Intravenous thrombolysis is the most commonly used drug therapy for patients with acute ischemic stroke, which is often accompanied by complications of intracerebral hemorrhage transformation (HT). This study proposed to build a reliable model for pretreatment prediction of HT. Specifically, 5400 radiomics features were extracted from 20 regions of interest (ROIs) of multiparametric MRI images of 71 patients. Furthermore, a minimal set of all-relevant features were selected by LASSO from all ROIs and used to build a radiomics model through the random forest (RF). To explore the significance of normal ROIs, we built a model only based on abnormal ROIs. In addition, a model combining clinical factors and radiomics features was further built. Finally, the models were tested on an independent validation cohort. The radiomics model with 14 All-ROIs features achieved pretreatment prediction of HT (AUC = 0.871, accuracy = 0.848), which significantly outperformed the model with only 14 Abnormal-ROIs features (AUC = 0.831, accuracy = 0.818). Besides, combining clinical factors with radiomics features further benefited the prediction performance (AUC = 0.911, accuracy = 0.894). So, we think that the combined model can greatly assist doctors in diagnosis. Furthermore, we find that even if there were no lesions in the normal ROIs, they also provide characteristic information for the prediction of HT.

Background Image‐guided computer‐aided navigation system is an indispensable part of computer assisted orthopaedic surgery. However, the location and number of fiducial markers, the time required to localise fiducial markers in existing systems affect their effectiveness. Method The study proposed that spatial surface registration between the point cloud on the surface of the fusion model based on preoperative knee MRI and CT images and the point cloud on the cartilage surface captured by intraoperative laser scanner could solve the above limitations. Results The experimental results show that the registration error of the method is less than 2 mm, but the total time from scanning the point cloud on patient's cartilage surface to registering it with the point cloud in preoperative image space is less than 2 min. Conclusion The method achieves the registration accuracy similar to existing methods without selecting anatomical corresponding points, which is of great help to the clinic.