Multi-parametric MRI-based radiomics for the diagnosis of malignant soft-tissue tumor.

PURPOSE: To develop and validate a multiparametric magnetic resonance imaging-based radiomics nomogram for differentiating malignant and benign soft-tissue tumors. METHODS: A total of 91 patients with pathologically confirmed soft-tissue tumors were enrolled between January 2017 and October 2020. Forty-eight patients were consecutively enrolled between November 2020 and March 2022, as a time-independent cohort. All patients underwent contrast-enhanced T1-weighted and T2-weighted fat-suppression magnetic resonance scans at 3.0 T. Radiomics features were extracted and selected from the two modalities to develop the radiomics signature. Significant clinical/morphological characteristics were identified using a multivariate logistic regression analysis. The least absolute shrinkage and selection operator regression were applied to identify discriminative features. A clinical-radiomics nomogram was constructed based on clinical/morphological characteristics and radiomics features. Finally, the performance of the nomogram was validated using the receiver operating characteristic and decision curve analysis (DCA). RESULTS: Six features were selected to establish the combined RS. Size, margin, and peritumoral edema were identified as the most important clinical and morphological factors, respectively. The radiomics signature outperformed the clinical model in terms of AUC and sensitivity. The nomogram integrating the combined RS, size, margin, and peritumoral edema achieved favorable predictive efficacy, generating AUCs of 0.954 (95% confidence interval [CI]: 0.907-1.000, Sen = 0.861, Spe = 0.917), 0.962 (95% CI: 0.901-1.000, Sen = 0.944, Spe = 0.923), and 0.935 (95% CI: 0.871-0.998, Sen = 0.815, Spe = 0.952) in the training (n = 60), validation (n = 31) and time-independent (n = 48) cohorts, respectively. The DCA curve indicated good clinical usefulness of the nomogram. CONCLUSIONS: Our study demonstrated the clinical potential of multiparametric MRI-based radiomics in distinguishing malignant from benign soft-tissue tumors, which can be considered as a noninvasive tool for individual treatment management.

Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans.

BACKGROUND: Pulmonary tuberculosis (TB) and lung cancer (LC) are common diseases with a high incidence and similar symptoms, which may be misdiagnosed by radiologists, thus delaying the best treatment opportunity for patients. AIM: To develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography (CT) images. METHODS: We enrolled 478 patients (January 2012 to October 2018), who underwent preoperative CT screening. Radiomics features were extracted and selected from the CT data to establish a logistic regression model. A radiomics nomogram model was constructed, with the receiver operating characteristic, decision and calibration curves plotted to evaluate the discriminative performance. RESULTS: Radiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance. The radiomics nomogram model exhibited good discrimination, with an area under the curve of 0.914 (sensitivity = 0.890, specificity = 0.796) in the training cohort, and 0.900 (sensitivity = 0.788, specificity = 0.907) in the validation cohort. The decision curve analysis revealed that the constructed nomogram had clinical usefulness. CONCLUSION: These proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.

Associations of psychological distress with positive psychological variables and activities of daily living among stroke patients: a cross-sectional study.

BACKGROUND: Depression and anxiety result in psychological distress, which can further affect mental status and quality of life in stroke patients. Exploring the associations between positive psychological variables and symptoms of psychological distress following stroke is of great significance for further psychological interventions. METHODS: A total of 710 stroke patients from the five largest cities in Liaoning Province in China were enrolled into the present study in July 2014. All patients independently completed the questionnaires with respect to psychological distress and positive psychological variables. Depressive and anxiety symptoms were evaluated using Center for Epidemiologic Studies Depression Scale (CES-D) and Self-Rating Anxiety Scale, respectively. Positive psychological variables were evaluated using Perceived Social Support Scale, Adult Hope Scale (AHS), General Perceived Self-Efficacy Scale and Resilience Scale-14 (RS-14). Activities of Daily Living (ADL) was measured using Barthel Index. Factors associated with psychological variables and depressive and anxiety symptoms were identified using t-test, ANOVA, correlation and hierarchical linear regression analysis. RESULTS: Depressive and anxiety symptoms were present in 600 of 710 (84.51%) and 537 of 710 (75.63%) stroke patients enrolled, respectively. Social support (ß = - 0.111, p < 0.001) and hope (ß = - 0.120, p < 0.001) were negatively associated with both depressive and anxiety symptoms. Resilience (ß = - 0.179, p < 0.001) was negatively associated with depressive symptoms. Self-efficacy (ß = - 0.135, p < 0.001) was negatively associated with anxiety symptoms. Hierarchical regression analyses indicated that ADL accounted for 10.0 and 6.0% of the variance of depressive and anxiety symptoms, respectively. Social support, resilience, self-efficacy and hope as a whole accounted for 7.5 and 5.3% of the variance of depressive and anxiety symptoms. CONCLUSIONS: The high frequency of depressive and anxiety symptoms among Chinese stroke survivors should receive attentions from all stakeholders. Findings suggested that intervention strategies on ADL, social support, hope, resilience and self-efficacy could be developed to improve psychosocial outcomes for stroke survivors.