Comparison of dose metrics between automated and manual radiotherapy planning for advanced stage non-small cell lung cancer with volumetric modulated arc therapy

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

A pilot study of machine-learning based automated planning for primary brain tumours

Purpose

High-quality radiotherapy (RT) planning for children and young adults with primary brain tumours is essential to minimize the risk of late treatment effects. The feasibility of using automated machine-learning (ML) to aid RT planning in this population has not previously been studied.

Methods and materials

We developed a ML model that identifies learned relationships between image features and expected dose in a training set of 95 patients with a primary brain tumour treated with focal radiotherapy to a dose of 54 Gy in 30 fractions. This ML method was then used to create predicted dose distributions for 15 previously-treated brain tumour patients across two institutions, as a testing set. Dosimetry to target volumes and organs-at-risk (OARs) were compared between the clinically-delivered (human-generated) plans versus the ML plans.

Results

The ML method was able to create deliverable plans in all 15 patients in the testing set. All ML plans were generated within 30 min of initiating planning. Planning target volume coverage with 95% of the prescription dose was attained in all plans. OAR doses were similar across most structures evaluated; mean doses to brain and left temporal lobe were lower in ML plans than manual plans (mean difference to left temporal, – 2.3 Gy, p = 0.006; mean differences to brain, – 1.3 Gy, p = 0.017), whereas mean doses to right cochlea and lenses were higher in ML plans (+ 1.6–2.2 Gy, p < 0.05 for each).

Conclusions

Use of an automated ML method to aid RT planning for children and young adults with primary brain tumours is dosimetrically feasible and can be successfully used to create high-quality 54 Gy RT plans. Further evaluation after clinical implementation is planned.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01967-3.

Evaluation of Automated Treatment Planning and Organ Dose Prediction for Lung Stereotactic Body Radiotherapy

PURPOSES

To evaluate whether the auto-planning (AP) module can achieve clinically acceptable treatment plans for lung stereotactic body radiotherapy (SBRT) and to evaluate the effectiveness of a dose prediction model.

METHODS

Twenty lung SBRT cases planned manually with 50 Gy in five fractions were replanned using the Pinnacle (Philips Radiation Oncology Systems, Fitchburg, WI) AP module according to the dose constraint tables from the Radiation Therapy Oncology Group (RTOG) 0813 protocol. Doses to the organs at risk (OAR) were compared between the manual and AP plans. Using a dose prediction model from a commercial product, PlanIQ (Sun Nuclear Corporation, Melbourne, FL), we also compared OAR doses from AP plans with predicted doses.

RESULTS

All manual and AP plans achieved clinically required dose coverage to the target volumes. The AP plans achieved equal or better OAR sparing when compared to the manual plans, most noticeable in the maximum doses of the spinal cord, ipsilateral brachial plexus, esophagus, and trachea. Predicted doses to the heart, esophagus, and trachea were highly correlated with the doses of these OARs from the AP plans with the highest correlation coefficient of 0.911, 0.823, and 0.803, respectively.

CONCLUSION

Auto-planning for lung SBRT improved OAR sparing while keeping the same dose coverage to the tumor. The dose prediction model can provide useful planning dose guidance.

Algorithm for an automatic treatment planning system using a single-arc VMAT for prostate cancer

Optimization process in treatment planning for intensity‐modulated radiation therapy varies with the treatment planner. Therefore, a large variation in the quality of dose distribution is usually observed. To reduce variation, an automatic optimizing toolkit was developed for the Monaco treatment planning system (Elekta AB, Stockholm, Sweden) for prostate cancer using volumetric‐modulated arc therapy (VMAT). This toolkit was able to create plans automatically. However, most plans needed two arcs per treatment to ensure the dose coverage for targets. For prostate cancer, providing a plan with a single arc was advisable in clinical practice because intrafraction motion management must be considered to irradiate accurately. The purpose of this work was to develop an automatic treatment planning system with a single arc per treatment for prostate cancer using VMAT. We designed the new algorithm for the automatic treatment planning system to use one arc per treatment for prostate cancer in Monaco. We constructed the system in two main steps: (1) Determine suitable cost function parameters for each case before optimization, and (2) repeat the calculation and optimization until the conditions for dose indices are fulfilled. To evaluate clinical suitability, the plan quality between manual planning and the automatic planning system was compared. Our system created the plans automatically in all patients within a few iterations. Statistical differences between the plans were not observed for the target and organ at risk. It created the plans with no human input other than the initial template setting and system initiation. This system offers improved efficiency in running the treatment planning system and human resources while ensuring high‐quality outputs.

Characterization of automatic treatment planning approaches in radiotherapy

•

Auto-Planning is widely used, yet creation of high quality treatment plans remains challenging.

•

Systematic investigation of behavior and optimal use of Auto-Planning.

•

Widely applicable solutions to create optimal plans.

•

Auto-Planning outperforms manual plans in DVH metrics and blind comparisons.

Background and purpose

Automatic approaches are widely implemented to automate dose optimization in radiotherapy treatment planning. This study systematically investigates how to configure automatic planning in order to create the best possible plans.

Materials and methods

Automatic plans were generated using protocol based automatic iterative optimization. Starting from a simple automation protocol which consisted of the constraints for targets and organs at risk (OAR), the performance of the automatic approach was evaluated in terms of target coverage, OAR sparing, conformity, beam complexity, and plan quality. More complex protocols were systematically explored to improve the quality of the automatic plans. The protocols could be improved by adding a dose goal on the outer 2 mm of the PTV, by setting goals on strategically chosen subparts of OARs, by adding goals for conformity, and by limiting the leaf motion. For prostate plans, development of an automated post-optimization procedure was required to achieve precise control over the dose distribution. Automatic and manually optimized plans were compared for 20 head and neck (H&N), 20 prostate, and 20 rectum cancer patients.

