Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma.

At present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality. A total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method. A trained deep learning model was employed to generate static field fluence maps for each patient with 3D computed tomography images and structure contours as input. Automatic IMRT treatment planning was achieved by using its generated dose with slight tightening for further plan fine-tuning. Lastly, the plan quality was compared between automatic plans and clinical plans. The average time for automatic plan generation was less than 4 min, including fluence maps prediction with a python script and automated plan tuning with a C# script. Compared with clinical plans, automatic plans showed better conformity and homogeneity for planning target volumes (PTVs) except for the conformity of PTV-1. Meanwhile, the dosimetric metrics for most organs at risk (OARs) were ameliorated in the automatic plan, especially Dmax of the brainstem and spinal cord, and Dmean of the left and right parotid glands significantly decreased (P < 0.05). We have successfully implemented an automatic IMRT plan generation method for patients with NPC. This method shows high planning efficiency and comparable or superior plan quality than clinical plans. The qualitative results before and after the plan fine-tuning indicates that further optimization using dose objectives generated by predicted fluence maps is crucial to obtain high-quality automatic plans.

Cai W ，Ding S ，Li H ，Zhou X ，Dou W ，Zhou L ，Song T ，Li Y ... -  《Radiation Oncology》

Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT.

Current intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively. We proposed a shared encoder network to simultaneously generate dose distribution and fluence maps. The same inputs (three-dimensional contours and CT images) were used for both dose distribution and fluence prediction. The model was trained with datasets of 340 nasopharyngeal carcinoma patients (260 cases for training, 40 cases for validation, 40 cases for testing) treated with nine-beam IMRT. The predicted fluence was then imported back to treatment planning system to generate the final deliverable plan. Predicted fluence accuracy was quantitatively evaluated within projected planning target volumes in beams-eye-view with 5 mm margin. The comparison between predicted doses, predicted fluence generated doses and ground truth doses were also conducted inside patient body. The proposed network successfully predicted similar dose distribution and fluence maps compared with ground truth. The quantitative evaluation showed that the pixel-based mean absolute error between predicted fluence and ground truth fluence was 0.53% ± 0.13%. The structural similarity index also showed high fluence similarity with values of 0.96 ± 0.02. Meanwhile, the difference in the clinical dose indices for most structures between predicted dose, predicted fluence generated dose and ground truth dose were less than 1 Gy. As a comparison, the predicted dose achieved better target dose coverage and dose hot spot than predicted fluence generated dose compared with ground truth dose. We proposed an approach to predict 3D dose distribution and fluence maps simultaneously for nasopharyngeal carcinoma patients. Hence, the proposed method can be potentially integrated in a fast automatic plan generation scheme by using predicted dose as dose objectives and predicted fluence as a warm start.

Li Y ，Cai W ，Xiao F ，Zhou X ，Cai J ，Zhou L ，Dou W ，Song T ... -  《Radiation Oncology》

Dosimetric evaluation of automatic and manual plans for early nasopharyngeal carcinoma to radiotherapy.

To investigate dosimetric differences and plan qualities between manual plans and automatic plans for nasopharyngeal carcinoma (NPC) in early stage, and provide better options to maximize the benefits. Sixteen cases diagnosed with early NPC were retrospectively investigated. Conventional step and shoot IMRT with 7-fields and full arc volumetric-modulated arc therapy (VMAT) with double arcs were manually generated by experienced planners and automatically generated by Auto-Planning module in Pinnacle3 respectively, such as IMRT manual-planning (mIMRT), IMRT auto-planning (aIMRT), VMAT manual-planning (mVMAT), and VMAT auto-planning (aVMAT) for each patient. Target coverage, organs at risk sparing, monitor units, and planning times were compared and evaluated. All parameters of plans are able to fulfill International Commission on Radiation Units and Measurements repor (ICRU) 83 recommendations. Automatic plans are comparable or superior to manual plans without time-consuming planning process. The CI and HI for PTVs are better in aVMAT when compared with aIMRT and mVMAT, but those are similar between aIMRT and mVMAT. Automatic plans not only have superior dose homogeneity and conformity in PTVs, but also have better sparing for spinal cord or slightly reduce the doses received by other OARs, while the VMAT plans have better sparing for brain stem, especially the aVMAT plans. However, Dmax, V30, and V40 of brain stem are similar between aIMRT and mVMAT without significant difference. The monitor units and planning time for treatment plans have been significantly decreased through automatic planning technique. The automatic VMAT plan has greater clinical advantages and should be recommended to a better option for treating NPC in early stage, while automatic IMRT would be preferentially considered instead of manual VMAT.

Zhang Q ，Peng Y ，Song X ，Yu H ，Wang L ，Zhang S ... -  《-》

Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy.

Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel-dose features has not been extensively studied. This study aims to investigate the efficiency of a direct optimization strategy with finite elements (FEs) after DL dose prediction for automatic intensity-modulated radiation therapy (IMRT) treatment planning. A double-UNet DL model was adapted for 220 cervical cancer patients (200 for training and 20 for testing), who underwent IMRT between 2016 and 2020 at our clinic. The model inputs were computed tomography (CT) slices, organs at risk (OARs), and planning target volumes (PTVs), and the outputs were dose distributions of uniformly generated high-dose region-controlled plans. The FEs were discretized into equal intervals of the dose prediction value within the [OARs avoid PTV(O-P)] and [body avoids OARs & PTV(B-OP)] regions in the test cohort and used to define the objectives for IMRT plan optimization. The plans were optimized using a two-step process. In the beginning, the plans of two extra cases with and without low-dose region control were compared to pursue robust and optimal dose adjustment degree pattern of FEs. In the first step, the mean dose of O-P FEs were constrained to differing degrees according to the pattern. The further the FEs from the PTV, the tighter the constraints. In the second step, the mean dose of O-P FEs from first step were constrained again but weakly and the dose of the B-OP FEs from dose prediction and PTV were tightly regulated. The dosimetric parameters of the OARs and PTV were evaluated and compared using an interstep approach. In another 10 cases, the plans optimized via the aforementioned steps (method 1) were compared with those directly generated by the double-UNet dose prediction model trained by low and high region-controlled plans (method 2). The mean differences in dose metrics between the UNet-predicted dose and the clinical plans were: 0.47 Gy for bladder D50% ; 0.62 Gy for rectum D50% ; 0% for small intestine V30Gy ; 1% for small intestine V40Gy ; 4% for left femoral head V30Gy ; and 6% for right femoral head V30Gy . The reductions in mean dose (p < 0.001) after FE-based optimization were: 4.0, 1.9, 2.8, 5.9, and 5.7 Gy for the bladder, rectum, small intestine, left femoral head, and right femoral head, respectively, with flat PTV homogeneity and conformity. Method 1 plans produced lower mean doses than those of method 2 for the bladder (0.7 Gy), rectum (1.0 Gy), and small intestine (0.6 Gy), while maintaining  PTV homogeneity and conformity. FE-based direct optimization produced lower OAR doses and adequate PTV doses after DL prediction. This solution offers rapid and automatic plan optimization without manual adjustment, particularly in low-dose regions.

Shen Y ，Tang X ，Lin S ，Jin X ，Ding J ，Shao M ... -  《-》

A hybrid optimization strategy for deliverable intensity-modulated radiotherapy plan generation using deep learning-based dose prediction.

To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy, including dose prediction via a deep learning and voxel-based optimization strategy. The dose distribution of patients was predicted using a U-Net-based deep learning network based on the patient's anatomy information. One hundred seventeen patients with nasopharyngeal cancer (NPC) and 200 patients with rectal cancer were enrolled in this study. For NPC cases, 94 cases were included in the training dataset, 13 in the validation dataset, and 10 in the testing dataset. For rectal cancer cases, 172 cases were included in the training set, 18 in the validation set, and 10 in the testing set. A voxel-based optimization strategy, "Voxel," was proposed to achieve treatment planning optimization by dividing body voxels into two parts: inside planning target volumes (PTVs) and outside PTVs. Fixed dose-volume objectives were attached to the total objective function to realize individualized planning intended as the "hybrid" optimizing strategy. Automatically generated plans were compared with clinically approved plans to evaluate clinical gains, according to dosimetric indices and dose-volume histograms (DVHs). Similarities were found between the DVH of the predicted dose and clinical plan, although significant differences were found in some organs at risk. Better organ sparing and suboptimal PTV coverage were shown using the voxel strategy; however, the deviations in homogeneity indices (HIs) and conformity indices (CIs) of the PTV between automatically generated plans and manual plans were reduced by the hybrid strategy ([manual plans]/[voxel plans[/[hybrid plans]: HI of PTV70 [1.06/1.12/1.02] and CI of PTV70 [0.79/0.58/0.76]). The optimization time for each patient was within 1 min and included fluence map optimization, leaf sequencing, and control point optimization. All the generated plans (voxel and hybrid strategy) could be delivered on uRT-linac 506c (United Imaging Healthcare, Shanghai, China). Deliverable plans can be generated by incorporating a voxel-based optimization strategy into a commercial treatment planning system (TPS). The hybrid optimization method shows the benefit and clinical feasibility in generating clinically acceptable plans.

Sun Z ，Xia X ，Fan J ，Zhao J ，Zhang K ，Wang J ，Hu W ... -  《-》