Uninvolved liver dose prediction in stereotactic body radiation therapy for liver cancer based on the neural network method

BACKGROUND The quality of a radiotherapy plan often depends on the knowledge and expertise of the plan designers.AIM To predict the uninvolved liver dose in stereotactic body radiotherapy(SBRT)for liver cancer using a neural network-based method.METHODS A total of 114 SBRT plans for liver cancer were used to test the neural network method.Sub-organs of the uninvolved liver were automatically generated.Correlations between the volume of each sub-organ,uninvolved liver dose,and neural network prediction model were established using MATLAB.Of the cases,70%were selected as the training set,15%as the validation set,and 15%as the test set.The regression R-value and mean square error(MSE)were used to evaluate the model.RESULTS The volume of the uninvolved liver was related to the volume of the corresponding sub-organs.For all sets of Rvalues of the prediction model,except for D_(n0)which was 0.7513,all R-values of D_(n10)-D_(n100)and D_(nmean)were>0.8.The MSE of the prediction model was also low.CONCLUSION We developed a neural network-based method to predict the uninvolved liver dose in SBRT for liver cancer.It is simple and easy to use and warrants further promotion and application.

Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.

Changes in the quality of river water before,during and after a major flood event associated with a La Nina cycle and treatment for drinking purposes

The treatment of organics present in the lower reaches of a major river system（the Murray–Darling Basin, Australia） before（March–July 2010）, during（December 2010–May 2011） and after（April–December 2012） a major flood period was investigated. The flood period（over 6 months）occurred during an intense La Nia cycle, leading to rapid and high increases in river flows and organic loads in the river water. Dissolved organic carbon（DOC） increased（2–3 times） to high concentrations（up to 16 mg/L） and was found to correlate with river flow rates. The treatability of organics was studied using conventional jar tests with alum and an enhanced coagulation model（mEnCo）. Predicted mean alum dose rates（per mg DOC） were higher before（9.1 mg alum/mg DOC） and after（8.5 mg alum/mg DOC） than during the flood event（8.0 mg alum/mg DOC）,indicating differences in the character of the organics in raw waters. To assess the character of natural organic matter present in raw and treated waters, high performance size exclusion chromatography with UV and fluorescence detectors were used. During the flood period, high molecular weight UV absorbing compounds（〉2 kDa） were mostly detected in waters collected,but were not evident in waters collected before and afterwards. The relative abundances of humic-like and protein-like compounds during and following the flood period were also investigated and found to be of a higher molecular weight during the flood period. The treatability of the organics was found to vary over the three climate conditions investigated.