Results

Based on simple automation protocols, the automatic optimizer was not always able to generate adequate treatment plans. For the improved final configurations for the three sites, the dose was lower in automatic plans compared to the manual plans in 12 out of 13 considered OARs. In blind tests, the automatic plans were preferred in 80% of cases.

Conclusions

With adequate, advanced, protocols the automatic planning approach is able to create high-quality treatment plans.

On the optimal number of dose-limiting shells in the SBRT auto-planning design for peripheral lung cancer

PURPOSE

The number of dose-limiting shells in the optimization process is one of the key factors determining the quality of stereotactic body radiotherapy (SBRT) auto-planning in the Pinnacle treatment planning system (TPS). This study attempted to derive the optimal number of shells by evaluating the auto-plans designed with different number of shells for peripheral lung cancer patients treated with SBRT.

METHODS

Identical treatment technique, optimization process, constraints, and dose calculation algorithm in the Pinnacle TPS were retrospectively applied to 50 peripheral lung cancer patients who underwent SBRT in our center. For each of the patients, auto-plans were optimized based on two shells, three shells, four shells, five shells, six shells, seven shells, eight shells, respectively. The optimal number of shells for the SBRT auto-planning was derived through the evaluations and comparisons of various dosimetric parameters of planning target volume (PTV) and organs at risk (OARs), monitor units (MU), and optimization time of the plans.

RESULTS

The conformity index (CI) and the gradient index (GI) of PTV, the maximum dose outside the 2 cm of PTV (D2cm ), Dmax of spinal cord (SCmax ), the percentage of volume of total lung excluding ITV receiving 20 Gy (V20) and 10 Gy (V10), and the mean lung dose (MLD) were improved when the number of shell increased, but the improvement became not significant as the number of shell reached six. The monitor units (MUs) varied little among different plans where no statistical differences were found. However, as the number of shell increased, the auto-plan optimization time increased significantly.

CONCLUSIONS

It appears that for peripheral lung SBRT plan using six shells can yield satisfactory plan quality with acceptable beam MUs and optimization time in the Pinnacle TPS.

Evaluation of automatic VMAT plans in locally advanced nasopharyngeal carcinoma

OBJECTIVE

This study aimed to evaluate the quality of locally advanced nasopharyngeal carcinoma (NPC) radiotherapy plans generated by the automated planning module of a commercial treatment planning system (TPS).

METHODS

Data of 30 patients with locally advanced NPC were retrospectively investigated. For each patient, volumetric modulated arc therapy (VMAT) plans with double arcs were generated manually by experienced physicists and automatically in the Pinnacle³ Auto-Planning module (Philips Medical Systems, Fitchburg, WI, USA). The anatomic distance between the second clinical target volume (CTV₂) and the pons of the brainstem, and the T category of disease were factored into the evaluation. Dosimetric verification was evaluated in terms of gamma pass rate. Target coverage, sparing of organs at risk (OARs), and monitor units were evaluated and compared between the manual and automatic VMAT plans.

RESULTS

Not all treatment plans fully met the dose objectives for planning target volumes (PTVs) and OARs, particularly in T4 patients. Overall, automatic VMAT provides a comparable or superior plan quality to manual VMAT in most cases. In stratified analysis, plan quality is mainly independent on T category but is also affected by anatomic distance. If the anatomic distance is less than 5 mm, the automatic VMAT plan quality is equal or even inferior to manual VMAT performed by experienced physicists. Conversely, if the anatomic distance is greater than 5 mm, the automatic VMAT plan quality is superior to manual VMAT. Gamma pass rates for quality assurance are similar between manual and automatic VMAT plans for the former case, but significantly higher in automatic VMAT for the latter.

CONCLUSION

The selection of manual versus automatic VMAT planning in locally advanced NPC should be made individually based on the anatomic distance, rather than blindly and habitually, since automatic VMAT is not good enough to completely replace manual VMAT.

Evaluation of auto-planning in IMRT and VMAT for head and neck cancer

PURPOSE

The purposes of this work are to (a) investigate whether the use of auto-planning and multiple iterations improves quality of head and neck (HN) radiotherapy plans; (b) determine whether delivery methods such as step-and-shoot (SS) and volumetric modulated arc therapy (VMAT) impact plan quality; (c) report on the observations of plan quality predictions of a commercial feasibility tool.

MATERIALS AND METHODS

Twenty HN cases were retrospectively selected from our clinical database for this study. The first ten plans were used to test setting up planning goals and other optimization parameters in the auto-planning module. Subsequently, the other ten plans were replanned with auto-planning using step-and-shoot (AP-SS) and VMAT (AP-VMAT) delivery methods. Dosimetric endpoints were compared between the clinical plans and the corresponding AP-SS and AP-VMAT plans. Finally, predicted dosimetric endpoints from a commercial program were assessed.

RESULTS

All AP-SS and AP-VMAT plans met the clinical dose constraints. With auto-planning, the dose coverage of the low dose planning target volume (PTV) was improved while the dose coverage of the high dose PTV was maintained. Compared to the clinical plans, the doses to critical organs, such as the brainstem, parotid, larynx, esophagus, and oral cavity were significantly reduced in the AP-VMAT (P < 0.05); the AP-SS plans had similar homogeneity indices (HI) and conformality indices (CI) and the AP-VMAT plans had comparable HI and improved CI. Good agreement in dosimetric endpoints between predictions and AP-VMAT plans were observed in five of seven critical organs.

CONCLUSION

With improved planning quality and efficiency, auto-planning module is an effective tool to enable planners to generate HN IMRT plans that are meeting institution specific planning protocols. DVH prediction is feasible in improving workflow and plan quality.