Dataset Information

Machine-Learning-Based Diagnostics of Cardiac Sarcoidosis Using Multi-Chamber Wall Motion Analyses.

ABSTRACT:

Background

Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis.

Objective

Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS.

Method

Forty-five CMR-negative (CMR(-), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients and forty-four controls (CTRL, 56.5(53.0;63.0)years)) underwent CMR examination. Cardiac parameters were processed using the classifiers of logistic regression, KNN(K-nearest-neighbor), DT (decision tree), RF (random forest), SVM, GBoost, XGBoost, Voting and feature selection.

Results

In a three-cluster analysis of CTRL versus vs. CMR(+) vs. CMR(-), RF and Voting classifier yielded the highest prediction rates (81.82%). The two-cluster analysis of CTRL vs. all sarcoidosis (All Sarc.) yielded high prediction rates with the classifiers logistic regression, RF and SVM (96.97%), and low prediction rates for the analysis of CMR(+) vs. CMR(-), which were augmented using feature selection with logistic regression (89.47%).

Conclusion

Multi-chamber cardiac function and strain-based supervised machine learning provides a non-contrast approach to accurately differentiate between healthy individuals and sarcoidosis patients. Feature selection overcomes the algorithmically challenging discrimination between CMR(+) and CMR(-) patients, yielding high accuracy predictions. The study findings imply higher prevalence of cardiac involvement than previously anticipated, which may impact clinical disease management.

SUBMITTER: Eckstein J

PROVIDER: S-EPMC10377893 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Publications

Machine-Learning-Based Diagnostics of Cardiac Sarcoidosis Using Multi-Chamber Wall Motion Analyses.

Eckstein Jan J Moghadasi Negin N Körperich Hermann H Akkuzu Rehsan R Sciacca Vanessa V Sohns Christian C Sommer Philipp P Berg Julian J Paluszkiewicz Jerzy J Burchert Wolfgang W Piran Misagh M

Diagnostics (Basel, Switzerland) 20230720 14

<h4>Background</h4>Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis.<h4>Objective</h4>Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS.<h4>Method</h4>Forty-five CMR-negative (CMR(-), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients ...[more]

PMID: 37510168

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Project description:BackgroundThe presence of left ventricular (LV) wall motion abnormalities (WMA) is an independent indicator of adverse cardiovascular events in patients with cardiovascular diseases. We develop and evaluate the ability to detect cardiac wall motion abnormalities (WMA) from dynamic volume renderings (VR) of clinical 4D computed tomography (CT) angiograms using a deep learning (DL) framework.MethodsThree hundred forty-three ECG-gated cardiac 4DCT studies (age: 61 ± 15, 60.1% male) were retrospectively evaluated. Volume-rendering videos of the LV blood pool were generated from 6 different perspectives (i.e., six views corresponding to every 60-degree rotation around the LV long axis); resulting in 2058 unique videos. Ground-truth WMA classification for each video was performed by evaluating the extent of impaired regional shortening visible (measured in the original 4DCT data). DL classification of each video for the presence of WMA was performed by first extracting image features frame-by-frame using a pre-trained Inception network and then evaluating the set of features using a long short-term memory network. Data were split into 60% for 5-fold cross-validation and 40% for testing.ResultsVolume rendering videos represent ~800-fold data compression of the 4DCT volumes. Per-video DL classification performance was high for both cross-validation (accuracy = 93.1%, sensitivity = 90.0% and specificity = 95.1%, κ: 0.86) and testing (90.9, 90.2, and 91.4% respectively, κ: 0.81). Per-study performance was also high (cross-validation: 93.7, 93.5, 93.8%, κ: 0.87; testing: 93.5, 91.9, 94.7%, κ: 0.87). By re-binning per-video results into the 6 regional views of the LV we showed DL was accurate (mean accuracy = 93.1 and 90.9% for cross-validation and testing cohort, respectively) for every region. DL classification strongly agreed (accuracy = 91.0%, κ: 0.81) with expert visual assessment.ConclusionsDynamic volume rendering of the LV blood pool combined with DL classification can accurately detect regional WMA from cardiac CT.

Project description:Approximately 5% of patients with sarcoidosis have clinically manifest cardiac involvement. Clinical features of Cardiac Sarcoidosis are dependent on the location, extent, and activity of the disease. First line therapy is usually with prednisone and this is recommended based on clinician experience, expert opinion and small observational cohorts. There are no published clinical trials in cardiac sarcoidosis and multiple experts in the field have called for randomized clinical trials to answer important patient care questions. Corticosteroid are associated with multiple adverse effects including hypertension, diabetes, weight gain, osteoporosis, and increased risk of infections. In contrast Methotrexate is generally well tolerated and is increasingly used in other forms of sarcoidosis.ObjectivesThe Cardiac Sarcoidosis Multi-Center Randomized Controlled Trial (CHASM CS-RCT; NCT03593759) is a multicenter randomized controlled trial designed to evaluate the optimal initial treatment strategy for patients with active cardiac sarcoidosis. We hypothesize that (1) a low dose prednisone/methotrexate combination will have non-inferior efficacy to standard dose prednisone and that (2) the low dose prednisone/ methotrexate combination will result in significantly better quality of life than standard dose prednisone, as a result of reduced burden of side effects.Methods/designEligible study subjects will have active clinically manifest cardiac sarcoidosis presenting with one or more of the following clinical findings: advanced conduction system disease, significant sinus node dysfunction, non-sustained or sustained ventricular arrhythmia, left ventricular dysfunction or right ventricular dysfunction. Subjects will be randomized in a 1:1 ratio to prednisone 0.5 mg/kg/day for 6 months (maximum dose 30 mg daily) OR to prednisone 20 mg daily for 1 month, then 10 mg daily for 1 month, then 5 mg daily for one month then stop AND methotrexate 15-20 mg once weekly for 6 months. The primary endpoint is summed perfusion rest score on 6-month PET (blinded core-lab review). The summed perfusion rest score is measure of myocardial fibrosis/scar. The design is non-inferiority with a sample size of 97 per group.DiscussionGiven the multiorgan system potential adverse side effects of prednisone, proving noninferiority of an alternate regimen would be sufficient to make the alternative compare favorably to standard dose steroids. This is the first ever clinical trial in cardiac sarcoidosis and thus in addition to the listed goals of the trial, we will also establish a multi-center, multinational cardiac sarcoidosis clinical trials network. Such a collaborative infrastructure will enable a new era of high quality data to guide physicians when treating cardiac sarcoidosis patients.

Dataset Information

Machine-Learning-Based Diagnostics of Cardiac Sarcoidosis Using Multi-Chamber Wall Motion Analyses.

Background

Objective

Method

Results

Conclusion

Publications

Machine-Learning-Based Diagnostics of Cardiac Sarcoidosis Using Multi-Chamber Wall Motion Analyses.

Similar Datasets

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