A Radiomic MRI based Nomogram for Prediction of Heart Failure with Preserved Ejection Fraction in Systemic Lupus Erythematosus Patients: Insights From a Three-Center Prospective Study

Artificial intelligence in cardiovascular medicine: clinical applications

Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

Radiomics features of the cardiac blood pool to indicate hemodynamic changes in pulmonary hypertension (PH) due to heart failure with preserved ejection fraction (PH-HFpEF)

To test the hypothesis that cine MRI-derived radiomics features of the cardiac blood pool can represent hemodynamic characteristics of pulmonary hypertension-heart failure with preserved ejection fraction (PH-HFpEF). Nineteen PH-HFpEF patients (9 male, 57.8 ± 14.7 years) and 19 healthy controls (13 male, 50.3 ± 13.6 years) were enrolled. All participants underwent a cardiac MRI scan. One hundred and seven radiomics features (7 classes) of the blood pool in the left and right ventricles/atrium (LV/RV/LA/RA) were extracted from 4-chamber cine (2D images) at the stages of systole, rapid filling, diastasis, and atrial contraction within a cardiac cycle. For PH-HFpEF patients, features acquired from LV/LA were related to the pulmonary capillary wedge pressure (PCWP); features acquired from RV/RA were related to the mean pulmonary artery pressure (mPAP) using the Pearson correlation coefficient (r). Logistic regression, receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to test the capability of radiomics features in discriminating 2 subject groups. Features acquired from different chambers at various periods present diverse properties in representing hemodynamic indices of PH-HFpEF. Multiple radiomics features blood pool were significantly related to PCWP and/or mPAP (r: 0.4-0.679, p < 0.05). In addition, multiple features of blood pools acquired at various time points within a cardiac cycle can efficiently discriminate PH-HFpEF from controls (individual AUC: 0.7-0.864). Cine MRI-derived radiomics features of the cardiac blood pool have the potential to characterize hemodynamic abnormalities in the context of PH-HFpEF.

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging

Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.

Non-invasive prediction of the chronic degree of lupus nephropathy based on ultrasound radiomics

OBJECTIVE

Through machine learning (ML) analysis of the radiomics features of ultrasound extracted from patients with lupus nephritis (LN), this attempt was made to non-invasively predict the chronicity index (CI)of LN.

METHODS

A retrospective collection of 136 patients with LN who had renal biopsy was retrospectively collected, and the patients were randomly divided into training set and validation set according to 7:3. Radiomics features are extracted from ultrasound images, independent factors are obtained by using LASSO dimensionality reduction, and then seven ML models were used to establish predictive models. At the same time, a clinical model and an US model were established. The diagnostic efficacy of the model is evaluated by analysis of the receiver operating characteristics (ROC) curve, accuracy, specificity, and sensitivity. The performance of the seven machine learning models was compared with each other and with clinical and US models.

RESULTS

A total of 1314 radiomics features are extracted from ultrasound images, and 5 features are finally screened out by LASSO for model construction, and the average ROC of the seven ML is 0.683, among which the Xgboost model performed the best, and the AUC in the test set is 0.826 (95% CI: 0.681-0.936). For the same test set, the AUC of clinical model constructed based on eGFR is 0.560 (95% CI: 0.357-0.761), and the AUC of US model constructed based on Ultrasound parameters is 0.679 (95% CI: 0.489-0.853). The Xgboost model is significantly more efficient than the clinical and US models.

CONCLUSION

ML model based on ultrasound radiomics features can accurately predict the chronic degree of LN, which can provide a valuable reference for clinicians in the treatment strategy of LN patients.