This is the first preliminary study to develop prediction models for aneurysm rupture risk using radiomics analysis based on follow-up magnetic resonance angiography (MRA) images. We selected 103 follow-up images from 18 unruptured aneurysm (UA) cases and 10 follow-up images from 10 ruptured aneurysm (RA) cases to build the prediction models. A total of 486 image features were calculated, including 54 original features and 432 wavelet-based features, within each aneurysm region in the MRA images for the texture patterns. We randomly divided the 103 UA data into 50 training and 53 testing data and separated the 10 RA data into 1 test and 9 training data to be increased to 54 using a synthetic minority oversampling technique. We selected 11 image features associated with UAs and RAs from 486 image features using the least absolute shrinkage and the selection operator logistic regression and input them into a support vector machine to build the rupture prediction models. An imbalanced adjustment training and test strategy was developed. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.971, 0.948, 0.700, and 0.953, respectively. This prediction model with non-invasive MRA images could predict aneurysm rupture risk for SAH prevention.
Purpose: It is time‐consuming and might cause re‐planning to check couch‐gantry and patient‐gantry collisions on a radiotherapy machine when using couch rotations for non‐coplanar beam angles. The aim of this study was to develop a computer‐graphics (CG)‐based radiation therapy simulator with physical modeling for avoidance of collisions between gantry and couch or patient on a radiotherapy machine. Methods: The radiation therapy simulator was three‐dimensionally constructed including a radiotherapy machine (Clinac iX, Varian Medical Systems), couch, and radiation treatment room according to their designs by using a physical‐modeling‐based computer graphics software (Blender, free and open‐source). Each patient was modeled by applying a surface rendering technique to their planning computed tomography (CT) images acquired from 16‐slice CT scanner (BrightSpeed, GE Healthcare). Immobilization devices for patients were scanned by the CT equipment, and were rendered as the patient planning CT images. The errors in the collision angle of the gantry with the couch or patient between gold standards and the estimated values were obtained by fixing the gantry angle for the evaluation of the proposed simulator. Results: The average error of estimated collision angles to the couch head side was ‐8.5% for gantry angles of 60 to 135 degree, and ‐5.5% for gantry angles of 225 to 300 degree. Moreover, the average error of estimated collision angles to the couch foot side was ‐1.1% for gantry angles of 60 to 135 degree, and 1.4% for gantry angles of 225 to 300 degree. Conclusion: The CG‐based radiation therapy simulator could make it possible to estimate the collision angle between gantry and couch or patient on the radiotherapy machine without verifying the collision angles in the radiation treatment room.
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