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The objective of this study was to predict Ki-67 proliferation index of meningioma by using a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features. A total of 318 cases were enrolled in the study. The clinical, radiomics, and DTL features were selected to construct models. The calculation of radiomics and DTL score was completed by using selected features and correlation coefficient. The deep transfer learning radiomics (DTLR) nomogram was constructed by selected clinical features, radiomics score, and DTL score. The area under the receiver operator characteristic curve (AUC) was calculated. The models were compared by Delong test of AUCs and decision curve analysis (DCA). The features of sex, size, and peritumoral edema were selected to construct clinical model. Seven radiomics features and 15 DTL features were selected. The AUCs of clinical, radiomics, DTL model, and DTLR nomogram were 0.746, 0.75, 0.717, and 0.779 respectively. DTLR nomogram had the highest AUC of 0.779 (95% CI 0.6643–0.8943) with an accuracy rate of 0.734, a sensitivity value of 0.719, and a specificity value of 0.75 in test set. There was no significant difference in AUCs among four models in Delong test. The DTLR nomogram had a larger net benefit than other models across all the threshold probability. The DTLR nomogram had a satisfactory performance in Ki-67 prediction and could be a new evaluation method of meningioma which would be useful in the clinical decision-making. [ABSTRACT FROM AUTHOR]

Purpose: In the clinical management of patients with locally advanced rectal cancer (LARC), the early identification of poor and good responders after neoadjuvant chemoradiotherapy (N-CRT) is essential. Therefore, we developed and validated predictive models including MRI findings from the structured report template, clinical and radiomics parameters to differentiate between poor and good responders in patients with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy. Methods: Preoperative multiparametric MRI from 183 patients with locally advanced rectal cancer (122 in the training cohort, 61 in the validation cohort) was included in this retrospective study. After preprocessing, radiomic features were extracted and two methods of feature selection was applied to reduce the number of radiomics features. Logistic regression (LR) and random forest (RF) machine learning classifiers were trained to identify good responders from poor responders. Multivariable logistic regression analysis was used to incorporate the radiomic signature and clinical risk factors into a nomogram. Classifier performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: For the differentiation of poor and good responders, the radiomics model with an LR classifier achieved AUCs of 0.869 and 0.842 for the training and validation cohorts, respectively. The nomogram showed the highest reproducibility and prognostic ability in the training and validation cohorts, with AUCs of 0.923 (95% confidence interval, 0.872–0.975) and 0.898 (0.819–0.978), respectively. Additionally, the nomogram achieved significant risk stratification of patients in respect to progression free survival (PFS). Conclusions: The nomogram accurately differentiated good and poor responders in patients with LARC undergoing N-CRT, and showed significant performance for predicting PFS. [ABSTRACT FROM AUTHOR]

Objective: To explore whether cumulative serum urate (cumSU) is correlated with diabetes type II mellitus incidence. Methods: In this study, we recruited individuals participating in all Kailuan health examinations from 2006 to 2013 without stroke, cancer, gestation, myocardial infarction, and diabetes type II diagnosis in the first three examinations. CumSU was calculated by multiplying the average serum urate concentration and the time between the two examinations (umol/L × year). CumSU levels were categorized into five groups: Q1–Q5. The effect of cumSU on diabetes type II incidence was estimated by logistic regression. Results: A total of 36,277 individuals (27,077 men and 9200 women) participated in the final analysis. The multivariate logistic regression model showed the odds ratios (95% confidence intervals) of diabetes type II from Q1 to Q5 were 1.00 (reference), 1.25 (1.00 to 1.56), 1.43 (1.15 to 1.79), 1.49 (1.18 to 1.87), and 1.80 (1.40 to 2.32), respectively. Multivariable odds ratios per 1-standard deviation increase in cumSU were 1.26 (1.17 to 1.37) in all populations, 1.20 (1.10 to 1.32) for men, and 1.52 (1.27 to 1.81) for women, respectively. Conclusions: CumSU is a significant risk factor for diabetes type II. Individuals with higher cumSU, especially women, are at a higher risk of diabetes type II independent of other known risk factors. Key Points • Cumulative exposure to serum urate is a significant risk factor for diabetes type II. • Individuals with higher cumSU, especially women, are at a higher risk of diabetes type II. [ABSTRACT FROM AUTHOR]