Suchergebnisse

Abstract
Background The aim of this study was to develop and internally validate a risk nomogram for postoperative complications of schwannoma surgery.
Methods From 2016 to 2020, we reviewed 83 patients who underwent schwannoma resection with a total number of 85 schwannomas. A predictive model was developed based on the dataset of this group. During model construction, univariate and multivariate logistic regression analysis were used to determine the independent predictors of postoperative complications. Assessment of the discriminative function, calibrating proficiency, and clinical usefulness of the predicting model was performed using C-index, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis. Internal validation was assessed using bootstrapping validation.
Results Predictors contained in the prediction nomogram included age, tumor location, symptoms, and surgical approach. The model displayed satisfying abilities of discrimination and calibration, with a C-index of 0.901 (95% confidence [CI]: 0.837–0.965). A high C-index value of 0.853 was achieved in the interval verification. Decision curve analysis showed that the nomogram was clinically useful when intervention was decided at the complication possibility threshold of 2%.
Conclusion This new risk nomogram for postoperative complications of schwannoma surgery has taken age, tumor location, symptoms, and surgical approach into account. It has reasonable predictive accuracy and can be conveniently used. It shall help patients understand the risk of postoperative complications before surgery, and offer guidance to surgeons in deciding on the surgical approach.

PURPOSE: To develop a clinical-radiomics nomogram by incorporating radiomics score and clinical predictors for preoperative prediction of microvascular invasion in hepatocellular carcinoma. METHODS: A total of 97 HCC patients were retrospectively enrolled from Shanghai Universal Medical Imaging Diagnostic Center and Changhai Hospital Affiliated to the Second Military Medical University. 909 CT and 909 PET slicers from 97 HCC patients were divided into a training cohort (N = 637) and a validation cohort (N = 272). Radiomics features were extracted from each CT or PET slicer, and features selection was performed with least absolute shrinkage and selection operator regression and radiomics score was also generated. The clinical-radiomics nomogram was established by integrating radiomics score and clinical predictors, and the performance of the models were evaluated from its discrimination ability, calibration ability, and clinical usefulness. RESULTS: The radiomics score consisted of 45 selected features, and age, the ratio of maximum to minimum tumor diameter, and [Formula: see text] F-FDG uptake status were independent predictors of microvascular invasion. The clinical-radiomics nomogram showed better performance for MVI detection (0.890 [0.854, 0.927]) than the clinical nomogram (0.849 [0.804, 0.893]) ([Formula: see text] ). Both nomograms showed good calibration and the clinical-radiomics nomogram's clinical practicability outperformed the clinical nomogram. CONCLUSIONS: With the combination of radiomics score and clinical predictors, the clinical-radiomics nomogram can significantly improve the predictive efficacy of microvascular invasion in hepatocellular carcinoma ([Formula: see text] ) compared with clinical nomogram.

AbstractThis study aims to construct and validate a nomogram for predicting blastocyst formation in patients with diminished ovarian reserve (DOR) during in vitro fertilization (IVF) procedures. A retrospective analysis was conducted on 445 DOR patients who underwent in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) at the Reproductive Center of Yulin Maternal and Child Health Hospital from January 2019 to January 2023. A total of 1016 embryos were cultured for blastocyst formation, of which 487 were usable blastocysts and 529 did not form usable blastocysts. The embryos were randomly divided into a training set (711 embryos) and a validation set (305 embryos). Relevant factors were initially identified through univariate logistic regression analysis based on the training set, followed by multivariate logistic regression analysis to establish a nomogram model. The prediction model was then calibrated and validated. Multivariate stepwise forward logistic regression analysis showed that female age, normal fertilization status, embryo grade on D2, and embryo grade on D3 were independent predictors of blastocyst formation in DOR patients. The Hosmer–Lemeshow test indicated no statistical difference between the predicted probabilities of blastocyst formation and actual blastocyst formation (P > 0.05). These results suggest that female age, normal fertilization status, embryo grade on D2, and embryo grade on D3 are independent predictors of blastocyst formation in DOR patients. The clinical prediction nomogram constructed from these factors has good predictive value and clinical utility and can provide a basis for clinical prognosis, intervention, and the formulation of individualized medical plans.

