Background and purpose: Recurrence is the main risk for high-grade serous ovarian cancer (HGSOC) and few prognostic biomarkers were reported. In this study, we proposed a novel deep learning (DL) method to extract prognostic biomarkers from preoperative computed tomography (CT) images, aiming at providing a non-invasive recurrence prediction model in HGSOC. Materials and methods: We enrolled 245 patients with HGSOC from two hospitals, which included a feature-learning cohort (n = 102), a primary cohort (n = 49) and two independent validation cohorts from two hospitals (n = 49 and n = 45). We trained a novel DL network in 8917 CT images from the featurelearning cohort to extract the prognostic biomarkers (DL feature) of HGSOC. Afterward, a DL-CPH model incorporating the DL feature and Cox proportional hazard (Cox-PH) regression was developed to predict the individual recurrence risk and 3-year recurrence probability of patients. Results: In the two validation cohorts, the concordance-index of the DL-CPH model was 0.713 and 0.694. Kaplan-Meier's analysis clearly identified two patient groups with high and low recurrence risk (p = 0.0038 and 0.0164). The 3-year recurrence prediction was also effective (AUC = 0.772 and 0.825), which was validated by the good calibration and decision curve analysis. Moreover, the DL feature demonstrated stronger prognostic value than clinical characteristics. Conclusions: The DL method extracts effective CT-based prognostic biomarkers for HGSOC, and provides a non-invasive and preoperative model for individualized recurrence prediction in HGSOC. In addition, the DL-CPH model provides a new prognostic analysis method that can utilize CT data without follow-up for prognostic biomarker extraction.
Chemical exchange saturation transfer (CEST) MRI is a versatile imaging method that probes the chemical exchange between bulk water and exchangeable protons. CEST imaging indirectly detects dilute labile protons via bulk water signal changes following selective saturation of exchangeable protons, which offers substantial sensitivity enhancement and has sparked numerous biomedical applications. Over the past decade, CEST imaging techniques have rapidly evolved due to contributions from multiple domains, including the development of CEST mathematical models, innovative contrast agent designs, sensitive data acquisition schemes, efficient field inhomogeneity correction algorithms, and quantitative CEST (qCEST) analysis. The CEST system that underlies the apparent CEST-weighted effect, however, is complex. The experimentally measurable CEST effect depends not only on parameters such as CEST agent concentration, pH and temperature, but also on relaxation rate, magnetic field strength and more importantly, experimental parameters including repetition time, RF irradiation amplitude and scheme, and image readout. Thorough understanding of the underlying CEST system using qCEST analysis may augment the diagnostic capability of conventional imaging. In this review, we provide a concise explanation of CEST acquisition methods and processing algorithms, including their advantages and limitations, for optimization and quantification of CEST MRI experiments.
Background Increasing studies demonstrated that the cardiac involvements are related to Coronavirus Disease 2019 (COVID‐19). Thus, we investigated the clinical characteristics of COVID‐19 patients and further determined the risk factors for cardiac involvements in them. Methods and Results We analyzed data from 102 consecutive laboratory‐confirmed and hospitalized COVID‐19 patients (52 women; age, 19–87 years). Epidemiological and demographic characteristics, clinical features, routine laboratory tests (including cardiac injury biomarkers), echocardiography, electrocardiography, chest imaging findings, management methods, and clinical outcomes were collected. Patients were divided into acute cardiac injury (ACI), with and without cardiac marker abnormities groups according to different level of cardiac markers. In this research, cardiac involvements were found in 72 of the 102 (70.6%) patients: tachycardia (n=20), electrocardiography abnormities (n=23), echocardiography abnormities (n=59), elevated myocardial enzymes (n=55), and acute myocardial injury (n=9). Eight ACI patients were aged >60 years; seven of them had two or more underlying comorbidities (hypertension, diabetes, cardiovascular diseases, chronic obstructive pulmonary disease and chronic kidney disease). Novel coronavirus pneumonia (NCP) was much more severe in the ACI patients than in patients with non‐definite ACI (p<0.001). Multivariate analyses showed that C‐reactive protein (CRP) levels, old age, NCP severity, and underlying comorbidities were the risk factors for cardiac abnormalities in COVID‐19 patients. Conclusions Cardiac involvements are common in COVID‐19 patients. Elevated CRP levels, old age, underlying comorbidities, and NCP severity are the main risk factors for cardiac involvement in COVID‐19 patients. More attention should be given to cardiovascular protection during COVID‐19 treatment for mortality reduction.
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