The development and evolution of the concept of quality of life(QOL)is introduced. Focuses on the quality of life scale in stroke patients with application and analyze the advantages and disadvantages of various scale.

Objective:To investigate the role of three-dimensional dose distribution-based deep learning model in predicting distant metastasis of head and neck cancer.Methods:Radiotherapy and clinical follow-up data of 237 patients with head and neck cancer undergoing intensity-modulated radiotherapy (IMRT) from 4 different institutions were collected. Among them, 131 patients from HGJ and CHUS institutions were used as the training set, 65 patients from CHUM institution as the validation set, and 41 patients from HMR institution as the test set. Three-dimensional dose distribution and GTV contours of 131 patients in the training set were input into the DM-DOSE model for training and then validated with validation set data. Finally, the independent test set data were used for evaluation. The evaluation content included the area under receiver operating characteristic curve (AUC), balanced accuracy, sensitivity, specificity, concordance index and Kaplan-Meier survival curve analysis.Results:In terms of prognostic prediction of distant metastasis of head and neck cancer, the DM-DOSE model based on three-dimensional dose distribution and GTV contours achieved the optimal prognostic prediction performance, with an AUC of 0.924, and could significantly distinguish patients with high and low risk of distant metastasis (log-rank test, P<0.001). Conclusion:Three-dimensional dose distribution has good predictive value for distant metastasis in head and neck cancer patients treated with IMRT, and the constructed prediction model can effectively predict distant metastasis.

OBJECTIVE: To compare the accuracy of different methods for image registration in image-guided adaptive brachytherapy (IGABT) for cervical cancer. METHODS: The last treatment planning CT images (CT1) and the first treatment planning CT images (CT2) were acquired from 15 patients with cervical cancer and registered with different match image qualities (retained/removed catheter source in images) and different match regions [target only (S Group)/ interested organ structure (M Group)/body (L Group)] in Velocity3.2 software. The dice similarity coefficient (DSC) between the clinical target volumes (CTV) of the CT1 and CT2 images (CTVCT1 and CTVCT2, respectively) and between the organs-at-risk (OAR) of the two imaging datasets (OARCT1 and OARCT2, respectively) were used to evaluate the image registration accuracy. RESULTS: The auto-segmentation volume of the catheter source using Velocity software based on the CT threshold was the closest to the actual volume within the CT value range of 1700-1800 HU. In the retained group, the DSC for the OARs of was better than or equal to that of the removed group, and the DSC value of the rectum was significantly improved ( < 0.05). For comparison of different match regions, the high-risk target volume (HRCTV) and the low-risk target volume (IRCTV) had the best precision for registration of the target area, which was significantly greater than that of M group and L group ( < 0.05). The M group had better registration accuracy of the target area and the best accuracy for the OARs. The DSC values of the bladder and rectum were significantly better than those of the other two groups ( < 0.05). CONCLUSIONS The CT value range of 1700-1800 HU is optimal for automatic image segmentation using Velocity software. Automatic segmentation and shielding the volume of the catheter source can improve the image quality. We recommend the use of interested organ structures regions for image registration in image-guided adaptive brachytherapy for cervical cancer.
Brachytherapy ; methods ; standards ; Female ; Humans ; Organs at Risk ; diagnostic imaging ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted ; methods ; standards ; Radiotherapy, Image-Guided ; methods ; standards ; Software ; Tomography, X-Ray Computed ; methods ; standards ; Uterine Cervical Neoplasms ; diagnostic imaging ; radiotherapy

In order to suppress the geometrical artifacts caused by random jitter in ray source scanning, and to achieve flexible ray source scanning trajectory and meet the requirements of task-driven scanning imaging, a method of free trajectory cone-beam computed tomography (CBCT) reconstruction is proposed in this paper. This method proposed a geometric calibration method of two-dimensional plane. Based on this method, the geometric calibration phantom and the imaging object could be simultaneously imaged. Then, the geometric parameters could be obtained by online calibration method, and then combined with the geometric parameters, the alternating direction multiplier method (ADMM) was used for image iterative reconstruction. Experimental results showed that this method obtained high quality reconstruction image with high contrast and clear feature edge. The root mean square errors (RMSE) of the simulation results were rather small, and the structural similarity (SSIM) values were all above 0.99. The experimental results showed that it had lower image information entropy (IE) and higher contrast noise ratio (CNR). This method provides some practical value for CBCT to realize trajectory freedom and obtain high quality reconstructed image.
Algorithms ; Calibration ; Cone-Beam Computed Tomography ; Image Processing, Computer-Assisted ; Phantoms, Imaging