Background: In recent years, with the development of artificial intelligence and deep learning techniques, it has become possible to predict the threedimensional distribution dose (3D 3 ) of a new patient based on the treatment plans of similar recent patients. Therefore, some new questions have arisen for the above issue: how to make use of the predicted 3D 3 obtained from deep learning, to facilitate treatment planning? How to convert the predicted 3D 3 to a clinical deliverable Pareto optimal plan? Little research has been done and limited software has been developed in this regard. Purpose: In the current research, an attempt was made to contribute the knowledge-based planning by presenting a new mathematical model, and to take a novel step towards optimizing the treatment plan derived from both predicted 3D 3 as well as dose prescription to generate a semi-automated clinically applicable optimal IMRT treatment plan. Methods: The presented model has benefited from both prescribed dose as well as predicted dose and its objective function includes both quadratic and linear phrases, so it was called the QuadLin model. The model has been run on the data of 30 patients with head and neck cancer randomly selected from the Open-KBP dataset. There are 19 sets of dose prediction data for each patient in this database. Therefore, a total of 570 problems have been solved in the CVX framework with commercial solver Mosek and the results have been evaluated by two plan quality approaches (1) DVH points differences, and (2) satisfied clinical criteria. Results:The results of the current study indicate a strong significant improvement in almost all plan evaluation indicators compared to the reference plan of the dataset, 3D 3 predictions, as well as the results of previous research, based on the Wilcoxon signed ranks test with a significance level of 0.01. Accordingly, for all regions of interest (ROIs) (or structures) of all 570 problems total clinical indicators have improved by more than 21%, 15%, and at least13%, on average, compared to the predicted dose, the reference plan, and previous research, respectively, with 341 s as the average of solving time. Conclusions: Evaluation of the research results indicates the significant effect of the QuadLin model on improving the dose delivery to the target volumes while reducing the dose and preserving organs at risk. Based on the literature, the proposed model has generated the best-known treatment plan from the predicted 3D 3 so far. K E Y W O R D S clinical applicable planning, CVX framework, head and neck cancer, knowledge-based planning, open KBP dataset, pareto optimality 3148
Background: Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Imaging (MRI) is a beneficial diagnostic tool in treatment planning. Treatment response assessment in glioma patients is usually based on the Response Assessment in Neuro Oncology (RANO) criteria. The limitation of assessment based on RANO is two-dimensional (2D) manual measurements. Deep learning (DL) has great potential in neuro-oncology to improve the accuracy of response assessment. Method: In the current research, firstly, the BraTS 2018 Challenge dataset included 210 HGG and 75 LGG were applied to train a designed U-Net network for automatic tumor and intra-tumoral segmentation, followed by training of the designed classifier with transfer learning for determining grading HGG and LGG. Then, designed networks were employed for the segmentation and classification of local MRI images of 49 glioma patients pre and post-radiotherapy. The results of tumor segmentation and its intra-tumoral regions were utilized to determine the volume of different regions and treatment response assessment. Results: Treatment response assessment demonstrated that radiotherapy is effective on the whole tumor and enhancing region with p-value ≤ 0.05 with a 95% confidence level, while it did not affect necrosis and peri-tumoral edema regions. Conclusion: This work demonstrated the potential of using deep learning in MRI images to provide a beneficial tool in the automated treatment response assessment so that the patient can obtain the best treatment.
Background: Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Imaging (MRI) is a bene cial diagnostic tool in treatment planning. Treatment response assessment in glioma patients is usually based on the Response Assessment in Neuro Oncology (RANO) criteria. The limitation of assessment based on RANO is two-dimensional (2D) manual measurements. Deep learning (DL) has great potential in neuro-oncology to improve the accuracy of response assessment.Method: In the current research, rstly, the BraTS 2018 Challenge dataset included 210 HGG and 75 LGG were applied to train a designed U-Net network for automatic tumor and intra-tumoral segmentation, followed by training of the designed classi er with transfer learning for determining grading HGG andLGG. Then, designed networks were employed for the segmentation and classi cation of local MRI images of 49 glioma patients pre and post-radiotherapy. The results of tumor segmentation and its intratumoral regions were utilized to determine the volume of different regions and treatment response assessment.Results: Treatment response assessment demonstrated that radiotherapy is effective on the whole tumor and enhancing region with p-value ≤ 0.05 with a 95% con dence level, while it did not affect necrosis and peri-tumoral edema regions.Conclusion: This work demonstrated the potential of using deep learning in MRI images to provide a bene cial tool in the automated treatment response assessment so that the patient can obtain the best treatment.
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