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Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer. Patients were retrospectively recruited in a study at the West Virginia University Cancer Institute. The final inclusion criteria were met by a total of 134 patients identified for the study. Patients were imaged using echocardiography in the parasternal long axis prior to receiving chemotherapy. All-cause mortality and HF, developed during treatment, were the primary outcomes. 269 features were assessed, grouped into four major classes: demographics (n = 21), heart function (n = 7), antineoplastic medication (n = 17), and ultrasomics (n = 224). Data was split into an internal training (60%, n = 81) and testing (40%, n = 53) set. Ultrasomics features augmented classification of mortality (area under the curve (AUC) 0.89 vs. 0.65, P = 0.003), when compared to demographic variables. When developing a risk prediction score for each feature category, ultrasomics features were significantly associated with both mortality (P = 0.031, log-rank test) and HF (P = 0.002, log-rank test). Further, only ultrasomics features provided significant improvement over demographic variables when predicting mortality (C-Index: 0.78 vs. 0.65, P = 0.044) and HF (C-Index: 0.77 vs. 0.60, P = 0.017), respectively. With further investigation, a clinical decision support tool could be developed utilizing routinely obtained patient data alongside ultrasomics variables to augment treatment regimens.

Introduction The differential diagnosis of transient ischemic attack (TIA) versus mild acute ischemic stroke (AIS) during the initial presentation to the emergency department is often difficult, as the diagnosis of both TIA and AIS relies on the presence of focal neurologic signs. As such, roughly 50% of patients with transient or mild neurologic deficits have an uncertain diagnosis prior to neuroimaging. Biomarkers, particularly leukocyte biomarkers, may be used by clinicians to diagnose mild AIS prior to neuroimaging, and this study is the first to describe the use of leukocyte biomarkers for the differentiation of mild AIS, TIA, and stroke mimic. Methods We performed a retrospective chart review of patients discharged from a local hospital with a discharge diagnosis of either TIA or AIS. Past medical history and complete blood cell count with differential upon admission were collected for all subjects. Statistical analyses were performed to compare immune cell parameters between the two groups. For all comparisons, logistic regression analysis was used to assess the effect of confounding variables, such as age, gender, and medical history for each study variable. Results Of all the immune parameters assessed in this study, the neutrophil percentage was the only significant biomarker that significantly differed between study groups. After adjustment for confounding variables using stepwise logistic regression, mild AIS patients were 5.3 times more likely than TIA cases to have a neutrophil percentage above the normal range. Conclusion Our results suggest that clinicians may utilize neutrophil percentage as an additional piece of information that may aid in their diagnosis of mild AIS versus TIA.