Radiology: Application of DTI and PWI Radiology and high-throughput sequencing techniques in IDH wild glioblastoma

Backgroundhigh-throughput sequencing technique (NGS) is highly sensitive to tumor genomics analysis, but its clinical significance to clinical treatment decision-making based on imaging prediction is limitedpurpose
this study aims to predict the core signaling pathways of IDH wild glioblastoma using diffusion-weighted imaging (DTI) and perfusion-weighted imaging (PWI) radiology features and NGSmaterials and methods
used patients with ngS and anatomy, diffusion weighting and PWI to build a radiology modelIn order to verify the model effectiveness of the predictive core signaling pathway, the patients of IDH wild glioma were evaluatedThe selection of radiology characteristics was carried out by T-test, minimum absolute shrinkage, selecting operator method and random forest methodThe combined radiology characteristics, age, location, RTK, P53 and retinoblastoma 1 pathways were evaluated using the area (AUC) under ROC curveresults120 patients were evaluated in this studyOf these, 85 patients were in the training group and 35 were idh wild glioma patients in the validation groupThe radiology genetic model included a total of 71 RTK features, 17 P53 features and 35 optic neuroblastoma channel semplicular tumor sempsThe combined model sat better than the results based on anatomical imaging in RTK (P - .03), psycoma (P - .03) and PWI-based P53 channel characteristics (P -.04) The combined model predicted the AUC values of the core signal channels RTK, P53, and psycoma tumors at 0.88 (95% CI: 0.74, 1), 0.76 (95% CI: 0.59, 0.92), 0.81 (95% CI: 0.64, 0.97) conclusions
radiomic characteristic models of diffusion-weighted imaging and perfusion-weighted imaging can help to evaluate the characteristics of core signal pathways, which will guide the targeted treatment of idh wild glioblastoma