Objective The ovarian cancer is a common malignant tumor of gynaecology,and it is lack of early specific differential diagnosis.This study is to investigate the application of MR-DWI and ADC values in diagnosis of ovarian tumors,and to provide reference for clinical diagnosis.Methods 124 patients with ovarian tumors were selected as observation group,and 45 normal women were selected as the control group during the same period.MRI was performed before operation,the lesions from and image signal of MR-DWI was analyzed,and the value of ADC was measured.Results The ovarian tissues in the observation group had high or equal signal,with round or circular ovarian tumor,and uneven cystic wall spaces,while ovarian tissues in the control group had no clear boundary,with multiple follicles and low signal.The value of ADC in the observation group was (1.49±0.23)×10-3mm2/s,higher than that of the control group ((0.97±0.18)×10-3mm2/s).The difference had statistical significance (P＜0.05).The values of ADC were lower in benign tumors than in the malignant ovarian tumors,and the difference had statistical significance (P＜0.05).For patients in the observation group,the values of ADC were higher for patients in level Ⅲ than for patients in level Ⅱ,and it was higher for patients in level Ⅱ than for patients in level Ⅰ,and the difference had statistical significance (P＜0.05).The values of ADC was posi tively correlated to ovarian tumor type (r=0.763,P＜0.05),and negatively correlated to pathological classification of ovarian tumor (r=-0.947,P＜0.05).Conclusion MR-DWI and ADC values can be used to diagnose ovarian tumors.The values of ADC are positively correlated to ovarian tumors' classification,ie,the higher the ADC values,the higher classification the ovarian tumors.

Background and purpose: The technique of dynamic contrast-enhanced MRI (DCE-MRI) is widely applied in differential diagnosis between benign and malignant tumor and therapeutic estimation of neoadjuvant chemotherapy in clinic. However, there is no standard quantitative measurement method. This study aimed to assess the variability of different region of interest (ROI) selections for tumor bed of breast cancer using DCE-MRI, and to ascertain the optimal ROI delineation. Methods: We retrospectively analyzed DCE-MRI of 30 patients diagnosed with breast cancer by pathology. The ROIs were delineated by 2 different observers using iCAD software with 4 methods, including whole tumor (Whole), the slice containing the most enhancing voxels (SliceMax), 3 slices centered in SliceMax (Partial) and the 5％ most enhancing contiguous voxels within SliceMax (5Max), to generate the volume transfer constant (Ktrans), the extracellular volume fraction (Ve) and rate constant (Kep). And the reproducibilities of the measurements were assessed using the Bland-Altman method. Results: In the analysis of ROIs delineation, the Ktrans, Ve and Kep reported by different observers were 1.26±0.54 vs 1.25±0.53, 0.75±0.23 vs 0.73±0.22 and 1.93±1.46 vs 1.95±1.51 (P＞0.05) using the method of Whole, and 1.28±0.43 vs 1.26±0.43, 0.74±0.21 vs 0.80±0.27, 1.95±1.53 vs 1.93±1.43 (P＞0.05) using the method of Partial, and 1.30±0.33 vs 1.32±0.33, 0.77±0.20 vs 0.73±0.24, 1.82±1.53 vs 1.87±1.45 (P＞0.05) using the method of SliceMax, and 1.31±0.35 vs 1.35±0.33, 0.77±0.20 vs 0.98±0.25, 1.97±1.36 vs 1.73±1.55 using the method of 5Max (P＜0.05). Using the methods of ROI delineation except 5Max, there was no significant difference between Ktrans, Ve and Kep reported by different observers. The bias vs limits of agreement were 0.002 vs-0.013 to 0.012,-0.003 vs-0.023 to 0.017, 0.006 vs-0.018 to 0.029,-0.035 vs-0.054 to 0.018 measured with Whole method, SliceMax, Partial and 5Max respectively using the Bland-Altman method. Conclusion: It may be reliable to measure functional parameters of primary tumors in breast cancer using DCE-MRI according to the methods of Whole, Partial and SliceMax.

OBJECTIVES: The objective of this study was to compare the performance of two popularly used early sepsis diagnostic criteria, systemic inflammatory response syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA), using statistical and machine learning approaches. METHODS: This retrospective study examined patient visits in Emergency Department (ED) with sepsis related diagnosis. The outcome was 28-day in-hospital mortality. Using odds ratio (OR) and modeling methods (decision tree [DT], multivariate logistic regression [LR], and naïve Bayes [NB]), the relationships between diagnostic criteria and mortality were examined. RESULTS: Of 132,704 eligible patient visits, 14% died within 28 days of ED admission. The association of qSOFA ≥2 with mortality (OR = 3.06; 95% confidence interval [CI], 2.96–3.17) greater than the association of SIRS ≥2 with mortality (OR = 1.22; 95% CI, 1.18–1.26). The area under the ROC curve for qSOFA (AUROC = 0.70) was significantly greater than for SIRS (AUROC = 0.63). For qSOFA, the sensitivity and specificity were DT = 0.39, LR = 0.64, NB = 0.62 and DT = 0.89, LR = 0.63, NB = 0.66, respectively. For SIRS, the sensitivity and specificity were DT = 0.46, LR = 0.62, NB = 0.62 and DT = 0.70, LR = 0.59, NB = 0.58, respectively. CONCLUSIONS: The evidences suggest that qSOFA is a better diagnostic criteria than SIRS. The low sensitivity of qSOFA can be improved by carefully selecting the threshold to translate the predicted probabilities into labels. These findings can guide healthcare providers in selecting risk-stratification measures for patients presenting to an ED with sepsis.
Artificial Intelligence ; Bays ; Diagnosis ; Emergency Service, Hospital ; Health Personnel ; Hospital Mortality ; Humans ; Logistic Models ; Machine Learning* ; Medical Informatics ; Methods* ; Mortality ; Odds Ratio ; Retrospective Studies ; ROC Curve ; Sensitivity and Specificity ; Sepsis ; Severity of Illness Index ; Systemic Inflammatory Response Syndrome ; Trees

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