A U-shaped contraction pattern was shown to be associated with a better Cardiac resynchronization therapy (CRT) response. The main goal of this study is to automatically recognize left ventricular contractile patterns using machine learning algorithms trained on conventional quantitative features (ConQuaFea) and radiomic features extracted from Gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). Among 98 patients with standard resting GSPECT MPI included in this study, 29 received CRT therapy and 69 did not (also had CRT inclusion criteria but did not receive treatment yet at the time of data collection, or refused treatment). A total of 69 non-CRT patients were employed for training, and the 29 were employed for testing. The models were built utilizing features from three distinct feature sets (ConQuaFea, radiomics, and ConQuaFea + radiomics (combined)), which were chosen using Recursive feature elimination (RFE) feature selection (FS), and then trained using seven different machine learning (ML) classifiers. In addition, CRT outcome prediction was assessed by different treatment inclusion criteria as the study’s final phase. The MLP classifier had the highest performance among ConQuaFea models (AUC, SEN, SPE = 0.80, 0.85, 0.76). RF achieved the best performance in terms of AUC, SEN, and SPE with values of 0.65, 0.62, and 0.68, respectively, among radiomic models. GB and RF approaches achieved the best AUC, SEN, and SPE values of 0.78, 0.92, and 0.63 and 0.74, 0.93, and 0.56, respectively, among the combined models. A promising outcome was obtained when using radiomic and ConQuaFea from GSPECT MPI to detect left ventricular contractile patterns by machine learning.

No abstract

Background: Developing asymmetries are uncommon mammographic findings with a chance of being associated with malignancy. Objectives: The current study aimed at correlating ultrasound, magnetic resonance imaging (MRI) findings, and histopathology of patients with developing focal asymmetry in opportunist screening mammograms setting, and presents a diagnostic approach to developing asymmetry. Methods: This was a cross-sectional study on a database of opportunist screening mammography at the Breast Clinic, Cancer Center, at Tehran University of Medical Sciences from January 2017 to December 2018. Mammogram screenings (n = 12,169) were evaluated for developing asymmetry. Findings of mammography, ultrasound, MRI findings, and histopathology of patients with developing asymmetry were collected and analyzed. Results: Fifty-four cases (0.44%) had developed asymmetry in screening mammograms. After excluding 18 patients with considering exclusion criteria, the data of 36 patients were analyzed. The summation artifact was the etiology of developing asymmetry in 11 (30.5%) patients. Ultrasound was performed in 28 patients, and 14 (38.8%) patients had no correlated findings. All 3 malignant cases had ultrasound correlates, and a significant association existed between sonography and the risk of malignancy in patients having developing asymmetry (P = 0.003). Three malignant cases of the study underwent MRI, 1 with segmental clumped non-mass enhancement, and 2 showed a mass with rim enhancement. A significant association was revealed between a family history of breast cancer (P = 0.04) and developing asymmetry. The positive predictive value of developing asymmetry for malignancy was 8.3%. Conclusions: Patients having developing asymmetry should be evaluated for malignancy, using supplementary techniques, such as additional mammographic views, ultrasound primarily, or MRI. A biopsy is required for indeterminate findings.