Objective:To investigate the value of ADC map-based radiomics model for identifying the ischemic penumbra (IP) in acute ischemic stroke (AIS).Methods:From January 2014 to October 2019, data of 241 patients with AIS involving the anterior cerebral circulation within 24 h after stroke onset in the First People′s Hospital of Nantong City was analyzed retrospectively. All patients received routine T 1WI, T 2WI, DWI and dynamic susceptibility contrast-perfusion weighted imaging (DSC-PWI). Considering the PWI-DWI mismatch model as the gold standard for determining IP, patients were divided into the PWI-DWI mismatch (84 cases) and PWI-DWI non-mismatch (157 cases) groups. The ROI of the low signal area and the surrounding area was drawn by two doctors at the maximum level of the lesions on the ADC maps. Then the images were imported into AK analysis software to extract the features. Firstly, the inter-class correlation coefficient was used to screen out the features with high consistency, then the maximum relevance and minimum redundancy (mRMR) and least absolute shrinkage and selection operator (Lasso) regression analysis were used to screen the features. The selected features were used to construct their own radiomics model. ROC curve was used to evaluate the performance of the models, and Delong test was used to compare the area under the curve (AUC) of the two models. Results:After screening, 12 features (LongRunLowGreyLevelEmphasis_angle135_offset7, LongRunLowGreyLevelEmphasis_AllDirection_offset7, GLCMEntropy_AllDirection_offset4_SD, GLCMEnergy_angle45_offset1, ColGE_W11B25_16, ColGE_W11B25_24, HaraEntropy, SurfaceVolumeRatio, Sphericity, Quantile0.025, uniformity and Percentile75) were used to construct the radiomics model based on the low signal area of the ADC map. The area under the ROC curve in the training set was 0.900, and the sensitivity, specificity and accuracy were 84.5%, 81.4% and 83.4%, respectively. The area under the ROC curve in the validation set was 0.870, and the sensitivity, specificity and accuracy were 80.9%, 84.0% and 81.9%, respectively. Eleven features(RunLengthNonuniformity_AllDirection_offset1_SD, ShortRunLowGreyLevelEmphasis_angle45_offset1, HighGreyLevelRunEmphasis_AllDirection_offset1_SD, ShortRunLowGreyLevelEmphasis_AllDirection_offset7, HaralickCorrelation_AllDirection_offset4_SD, ClusterShade_angle45_offset7, InverseDifferenceMoment_AllDirection_offset7_SD, ColGE_W3B20_0, sumAverage, SurfaceVolumeRatio and VolumeMM) were used to construct the radiomics model based on the surrounding area of ADC map. The area under ROC curve in training set was 0.820, the sensitivity, specificity and accuracy were 80.5%, 80.2% and 80.4%, respectively; the area under ROC curve in validation set was 0.800, the sensitivity, specificity and accuracy were 78.7%, 80.0% and 79.2%, respectively. The AUC of the radiomics model based on the low signal area of the ADC map was larger than that based on the surrounding area of the ADC map (training set: Z=3.017, P=0.003; validation set: Z=0.604, P=0.002). Conclusion:The radiomics model based on ADC map has a good diagnostic efficacyin identifying the IP.

Objective To investigate the effects of platelet-rich plasma-derived exosomes(PRP-Exos)on the proliferation and migration of tendon stem/progenitor cell(TSPC).Methods PRP-Exos were extracted through the combination of polymer-based precipitation and ultracentrifugation.The morphology,concentration,and particle size of PRP-Exos were identified by transmission electron microscopy and nanoparticle tracking analysis.The expression levels of surface marker proteins on PRP-Exos and platelet membrane glycoproteins were deter-mined by Western blot analysis.Rat TSPC was extracted and cultured,and the expression of surface marker mol-ecules on TSPC was detected using flow cytometry and immunofluorescence staining.The proliferation of TSPC in-fluenced by PRP-Exos was evaluated using CCK-8 assay and EdU assay.The effect of PRP-Exos on the migration of TSPC was evaluated by cell scratch assay and Transwell assay.Results The extracted PRP-Exos exhibit typi-cal saucer-like structures,with a concentration of 4.9 ×1011 particles/mL,an average particle size of(132.2±56.8)nm,and surface expression of CD9,CD63 and CD41.The extracted TSPC expressed the CD44 pro-tein.PRP-Exos can be taken up by TSPC,and after co-cultured for 48 h,concentrations of 50 and 100 μg/mL of PRP-Exos significantly promoted the proliferation of TSPC(both P＜0.001),with no statistical difference be-tween the two concentrations(P=0.283).Additionally,after co-cultured for 24 h,50 μg/mL of PRP-Exos significantly promoted the migration of TSPC(P＜0.001).Conclusion Under in vitro culture conditions,PRP-Exos significantly promote the proliferation and migration of rat TSPC.

