Multicriteria optimization of the spatial dose distribution

Purpose To develop and test a three‐dimensional (3D) deep learning model for predicting 3D voxel‐wise dose distributions for intensity‐modulated radiotherapy (IMRT). Methods A total of 122 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 100 cases were randomly selected as the training–validating set and the remaining as the testing set. A 3D deep learning model named 3D U‐Res‐Net_B was constructed to predict 3D dose distributions. Eight types of 3D matrices from CT images, contoured structures, and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: (a) The dice similarity coefficients (DSCs) of different isodose volumes, the average dose difference of all voxels within the body, and 3%/5 mm global gamma passing rates of organs at risks (OARs) and planned target volume (PTV) were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; (b) The dosimetric index (DI) including homogeneity index, conformity index, V50, V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired‐samples t test. The model was also compared with 3D U‐Net and the same architecture model without beam configurations input (named as 3D U‐Res‐Net_O). Results The 3D U‐Res‐Net_B model predicted 3D dose distributions accurately. For the 22 testing cases, the average prediction bias ranged from −1.94% to 1.58%, and the overall mean absolute errors (MAEs) was 3.92 ± 4.16%; there was no statistically significant difference for nearly all DIs. The model had a DSCs value above 0.9 for most isodose volumes, and global 3D gamma passing rates varying from 0.81 to 0.90 for PTV and OARs, clearly outperforming 3D U‐Res‐Net_O and being slightly superior to 3D U‐Net. Conclusions This study developed a more general deep learning model by considering beam configurations input and achieved an accurate 3D voxel‐wise dose prediction for rectal cancer treated by IMRT, a potentially easier clinical implementation for more comprehensive automatic planning.

Section: Introductionmentioning

confidence: 99%

A method of using deep learning to predict three‐dimensional dose distributions for intensity‐modulated radiotherapy of rectal cancer

Zhou

Zhao

Song

et al. 2020

“…However, we cannot state with absolute certainty that the treatment planning approach or the quality of the best‐ranked treatment plan in this study (i.e., case 10) is the best achievable for this patient. Further improvements to robotic radiosurgery optimization of spine SBRT have already been demonstrated 38 , 39 , 40 and the new InCise (Accuray Inc.) multileaf collimator for the CyberKnife may further improve plan quality. (41) Nevertheless, our study demonstrates that a best practice approach to CyberKnife treatment planning is feasible, and further cases and a measure for treatment planning quality improvement are under investigation.…”

Section: Discussionmentioning

confidence: 99%

Inverse treatment planning for spinal robotic radiosurgery: an international multi‐institutional benchmark trial

Blanck

Wang

Baus

et al. 2016

Stereotactic radiosurgery (SRS) is the accurate, conformal delivery of high‐dose radiation to well‐defined targets while minimizing normal structure doses via steep dose gradients. While inverse treatment planning (ITP) with computerized optimization algorithms are routine, many aspects of the planning process remain user‐dependent. We performed an international, multi‐institutional benchmark trial to study planning variability and to analyze preferable ITP practice for spinal robotic radiosurgery. 10 SRS treatment plans were generated for a complex‐shaped spinal metastasis with 21 Gy in 3 fractions and tight constraints for spinal cord (normalV14Gy<2 cc, normalV18Gy<0.1 cc) and target (coverage >95%). The resulting plans were rated on a scale from 1 to 4 (excellent‐poor) in five categories (constraint compliance, optimization goals, low‐dose regions, ITP complexity, and clinical acceptability) by a blinded review panel. Additionally, the plans were mathematically rated based on plan indices (critical structure and target doses, conformity, monitor units, normal tissue complication probability, and treatment time) and compared to the human rankings. The treatment plans and the reviewers' rankings varied substantially among the participating centers. The average mean overall rank was 2.4 (1.2‐4.0) and 8/10 plans were rated excellent in at least one category by at least one reviewer. The mathematical rankings agreed with the mean overall human rankings in 9/10 cases pointing toward the possibility for sole mathematical plan quality comparison. The final rankings revealed that a plan with a well‐balanced trade‐off among all planning objectives was preferred for treatment by most participants, reviewers, and the mathematical ranking system. Furthermore, this plan was generated with simple planning techniques. Our multi‐institutional planning study found wide variability in ITP approaches for spinal robotic radiosurgery. The participants', reviewers', and mathematical match on preferable treatment plans and ITP techniques indicate that agreement on treatment planning and plan quality can be reached for spinal robotic radiosurgery.PACS number(s): 87.55.de

“…However, it is noted that a “warm‐start” is not necessary to find the optimal plan. Our calculation proceeds in analogous to the planning process of a human planner, with the resultant dose distribution assessed by a decision function each time after the Eclipse optimization is done (35) . The algorithm is formally described by the following equations:

\begin{matrix} right left right left right left right left right left right left3pt0em 2em 0em 2em 0em 2em 0em 2em 0em 2em 0emtrue \\ o p t T P S o b j e c t i v e f u n c t i o n w i t h c o n s t r a i n t s \\ s . t . | d_{σ j} - d_{σ j}^{r e f} | < ε_{σ} (σ \in 1, 2, \dots, N; j \in 1, 2, \dots, J) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Application programming in C# environment with recorded user software interactions and its application in autopilot of VMAT/IMRT treatment planning

Wang

Liu

2016

An autopilot scheme of volumetric‐modulated arc therapy (VMAT)/intensity‐modulated radiation therapy (IMRT) planning with the guidance of prior knowledge is established with recorded interactions between a planner and a commercial treatment planning system (TPS). Microsoft (MS) Visual Studio Coded UI is applied to record some common planner‐TPS interactions as subroutines. The TPS used in this study is a Windows‐based Eclipse system. The interactions of our application program with Eclipse TPS are realized through a series of subroutines obtained by prerecording the mouse clicks or keyboard strokes of a planner in operating the TPS. A strategy to autopilot Eclipse VMAT/IMRT plan selection process is developed as a specific example of the proposed “scripting” method. The autopiloted planning is navigated by a decision function constructed with a reference plan that has the same prescription and similar anatomy with the case at hand. The calculation proceeds by alternating between the Eclipse optimization and the outer‐loop optimization independent of the Eclipse. In the C# program, the dosimetric characteristics of a reference treatment plan are used to assess and modify the Eclipse planning parameters and to guide the search for a clinically sensible treatment plan. The approach is applied to plan a head and neck (HN) VMAT case and a prostate IMRT case. Our study demonstrated the feasibility of application programming method in C# environment with recorded interactions of planner‐TPS. The process mimics a planner's planning process and automatically provides clinically sensible treatment plans that would otherwise require a large amount of manual trial and error of a planner. The proposed technique enables us to harness a commercial TPS by application programming via the use of recorded human computer interactions and provides an effective tool to greatly facilitate the treatment planning process.PACS number(s): 87.55.D‐, 87.55.kd, 87.55.de