Purpose: To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorithm. Methods and Materials: A residual neural network-based deep learning model is trained to predict a dose distribution based on patient-specific geometry and prescription dose. A total of 270 headand-neck cancer cases were enrolled in this study, including 195 cases in the training dataset, 25 cases in the validation dataset, and 50 cases in the testing dataset. All patients were treated with IMRT with a variety of different prescription patterns. The model input consists of CT images and contours delineating the organs at risk (OARs) and planning target volumes (PTVs). The algorithm output is trained to predict the dose distribution on the CT image slices. The obtained prediction model is used to predict dose distributions for new patients. Then, an optimization objective function based on these predicted dose distributions is created for automatic plan generation. Results: Our results demonstrate that the deep learning method can predict clinically acceptable dose distributions. There is no statistically significant difference between prediction and real clinical plan for all clinically relevant dose-volume histogram (DVH) indices, except brainstem, right and left lens. However, the predicted plans were still clinically acceptable. The results of plan generation show no statistically significant differences between the automatic generated plan and the predicted plan except PTV 70.4 , but the difference is only 0.5% which is still clinically acceptable. Conclusion: This study developed a new automated radiotherapy treatment planning system based on 3D dose prediction and 3D dose distribution-based optimization. It is a promising approach for realizing automated treatment planning in the future.
In a selected group of patients with small tumor and good operative risk, SILC is a safe alternative to CLC. Single-port laparoscopic colectomy also is associated with the benefits of less postoperative pain and shorter hospital stay than CLC.
The disease-free survival and the local recurrence were significantly worse by the presence of conversion in laparoscopic resection for colorectal malignancy. Adoption of a standardized operative strategy may improve the perioperative outcome after conversion.
In current practice of colorectal surgery, operative factors are more important than patient factors for SSI. Good surgical technique to reduce anastomotic leakage and reduce blood transfusion has paramount importance in SSI prevention. Laparoscopic surgery was associated with reduction of rate of SSI by more than 50% when compared with open surgery and would have a strong impact on the prevention of surgical infection.
Purpose
To develop a deep learning method for prediction of three‐dimensional (3D) voxel‐by‐voxel dose distributions of helical tomotherapy (HT).
Methods
Using previously treated HT plans as training data, a deep learning model named U‐ResNet‐D was trained to predict a 3D dose distribution. First, the contoured structures and dose volumes were converted from plan database to 3D matrix with a program based on a developed visualization toolkit (VTK), then transferred to U‐ResNet‐D for correlating anatomical features and dose distributions at voxel‐level. One hundred and ninety nasopharyngeal cancer (NPC) patients treated by HT with multiple planning target volumes (PTVs) in different prescription patterns were studied. The model was typically trained from scratch with weights randomly initialized rather than using transfer‐learning method, and used to predict new patient's 3D dose distributions. The predictive accuracy was evaluated with three methods: (a) The dose difference at the position r, δ(r, r) = Dc(r) − Dp(r), was calculated for each voxel. The mean (μδ(r,r)) and standard deviation (σδ(r,r)) of δ(r, r) were calculated to assess the prediction bias and precision; (b) The mean absolute differences of dosimetric indexes (DIs) including maximum and mean dose, homogeneity index, conformity index, and dose spillage for PTVs and organ at risks (OARs) were calculated and statistically analyzed with the paired‐samples t test; (c) Dice similarity coefficients (DSC) between predicted and clinical isodose volumes were calculated.
Results
The U‐ResNet‐D model predicted 3D dose distribution accurately. For twenty tested patients, the prediction bias ranged from −2.0% to 2.3% and prediction error varied from 1.5% to 4.5% (relative to prescription) for 3D dose differences. The mean absolute dose differences for PTVs and OARs are within 2.0% and 4.2%, and nearly all the DIs for PTVs and OARs had no significant differences. The averaged DSC ranged from 0.95 to 1 for different isodose volumes.
Conclusions
The study developed a new deep learning method for 3D voxel‐by‐voxel dose prediction, and shown to be able to produce accurately dose predictions for nasopharyngeal patients treated by HT. The predicted 3D dose map can be useful for improving radiotherapy planning design, ensuring plan quality and consistency, making clinical technique comparison, and guiding automatic treatment planning.
Surgical smoke is the gaseous by-product formed during surgical procedures. Most surgeons, operating theatre staff and administrators are unaware of its potential health risks. Surgical smoke is produced by various surgical instruments including those used in electrocautery, lasers, ultrasonic scalpels, high speed drills, burrs and saws. The potential risks include carbon monoxide toxicity to the patient undergoing a laparoscopic operation, pulmonary fibrosis induced by non-viable particles, and transmission of infectious diseases like human papilloma virus. Cytotoxicity and mutagenicity are other concerns. Minimisation of the production of surgical smoke and modification of any evacuation systems are possible solutions. In general, a surgical mask can provide more than 90% protection to exposure to surgical smoke; however, in most circumstances it cannot provide air-tight protection to the user. An at least N95 grade or equivalent respirator offers the best protection against surgical smoke, but whether such protection is necessary is currently unknown.
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