Objective : To evaluate the prognostic value of a radiomics model based on the peritumoral region of gli- oma． Methods : 138 patients with glioma were retrospectively analyzed ，medical imaging interaction toolkit ( MITK) software was used to obtain the magnetic resonance imaging (MRI) images of peritumoral area 5 mm，10 mm and 20 mm from the tumor edge and extract texture features．The texture features were screened the radiomics model was established and the radiomic score was calculated．A clinical prediction model and a combined predic- tion model along with Rad-score and clinical risk factors were established．The combined prediction model was dis- played as a nomogram，and the predictive performance of the model for survival in glioma patients was evaluated. Results : In the validation set，the C-index value of the radiomics model based on the peritumoral region 10 mm a- way from the tumor edge based on T2 weighted image (T2WI) images was 0. 663 (95% CI = 0. 72－0. 78) ，resul- ting in the best prediction performance．On the training set and validation set，the C-index of the nomogram was 0. 770 and 0. 730，respectively，indicating that the prediction performance of nomogram was better than those of the radiomics model and clinical prediction model．The model had the highest prediction effect on the 3-year survival rate of glioma patients (training set area under curve (AUC) = 0. 93，95% CI = 0. 83 － 0. 98 ; validation set AUC = 0. 88，95% CI = 0. 76 －0. 99) ．The calibration curve showed that the joint prediction nomogram in both the training set and the validation set had good performance． Conclusion The combined prediction model based on the preoperative T2WI images in the peritumoral region 10 mm from the tumor edge and the clinicopathological risk factors can accurately predict the prognosis of glioma，providing the best effect of prediction on the 3-year survival rate of glioma.

Objective : To study the best machine learning method for early prediction of hematoma expansion in hypertensive intracerebral hemorrhage based on head CT plain scan. Methods : The CT images of 130 patients with cerebral hemorrhage were retrospectively analyzed , and the texture features of the head CT plain scan were extracted. The classifier was trained by selecting the features , and the six classic machine learning methods were crossvalidated to evaluate the stability and performanceof predicting cerebral hemorrhage hematoma expansion. Results: The prediction performance of support vector machine (SVM⁃Radial) (AUC 0. 714 ± 0. 144 , accuracy 0. 723 ± 0. 109) , generalized linear model ( GLM) prediction performance ( AUC 0. 643 ± 0. 125 , accuracy 0. 587 ± 0. 136) , random forest (RF) prediction performance (AUC 0. 686 ± 0. 128 , accuracy 0. 680 ± 0. 130) , k ⁃nearest neighbor (kNN) prediction performance ( AUC 0. 657 ± 7C 15 , accuracy 0. 639 ± 39 performance 19) , gradient boosting tree algorithm (GBM) Prediction performance ( AUC 0. 718 ± 0. 141 , accuracy 0. 670 ± 0. 126) , neural network (NNet) prediction performance (AUC 0. 659 ± 0. 162 , accuracy 0. 680 ± 0. 130) , in which support vector machines showed high prediction performance , generalized linear model showed low predictive performance. Conclusion Among the six machine learning methods based on cranial CT radiomics to predict early hematoma expansion in hypertensive intracerebral hemorrhage , support vector machine (SVM⁃Radial) has the best predictive performance and has potential clinical application value.