantly associated with high-frequency wavelet filters across all sequences (32/50 features).

Data conclusion

In cardiac MR, a considerable number of radiomic features are sensitive to changes in sequence parameters.

Evidence level

1 TECHNICAL EFFICACY: Stage 1.

SUBMITTER: Jang J

PROVIDER: S-EPMC9190024 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging.

Jang Jihye J El-Rewaidy Hossam H Ngo Long H LH Mancio Jennifer J Csecs Ibolya I Rodriguez Jennifer J Pierce Patrick P Goddu Beth B Neisius Ulf U Manning Warren W Nezafat Reza R

Journal of magnetic resonance imaging : JMRI 20210301 3

<h4>Background</h4>Cardiac magnetic resonance (MR) images are often collected with different imaging parameters, which may impact the calculated values of myocardial radiomic features.<h4>Purpose</h4>To investigate the sensitivity of myocardial radiomic features to changes in imaging parameters in cardiac MR images.<h4>Study type</h4>Prospective.<h4>Population</h4>A total of 11 healthy participants/five patients.<h4>Field strength/ sequence</h4>A 3 T/cine balanced steady-state free-precession, T ...[more]

PMID: 33650227

Similar Datasets

Project description:PurposeTo investigate reproducibility of myocardial radiomic features with cardiac MRI.Materials and methodsTest-retest studies were performed with a 3-T MRI system using commonly used cardiac MRI sequences of cine balanced steady-state free precession (cine bSSFP), T1-weighted and T2-weighted imaging, and quantitative T1 and T2 mapping in phantom experiments and 10 healthy participants (mean ± standard deviation age, 29 years ± 13). In addition, this study assessed repeatability in 51 patients (56 years ± 14) who underwent imaging twice during the same session. Three readers independently delineated the myocardium to investigate inter- and intraobserver reproducibility of radiomic features. A total of 1023 radiomic features were extracted by using PyRadiomics (https://pyradiomics.readthedocs.io/) with 11 image filters and six feature families. The intraclass correlation coefficient (ICC) was estimated to assess reproducibility and repeatability, and features with ICCs greater than or equal to 0.8 were considered reproducible.ResultsDifferent reproducibility patterns were observed among sequences in in vivo test-retest studies. In cine bSSFP, the gray-level run-length matrix was the most reproducible feature family, and the wavelet low-pass filter applied horizontally and vertically was the most reproducible image filter. In T1 and T2 maps, intensity-based statistics (first-order) and gray-level co-occurrence matrix features were the most reproducible feature families, without a dominant reproducible image filter. Across all sequences, gray-level nonuniformity was the most frequently identified reproducible feature name. In inter- and intraobserver reproducibility studies, respectively, only 32%-47% and 61%-73% of features were identified as reproducible.ConclusionOnly a small subset of myocardial radiomic features was reproducible, and these reproducible radiomic features varied among different sequences. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Leiner in this issue.

Project description:PurposeTo evaluate T2 and T2* changes in acute reperfused hemorrhagic and nonhemorrhagic myocardial infarctions and to determine which technique is more suitable in the detection of intramyocardial hemorrhage at 1.5 T.Materials and methodsPatient studies were approved by the institutional review board and were HIPAA compliant. Patients (n = 14, three women) with first ST-elevation myocardial infarction underwent cardiac magnetic resonance (MR) imaging 3 days after angioplasty. T2* maps, T2 short inversion time inversion-recovery (STIR) images, and late gadolinium enhancement (LGE) images were acquired. Animal studies were approved by the institutional animal care and use committee. Canines (n = 20) were subjected to ischemia-reperfusion injury, and cardiac MR imaging was performed 5 days after reperfusion. T2* and T2 maps and T2 STIR and LGE images were acquired. Repeated-measures analysis of variance or the Friedman test was used to compare T2 and T2* changes in patients with hemorrhagic infarctions and those with nonhemorrhagic infarctions.ResultsRelative to remote myocardium, mean T2* of hemorrhagic infarctions was 54% ± 13 (standard deviation) lower in patients (15.9 msec ± 4.5 vs 35.2 msec ± 2.1, P < .001) and 40% ± 10 lower in canines (23.0 msec ± 4.0 vs 39.3 msec ± 2.5, P < .001). Mean T2* of nonhemorrhagic infarctions was marginally elevated by 6% ± 2.5 (37.8 msec ± 2.5, P = .021) in patients and by 8% ± 5 (44.6 msec ± 4.8, P = .012) in canines. In contrast, mean T2 STIR signal intensity (SI) of both hemorrhagic infarctions and nonhemorrhagic infarctions was higher than that in remote myocardium both in patients (hemorrhagic: 37% ± 19, P < .001; nonhemorrhagic: 78% ± 27, P < .001) and in canines (hemorrhagic: 42% ± 22, P < .001; nonhemorrhagic: 65% ± 22, P < .001). Consistent with STIR SI findings, mean T2 of both hemorrhagic (62.0 msec ± 4.9) and nonhemorrhagic (71.7 msec ± 7.3) infarctions in canines was elevated relative to mean T2 of remote myocardium (52.1 msec ± 4.8) by 18% ± 9 and 38% ± 13, respectively (P < .001 for both).ConclusionT2* cardiac MR imaging is more suitable than T2 cardiac MR imaging in the detection and characterization of acute reperfusion myocardial hemorrhage.Supplemental materialhttp://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13122397/-/DC1.

Project description:BACKGROUND AND PURPOSE:Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma. MATERIALS AND METHODS:In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance. RESULTS:Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma. CONCLUSIONS:This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.

Project description:Boron-10-containing positron emission tomography (PET) radio-tracer, 18F-FBPA, has been used to evaluate the feasibility and treatment outcomes of Boron neutron capture therapy (BNCT). The clinical use of PET/MR is increasing and reveals its benefit in certain applications. However, the PET/CT is still the most widely used modality for daily PET practice due to its high quantitative accuracy and relatively low cost. Considering the different attenuation correction maps between PET/CT and PET/MR, comparison of derived image features from these two modalities is critical to identify quantitative imaging biomarkers for diagnosis and prognosis. This study aimed to investigate the comparability of image features extracted from 18F-FBPA PET/CT and PET/MR. A total of 15 patients with malignant brain tumor who underwent 18F-FBPA examinations using both PET/CT and PET/MR on the same day were retrospectively analyzed. Overall, four conventional imaging characteristics and 449 radiomic features were calculated from PET/CT and PET/MR, respectively. A linear regression model and intraclass correlation coefficient (ICC) were estimated to evaluate the comparability of derived features between two modalities. Features were classified into strong, moderate, and weak comparability based on coefficient of determination (r2) and ICC. All of the conventional features, 81.2% of histogram, 37.5% of geometry, 51.5% of texture, and 25% of wavelet-based features, showed strong comparability between PET/CT and PET/MR. With regard to the wavelet filtering, radiomic features without filtering (61.2%) or with low-pass filtering (59.2%) along three axes produced strong comparability between the two modalities. However, only 8.2% of the features with high-pass filtering showed strong comparability. The linear regression models were provided for the features with strong and moderate consensus to interchange the quantitative features between the PET/CT and the PET/MR. All of the conventional and 71% of the radiomic (mostly histogram and texture) features were sufficiently stable and could be interchanged between 18F-FBPA PET with different hybrid modalities using the proposed equations. Our findings suggested that the image features high interchangeability may facilitate future studies in comparing PET/CT and PET/MR.

Dataset Information

Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging.

Background

Purpose

Study type

Population

Field strength/ sequence

Assessment

Statistical tests

Results

Data conclusion

Evidence level

Publications

Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets