Work-related social skills training embedded in ISE with generalization strategies can enhance vocational and nonvocational outcomes for people with schizophrenia in mainland China.

DW MR imaging is useful for differentiating BEOTs from MEOTs. Patients with BEOTs are treated differently from patients with MEOTs. Conservative fertility-sparing laparoscopic surgery can be performed in patients with BEOTs. BEOTs often affect young women of childbearing age.

BackgroundLymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early‐stage cervical cancer.PurposeTo establish a multiparametric MRI (mpMRI)‐based radiomics nomogram for preoperatively predicting LNM status.Study TypeRetrospective.PopulationAmong 233 consecutive patients, 155 patients were randomly allocated to the primary cohort and 78 patients to the validation cohort.Field StrengthRadiomic features were extracted from a 1.5T mpMRI scan (T1‐weighted imaging [T1WI], fat‐saturated T2‐weighted imaging [FS‐T2WI], contrast‐enhanced [CE], diffusion‐weighted imaging [DWI], and apparent diffusion coefficient [ADC] maps).AssessmentThe performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The area under the receiver operating characteristics curve (ROC AUC), accuracy, sensitivity, and specificity were also calculated.Statistical TestsThe least absolute shrinkage and selection operator (LASSO) method was used for dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the radiomics nomogram. An independent sample t‐test and chi‐squared test were used to compare the differences in continuous and categorical variables, respectively.ResultsThe radiomic signature allowed a good discrimination between the LNM and non‐LNM groups, with a C‐index of 0.856 (95% confidence interval [CI], 0.794–0.918) in the primary cohort and 0.883 (95% CI, 0.809–0.957) in the validation cohort. Additionally, the radiomics nomogram also had a good discriminating performance and yielded good calibration both in the primary and validation cohorts (C‐index, 0.882 [95% CI, 0.827–0.937], C‐index, 0.893 [95% CI, 0.822–0.964], respectively). Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.Data ConclusionA radiomics nomogram was developed by incorporating the radiomics signature with the MRI‐reported LN status and FIGO stage. This nomogram might be used to facilitate the individualized prediction of LNM in patients with early‐stage cervical cancer.Level of Evidence3Technical Efficacy Stage2 J. Magn. Reson. Imaging 2020;52:885–896.

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Background: High-and low-risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high-risk EC is essential for planning surgery appropriately. Purpose: To develop a radiomics nomogram for high-risk EC prediction preoperatively. Study Type: Retrospective. Population: In all, 717 histopathologically confirmed EC patients (mean age, 56 years AE 9) divided into a primary group (394 patients from Center A), validation groups 1 and 2 (146 patients from Center B and 177 patients from Centers C-E). Field Strength/Sequence: 1.5/3T scanners; T 2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences. Assessment: A radiomics nomogram was generated by combining the selected radiomics features and clinical parameters (metabolic syndrome, cancer antigen 125, age, tumor grade following curettage, and tumor size). The area under the curve (AUC) of the receiver operator characteristic was used to evaluate the predictive performance of the radiomics nomogram for high-risk EC. The surgical procedure suggested by the nomogram was compared with the actual procedure performed for the patients. Net benefit of the radiomics nomogram was evaluated by a clinical decision curve (CDC), net reclassification index (NRI), and integrated discrimination improvement (IDI). Statistical Tests: Binary least absolute shrinkage and selection operator (LASSO) logistic regression, linear regression, and multivariate binary logistic regression were used to select radiomics features and clinical parameters. Results: The AUC for prediction of high-risk EC for the radiomics nomogram in the primary group, validation groups 1 and 2 were 0.896 (95% confidence interval [CI]: 0.866-0.926), 0.877 (95% CI: 0.825-0.930), and 0.919 (95% CI: 0.879-0.960), respectively. The nomogram achieved good net benefit by CDC analysis for high-risk EC. NRIs were 1.17, 1.28, and 1.51, and IDIs were 0.41, 0.60, and 0.61 in the primary group, validation groups 1 and 2, respectively. Data Conclusion: The radiomics nomogram exhibited good performance in the individual prediction of high-risk EC, and might be used for surgical management of EC. Level of Evidence: 4 Technical Efficacy Stage: 2