Objective:To investigate the efficacy of a predictive model for differentiating benign and malignant renal tumors based on CT radiomic features and clinical features.Methods:A retrospective study was conducted on 1 395 patients with renal tumors admitted to the First Affiliated Hospital of Zhengzhou University from December 2011 to December 2021, including 842 males and 553 females. The median age was 55 (44, 59) years, and the median tumor diameter was 3.6 (2.7, 4.6) cm. All patients underwent contrast-enhanced CT scaning before surgery, and radiomic features were extracted from non-contrast, arterial, and venous phase images. Prediction models for distinguishing benign and malignant renal tumors were constructed using five machine learning algorithms (logistic regression, support vector machine, neural network, random forest, and extreme gradient boosting), and these models were then ensembled to construct a stacking classifier. All patients underwent partial nephrectomy, and they were divided into a training group (941 cases, December 2011 to June 2020) and a validation group (454 cases, July 2020 to December 2021) based on the date of surgery. A clinical-radiomic model was developed by combining the result of stacking classifier, clinical features and CT report results, and its predictive performance was evaluated in the validation group.Results:The radiomic signature based on the combined features and five machine learning algorithms(AUC 0.835-0.844) showed higher accuracy in predicting benign and malignant renal tumors compared to single phases (AUC 0.744-0.831). After integrating the five machine learning algorithms, the AUC of the three-phase combined radiomic model in the validation group improved to 0.847(95% CI 0.802-0.892). The clinical-radiomic model, incorporating radiomic features, clinical features, and CT report results, achieved a significantly higher AUC in the validation group compared to radiologists [0.919(95% CI 0.889-0.950)vs. 0.835(95% CI 0.786-0.883), P<0.01]. Conclusions:The predictive model integrating CT radiomics features, clinical characteristics, and CT report results demonstrates excellent discriminative ability in distinguishing benign and malignant renal tumors.

Objective:To investigate the efficacy of a predictive model for differentiating benign and malignant renal tumors based on CT radiomic features and clinical features.Methods:A retrospective study was conducted on 1 395 patients with renal tumors admitted to the First Affiliated Hospital of Zhengzhou University from December 2011 to December 2021, including 842 males and 553 females. The median age was 55 (44, 59) years, and the median tumor diameter was 3.6 (2.7, 4.6) cm. All patients underwent contrast-enhanced CT scaning before surgery, and radiomic features were extracted from non-contrast, arterial, and venous phase images. Prediction models for distinguishing benign and malignant renal tumors were constructed using five machine learning algorithms (logistic regression, support vector machine, neural network, random forest, and extreme gradient boosting), and these models were then ensembled to construct a stacking classifier. All patients underwent partial nephrectomy, and they were divided into a training group (941 cases, December 2011 to June 2020) and a validation group (454 cases, July 2020 to December 2021) based on the date of surgery. A clinical-radiomic model was developed by combining the result of stacking classifier, clinical features and CT report results, and its predictive performance was evaluated in the validation group.Results:The radiomic signature based on the combined features and five machine learning algorithms(AUC 0.835-0.844) showed higher accuracy in predicting benign and malignant renal tumors compared to single phases (AUC 0.744-0.831). After integrating the five machine learning algorithms, the AUC of the three-phase combined radiomic model in the validation group improved to 0.847(95% CI 0.802-0.892). The clinical-radiomic model, incorporating radiomic features, clinical features, and CT report results, achieved a significantly higher AUC in the validation group compared to radiologists [0.919(95% CI 0.889-0.950)vs. 0.835(95% CI 0.786-0.883), P<0.01]. Conclusions:The predictive model integrating CT radiomics features, clinical characteristics, and CT report results demonstrates excellent discriminative ability in distinguishing benign and malignant renal tumors.

Objective To investigate the predictive value of CALLY index for ischemic post-stroke depression(PSD).Methods The clinical data of 179 patients with ischemic stroke were included,and the demographic information,medical history,stroke severity and laboratory indicators at admission were collected.After 6 months of follow-up,all patients were assessed for depressive symptoms using the 17-item Hamilton Depression Scale(HAMD-17).Patients were divided into the PSD group(48 cases)and the non-PSD group(131 cases).Differences in clinical characteristics were compared between the PSD group and the non-PSD group.CALLY index was calculated from C-reactive protein(CRP),albumin(ALB)and lymphocyte counts.Receiver operating characteristic(ROC)curve was used to analyze the predictive value of CALLY index to PSD.Spearman correlation analysis was used for the correlation between CALLY index and neurological and cognitive function in PSD patients.K-M curve and Cox regression were used for analyzing the influence of CALLY index on PSD.Results The CALLY index of 179 patients ranged from 0.54 to 1.79,with a median of 1.08.ROC curve analysis showed that the optimal critical value of CALLY index to predict PSD was 1.09,and the area under ROC curve was 0.757(95%CI:0.687-0.818).Compared with the non-PSD group,the proportion of females was higher in the PSD group,and the proportion of patients with hyperlipidemia was increased with shorter years of education.The serum C-reactive protein(CRP)was higher,and albumin(ALB)and CALLY index were lower(P＜0.05).The K-M curve showed that the incidence of PSD was significantly higher in the low CALLY index group(CALLY≤1.08)than that in the higher CALLY index group(CALLY＞1.08,33.0%vs.20.5%,Log rank χ2=8.553,P=0.004).Cox regression analysis showed that after adjusting for other covariates,the decreased CALLY index was an independent risk factor for PSD(HR=2.651,95%CI:1.269-5.540,P＜0.05).Conclusion CALLY index has a certain predictive value for PSD in acute ischemic stroke patients,which is helpful for early identification and timely intervention to improve the prognosis of patients.

BACKGROUND: Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer. METHODS: MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias was evaluated using a Deeks' funnel plot and linear regression test. RESULTS: A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96-0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94-0.98) and 0.87 (95% CI: 0.82-0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies. CONCLUSIONS Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening.
Humans ; Adult ; Middle Aged ; Aged ; Lung Neoplasms/diagnostic imaging* ; Early Detection of Cancer ; Sensitivity and Specificity ; Mass Screening ; Tomography, X-Ray Computed

Objective To observe the effect of different energy levels of pulsed Nd∶YAG laser irradiation on the changes of NO and NOS levels in ulcer tissue of oral ulcer model.Methods Forty-eight Sprague-Dawley rats were inflicted with oral ulcer,and then randomly and equally assigned to 6 groups,that is,oral ulcer group,1% iodine glycerin group and the rest 4 groups receiving pulsed Nd∶YAG laser irradiation at 1.5 W 40 mJ,1.5 W 60 mJ,1.5 W 80 mJ or 1.5 W 100 mJ(once per day,for 3 consecutive days).The other 8 rats served as normal control.In 24 h after last treatment,4 rats from each group were sacrificed and their cheek pouches were taken out for NO and NOS levels in the mucosal tissue by spectrophotomentry.Ulcer healing were observed 24 h after the last treatment for 8 consecutive days.The efficiency of Nd∶YAG laser irradiation at different powers were evaluated.Results Irradiation groups had significantly shorter healing time compared with iodine glycerin group.Level of NO and NOS activity in 1.5 W 60 mJ and 1.5 W 80 mJ groups was evidently lower than the ulcer group(P