Noninvasive assessment of coronary microvascular dysfunction using SPECT myocardial perfusion imaging and myocardial perfusion entropy quantification in a rodent model of type 2 diabetes

Usability of myocardial work parameters in demonstrating myocardial involvement in INOCA patients

BACKGROUND

Myocardial work (MW) is a novel echocardiographic modality, which has been shown to have diagnostic and prognostic values in patients with cardiovascular diseases, patients with obstructive coronary artery disease, in particular. However, only a handful of studies have examined the MW analysis in ischemia with nonobstructive coronary artery (INOCA) disease. This study, therefore, aimed to detect the early left ventricular involvement in INOCA patients diagnosed by an invasive coronary angiography performing the MW analysis.

METHODS

This study included a total of 119 patients with nonobstructive coronary artery disease diagnosed by invasive coronary angiography, who were checked for prior ischemia tests performing myocardial perfusion scintigraphy. Out of these 119 patients, 49 patients developed ischemia (i.e., ischemic group) diagnosed using cardiac single-photon emission computed tomography, whereas 70 patients did not (i.e., nonischemic group). The subjects were divided into three groups based on the global MW tertiles. The groups were compared in terms of the conventional, longitudinal strain, and MW findings by conducting echocardiographic examinations.

RESULTS

The study subjects were divided into three groups based on the global constrictive work (GCW) value. The three groups were not statistically different in terms of the mean age of the patients (53.0 ± 12 vs. 52.4 ± 13.3 vs. 52.1 ± 12.3; p = 0.96). Furthermore, the three groups were not statistically different regarding the gender, height, weight, and laboratory parameters of the patients except albumin. There was no statistically difference among the tertiles of GCW groups in the measurements of cardiac chambers, LA diameter, interventricular septum, E wave, and A wave. Also, there was no statistical difference in tissue Doppler recordings. The parameters associated with MW were examined, three groups were not statistically different in terms of the global waste work (GWW) (116 ± 92, 122 ± 73, 135 ± 62, p = 0.52, respectively). In contrast, the three groups were different regarding the Global work index (GWI) (1716 ± 300, 1999 ± 130, 2253 ± 195, p < 0.001, respectively), GCW (1888 ± 206, 2298 ± 75, 2614 ± 155, p < 0.001, respectively), and Global work efficiency parameters (92.8 ± 3.6, 94.4 ± 3.2, 95.1 ± 1.8 p = 0.004, respectively).

CONCLUSION

It was concluded that the MW parameters GCW and GWI may have been used for predicting INOCA in patients.

Single-Photon Emission Computed Tomography/Computed Tomography Image-Based Radiomics for Discriminating Vertebral Bone Metastases From Benign Bone Lesions in Patients With Tumors

Purpose: The purpose of this study was to investigate the feasibility of Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) image-based radiomics in differentiating bone metastases from benign bone lesions in patients with tumors. Methods: A total of 192 lesions from 132 patients (134 in the training group, 58 in the validation group) diagnosed with vertebral bone metastases or benign bone lesions were enrolled. All images were evaluated and diagnosed independently by two physicians with more than 20 years of diagnostic experience for qualitative classification, the images were imported into MaZda software in Bitmap (BMP) format for feature extraction. All radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation algorithms after the process of normalization and correlation analysis. Based on these selected features, two models were established: The CT model and SPECT model (radiomics features were derived from CT and SPECT images, respectively). In addition, a combination model (ComModel) combined CT and SPECT features was developed in order to better evaluate the predictive performance of radiomics models. Subsequently, the diagnostic performance between each model was separately evaluated by a confusion matrix. Results: There were 12, 13, and 18 features contained within the CT, SPECT, and ComModel, respectively. The constructed radiomics models based on SPECT/CT images to discriminate between bone metastases and benign bone lesions not only had high diagnostic efficacy in the training group (AUC of 0.894, 0.914, 0.951 for CT model, SPECT model, and ComModel, respectively), but also performed well in the validation group (AUC; 0.844, 0.871, 0.926). The AUC value of the human experts was 0.849 and 0.839 in the training and validation groups, respectively. Furthermore, both SPECT model and ComModel show higher classification performance than human experts in the training group (P = 0.021 and P = 0.001, respectively) and the validation group (P = 0.037 and P = 0.007, respectively). All models showed better diagnostic accuracy than human experts in the training group and the validation group. Conclusion: Radiomics derived from SPECT/CT images could effectively discriminate between bone metastases and benign bone lesions. This technique may be a new non-invasive way to help prevent unnecessary delays in diagnosis and a potential contribution in disease staging and treatment planning.