Since China's launch of the COVID-19 vaccination, the situation of the public, especially the mobile population, has not been optimistic. We investigated 782 factory workers for whether they would get a COVID-19 vaccine within the next 6 months. The participants were divided into a training set and a testing set for external validation conformed to a ratio of 3:1 with R software. The variables were screened by the Lead Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Then, the prediction model, including important variables, used a multivariate logistic regression analysis and presented as a nomogram. The Receiver Operating Characteristic (ROC) curve, Kolmogorov–Smirnov (K-S) test, Lift test and Population Stability Index (PSI) were performed to test the validity and stability of the model and summarize the validation results. Only 45.54% of the participants had vaccination intentions, while 339 (43.35%) were unsure. Four of the 16 screened variables—self-efficacy, risk perception, perceived support and capability—were included in the prediction model. The results indicated that the model has a high predictive power and is highly stable. The government should be in the leading position, and the whole society should be mobilized and also make full use of peer education during vaccination initiatives.

BACKGROUND: The internet has become an important resource for the public to obtain health information. Therefore, the ability to obtain and use such resources has become important for health literacy. This study aimed to establish a prediction model of Chinese students' electronic health literacy (EHL) to guide government policymaking and parental interventions, identify the predictors of EHL in Chinese students using random forests, and establish a corresponding prediction model to help policymakers and parents determine whether primary and secondary school students have high EHL. METHODS: This is a cross-sectional study. From June to August 2021, a cluster sample survey was conducted with 1,300 students from seven primary and secondary schools in Shaanxi Province, China. We evaluated 1,235 primary and secondary school students using the e-health literacy scale. The data were divided into training and testing datasets in a 70:30 ratio for further analysis using random forest. The predictive accuracy of the score was measured using the area under the receiver operating characteristic curve. We also used decision curve analysis to determine the usefulness of the prediction model by quantifying the net benefits at different threshold probabilities in the validation dataset. RESULTS: We found that 33.6% of students had high EHL. The univariate analysis showed that age (P < 0.001), grade (P < 0.001), employment status (P < 0.001), household location (P < 0.001), parental phubbing behavior (P < 0.001), and general self-efficacy (P < 0.001) were significantly associated with EHL. A random forest classification model was developed with the training dataset (872 students), and seven variables were confirmed as important: age, grade, employment status, father education level, game time, parental phubbing behavior, and general self-efficacy. The validation of the model showed good discrimination, with an area under the curve of 0.975 in the training dataset and 0.738 in the testing dataset. The model was ...

