The poor 5-year survival rate in high-grade osteosarcoma (HOS) has not been increased significantly over the past 30 years. This work aimed to develop a radiomics nomogram for survival prediction at the time of diagnosis in HOS.In this retrospective study, an initial cohort of 102 HOS patients, diagnosed from January 2008 to March 2011, was used as the training cohort. Radiomics features were extracted from the pretreatment diagnostic computed tomography images. A radiomics signature was constructed with the lasso algorithm; then, a radiomics score was calculated to reflect survival probability by using the radiomics signature for each patient. A radiomics nomogram was developed by incorporating the radiomics score and clinical factors. A clinical model was constructed by using clinical factors only. The models were validated in an independent cohort comprising 48 patients diagnosed from April 2011 to April 2012. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Kaplan–Meier survival analysis was performed.The radiomics nomogram showed better calibration and classification capacity than the clinical model with AUC 0.86 vs. 0.79 for the training cohort, and 0.84 vs. 0.73 for the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. A significant difference (p-value <.05; log-rank test) was observed between the survival curves of the nomogram-predicted survival and non-survival groups. The radiomics nomogram may assist clinicians in tailoring appropriate therapy.
Scatter correction is an essential technique to improve the image quality of cone-beam CT (CBCT). Although different scatter correction methods have been proposed in the literature, a standard solution is still being studied due to the limitations including accuracy, computation efficiency and generalization. In this paper, we propose a novel scatter correction scheme for CBCT using a deep residual convolution neural network (DRCNN) to overcome the limitations. The proposed method combines the deep convolution neural network (CNN) and the residual learning framework (RLF) to train the mapping function from the uncorrected image to the corrected image. Two residual network modules (RNMs) are built based on the RLF to improve the accuracy of the mapping function by strengthening the propagation of the gradient. The dropout operations are applied as the regularizer of the network to avoid the overfitting problem. The RMSE of the corrected images reconstructed using the DRCNN is reduced from over 200 HU to be about 20 HU. The structural similarity (SSIM) is slightly increased from 0.95 to 0.99, indicating that the proposed scheme maintains the anatomical structure. The proposed DRCNN has a higher accuracy of scatter correction than the networks without the RLF or the dropout operations. The proposed network is effective, efficient and robust as a solution to the CBCT scatter correction.
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