Background Acute large vessel occlusion due to underlying intracranial atherosclerotic stenosis (ICAS-LVO) increases the difficulty of revascularization, resulting in frequent re-occlusion. The establishment of its pathogenesis before endovascular treatment (EVT) is beneficial for patients. We aimed at developing and validating a clinical prediction model for ICAS-LVO patients before EVT. Methods Patients with acute large vessel occlusion at Jining No. 1 People’s Hospital from January 2019 to September 2021 were retrospectively included as the training cohort. The 70 patients who met the inclusion and exclusion criteria were included in the validation cohort (October 2021 to May 2022). Demographics, onset form, medical history, digital subtraction angiography (DSA) imaging data, and laboratory test data were collected. Preprocedural parameters for the ICAS-LVO risk prediction model were established by stepwise logistic regression controlling for the confounding effects. Then, we constructed a nomogram model and evaluated its performance via the Hosmer-Lemeshow goodness-of-fit test, area under the ROC curve (AUC) analysis. Results The 231 acute LVO patients were included in the final analysis, 74 (32.3%) patients were ICAS-LVO. A preoperative diagnosis prediction model consisting of five predictors for ICAS-LVO, including fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2. The model depicted an acceptable calibration (Hosmer-Lemeshow test, p = 0.451) and good discrimination (AUC, 0.941; 95% confidence interval, 0.910–0.971). The optimal cut-off value for the ICAS-LVO scale was 2 points, with 86.5% sensitivity, 91.1% specificity, and 90.5% accuracy. In the validation cohort, the discriminative ability was promising with an AUC value of 0.897, implying a good predictive performance. Conclusion The established ICAS-LVO scale, which is composed of five predictors: fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2, has a good predictive value for ICAS-LVO in Chinese populations.
PurposeTo assess the association of radiomics features based on multiparametric MRI (mpMRI) with the proportion of intraductal carcinoma of prostate (IDC-P) and validate the predictive models.Materials and MethodsWe retrospectively included pre-treatment MR images of prostate cancer (PCa) with IDC components of high proportion (≥10%, hpIDC-P), low proportion (<10%, lpIDC-P), and pure acinar adenocarcinoma (PAC) from our institution for training and internal validation and cooperated cohort for external validation. Normalized images of T2WI, diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) map, and dynamic contrast enhanced (DCE) sequences were used for radiomics modeling. The clinical model was built based on serum total prostate specific antigen (tPSA) and Gleason score (GS), and the integrated model was the combination of Rad-score and clinicopathological data. The discrimination ability was assessed by area under the receiver operating characteristic curve (ROC-AUC) in the internal and external validation sets and compared by DeLong test.ResultsOverall, 97 patients with hpIDC-P, 87 lpIDC-P, and 78 PAC were included for training and internal validation, and 11, 16, and 19 patients for external validation. The integrated model for predicting hpIDC-P got the best ROC-AUC of 0.88 (95%CI = 0.83-0.93) in internal and 0.86 (95%CI = 0.72-1.0) in external validation, which both outperformed clinical models (AUC=0.78, 95% CI = 0.72-0.85, AUC=0.69, 95% CI = 0.5-0.85, respectively) based solely on GS, and the radiomics model (AUC=0.85, 95% CI = 0.79-0.91) was slightly inferior to the integrated model and better than the clinical model in internal dataset. The integrated model for predicting lpIDC-P outperformed both radiomics and clinical models in the internal dataset, while slightly inferior to the integrated model for predicting hpIDC-P.ConclusionsRadiomics signature improved differentiation of both hpIDC-P and lpIDC-P versus PAC when compared with the clinical model based on Gleason score, and was validated in an external cohort.
Background: Periostin is a secretory extracellular protein that has multiple biological roles. It has been confirmed to play a significant role in embryo implantation and development,placental formation. In our study, we aimed to explore whether the levels of serum periostin on the day of frozen-thawed embryo transfer (FET) affected pregnancy outcomes.Methods: This was a retrospective cohort study consisting of 286 frozen-thawed embryo transfer cycles(FETs). According to pregnancy outcomes, the participants were devided into the pregnant group (n=110) and non-pregnant group (n=176). The concentration of serum periostin was determined by enzyme-linked immunosorbent assay (ELISA).Results: In the pregnant group, the level of serum periostin was dramatically higher compared with the non-pregnant group (41.25 ± 25.77 vs. 30.94 ± 18.28 ng/mL; P<0.05). Multivariate logistic regression analysis demonstrated that the level of serum periostin had a significant effect on the clinical pregnancy (OR=1.03, 95% CI: 1.01, 1.04). The receiver operator characteristic (ROC) curve analysis was performed to assess the predictive value of the serum periostin level (AUC 0.61; 95% CI:0.52 ,0.68). Conclusions: In conclusion, the levels of periostin serum on the day of frozen-thawed embryo transfer affected the pregnancy outcomes for patients who underwent frozen thawed embryo transfer.
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