Wei Dong,1,2,* Hua Yu,1,2,* Yu-Yao Zhu,1,2,* Zhi-Hong Xian,1,2 Jia Chen,1,2 Hao Wang,3 Chun-Chao Shi,4 Guang-Zhi Jin,5 Hui Dong,1,2 Wen-Ming Cong1,2 1Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai 200438, People's Republic of China; 2Key Laboratory of Signaling Regulation and Targeting Therapy of Liver Cancer, Second Military Medical University, The Ministry of Education, Shanghai 200438, People's Republic of China; 3Department of Hepatobiliary Diseases, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai 200438, People's Republic of China; 4Second Department of Hepatic Surgery, Eastern Hepatobiliary Hospital, Second Military Medical University, Shanghai 200438, People's Republic of China; 5Department of Oncology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200050, People's Republic of China*These authors contributed equally to this workCorrespondence: Hui DongDepartment of Pathology, Eastern Hepatobiliary Surgery Hospital, Shanghai 200438, People's Republic of ChinaEmail 13917078308@126.comWen-Ming CongDepartment of Pathology, Eastern Hepatobiliary Surgery Hospital, Shanghai 200438, People's Republic of ChinaEmail wmcong@smmu.edu.cnPurpose: This study aimed to propose an effective quantitative pathological scoring system and to establish nomogram to assess the stage of cirrhosis and predict postoperative survival of hepatocellular carcinoma (HCC) with cirrhosis patients after hepatectomy.Methods: The scoring system was based on a retrospective study on 163 patients who underwent partial hepatectomy for HCC with cirrhosis. The clinicopathological and follow-up data of 163 HCC with cirrhosis patients who underwent hepatectomy in our hospital from 2010 to 2014 were retrospectively reviewed. A scoring system was established based on the total value of independent predictive factors of cirrhosis. The results were validated using 97 patients operated on from 2011 to 2015 at the same institution. Nomogram was then formulated using a multivariate Cox proportional hazards model to analyze.Results: The scoring system was ultimately composed of 4 independent predictive factors and was divided into 3 levels. The new cirrhosis system score strongly correlated with Child–Pugh score (r=0.8058, P< 0.0001) 3 months after surgery; higher cirrhosis system scores predicted poorer liver function and stronger liver damage 3 months after surgery. Then, a four-factor nomogram for survival prediction was established. The concordance indices were 0.79 for the survival-prediction nomogram. The calibration curves showed good agreement between predictions by the nomogram and actual survival outcomes.Conclusion: This new scoring system of cirrhosis can help us predict the liver function and liver injury 3 months after surgery, and the nomogram enabled accurate predictions of risk of overall survival in patients of HCC with cirrhosis after hepatectomy.Keywords: cirrhosis, hepatocellular carcinoma, HCC, pathology, scoring system, nomogram

Abstract Purpose To develop and externally validate a predictive model for pathologic complete response (pCR) for locally advanced rectal cancer (LARC) based on clinical features and early sequential 18 F-FDG PETCT imaging. Materials and methods Prospective data (i.a. THUNDER trial) were used to train ( N =112, MAASTRO Clinic) and validate ( N =78, Universita Cattolica del S. Cuore) the model for pCR (ypT0N0). All patients received long-course chemoradiotherapy (CRT) and surgery. Clinical parameters were age, gender, clinical tumour (cT) stage and clinical nodal (cN) stage. PET parameters were SUV max , SUV mean , metabolic tumour volume (MTV) and maximal tumour diameter, for which response indices between pre-treatment and intermediate scan were calculated. Using multivariate logistic regression, three probability groups for pCR were defined. Results The pCR rates were 21.4% (training) and 23.1% (validation). The selected predictive features for pCR were cT-stage, cN-stage, response index of SUV mean and maximal tumour diameter during treatment. The models' performances (AUC) were 0.78 (training) and 0.70 (validation). The high probability group for pCR resulted in 100% correct predictions for training and 67% for validation. The model is available on the website www.predictcancer.org. Conclusions The developed predictive model for pCR is accurate and externally validated. This model may assist in treatment decisions during CRT to select complete responders for a wait-and-see policy, good responders for extra RT boost and bad responders for additional chemotherapy.

AbstractThis article aims to identify if belonging to one side or another of the former Hapsburg Empire's border matters in relation to migration intentions. Based on a survey of 3,051 students enrolled at three Romanian universities, and using geo‐referencing, data mining tools, logistic regressions and prediction nomograms, we found that students who have their homes in different parts, depending on this historical border, manifest different sensitivity levels towards recognition of their own value and the poor quality of public institutions and services, as they have different perceptions concerning the role of individual freedom, parental role models, the work ethic and interpersonal trust. These differences further generate opposite migration intentions for the two sub‐samples. Therefore, students who have their homes in the former Empire's area have lower migration intentions than those outside it, despite their proximity to the western borders. The results suggest various economic and non‐economic determinants as important predictors of migration intentions.