Multi-criteria optimization achieves superior normal tissue sparing in a planning study of intensity-modulated radiation therapy for RTOG 1308-eligible non-small cell lung cancer patients

An assessment of the influence of trade-off optimization in commercial knowledge based planning library creation for tongue cancer patients

This article aims to compare the dosimetric performance between knowledge-based plan (KBP) libraries with and without trade-off (TO) exploration using multicriterial optimization (MCO) for tongue cancer patients. The trade-off optimized library (KBP_MCO) contains a minimal number of constituent plans, whereas two nontrade-off optimized libraries contain a minimal and a large number of treatment plans, respectively. Three KBP libraries were created: KBP_100 and KBP_20, each comprising of 100 and 20 manually optimized plans, respectively. Additionally, another KBP library (KBP_MCO_20) was created by reoptimizing the constituent plans from KBP_20 using MCO techniques. A total of 70 tongue plans were validated through these libraries. Validation plans were evaluated for PTV and organ at risk (OAR) doses. Greenhouse-Geisser analysis (ANOVA) and the Bonferroni procedure (t-test) were used for statistical evaluation. The mean PTVD95% for KBP_100, KBP_20, and KBP_MCO_20 was 98.4% ± 0.3%, 98.9% ± 0.2%, and 98.7% ± 0.2%, respectively. The statistical significance of PTVD95% for the 3 possible combinations-KBP_100 vs KBP_20, KBP_100 vs KBP_MCO_20, and KBP_20 vs KBP_MCO_20 were statistically significant with p < 0.001. Spinal cord doses for KBP_100, KBP_20, and KBP_MCO_20 were 29.6 ± 1.8 Gy, 31.2 ± 2.5 Gy, and 26.8 ± 1.9 Gy, respectively, with p(KBP_100 vs KBP_20) = 0.14, p(KBP_100 vs KBP_MCO_20) = 0.001, and p(KBP_20 vs KBP_MCO_20) < 0.001. Only the first comparison showed a statistically insignificant variation. A trade-off optimized plan library with a minimal number of patients (20) yields better performance for serial structures (spinal cord and brainstem) compared to large manually optimized KBP libraries. For other organs at risk (OARs) and target dose coverage, although statistical differences were significant in most instances, the differences in physical dose were small and probably will not yield any significant clinical differences.

Multicriteria optimization achieves superior normal tissue sparing in volumetric modulated arc therapy for gastric cancer

OBJECTIVE

To evaluate the benefits of volumetric modulated arc therapy (VMAT) based on multicriteria optimization (MCO) for gastric cancer patients, particularly the protection of serial organs at risk (OARs) that overlap with the target volume.

METHODS

MCO and single-criterion optimization (SCO) VMAT plans were conducted among 30 gastric cancer patients, with a prescription dose of 50.4 Gy delivered in 28 fractions. All treatment plans underwent review, and a comparison was made between the active planning time and different dose-volume parameters.

RESULTS

Both the MCO and SCO VMAT plans achieved the target dose coverage, with no significant difference in the conformity index (CI) for the planning target volume (PTV), at median CI values of 0.887 and 0.891, respectively (P = 0.417). The MCO plans showed a slight but significant increase in the homogeneity index of the PTV, with a median increase of 0.029 (P < 0.001). Additionally, the MCO plans resulted in a lower D2% to the small intestine and duodenum, with reductions of 3.43 Gy and 0.3 Gy, respectively (P < 0.05). Furthermore, the Dmax to the small intestine correlated moderately with the overlapping volume between the small intestine and the target volume (ρ = 0.42, P = 0.023). Except for the mean dose to the liver, the MCO plans performed better in terms of dose indicators for other OARs. Moreover, compared to the SCO plans, the median active planning time in the MCO plans was significantly reduced by 53.2 min (P < 0.0001).

CONCLUSIONS

MCO can effectively help the physicians to quickly select an optimal treatment plan for patients with gastric cancer. It has been shown that MCO VMAT plans can significantly reduce the dose to OARs and shorten the active planning time, with acceptable target coverage. In addition, these plans take less dosimetric time, thereby streamlining the treatment planning process.

Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer

PURPOSE

The purpose of the study is to investigate the clinical application of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer.

METHODS

A DL model was developed for predicting patient-specific doses, trained and validated on a dataset of 235 patients with diverse target volumes and prescriptions. The model was integrated into clinical workflow with DL-predicted objective functions. The automatic plans were retrospectively designed for additional 50 treated manual volumetric modulated arc therapy (VMAT) plans. A comparison was made between automatic and manual plans in terms of dosimetric indexes, monitor units (MUs) and planning time. Plan quality metric (PQM) encompassing these indexes was evaluated, with higher PQM values indicating superior plan quality. Qualitative evaluations of two plans were conducted by four reviewers.

RESULTS

The PQM score was 40.7 ± 13.1 for manual plans and 40.8 ± 13.5 for automatic plans (P = 0.75). Compared to manual plans, the targets coverage and homogeneity of automatic plans demonstrated no significant difference. Manual plans exhibited better sparing for lung in V5 (difference: 1.8 ± 4.2 %, P = 0.02), whereas automatic plans showed enhanced sparing for heart in V30 (difference: 1.4 ± 4.7 %, P = 0.02) and for spinal cord in Dmax (difference: 0.7 ± 4.7 Gy, P = 0.04). The planning time and MUs of automatic plans were significantly reduced by 70.5 ± 20.0 min and 97.4 ± 82.1. Automatic plans were deemed acceptable in 88 % of the reviews (176/200).

CONCLUSIONS

The DL-assisted approach for lung cancer notably decreased planning time and MUs, while demonstrating comparable or superior quality relative to manual plans. It has the potential to provide benefit to lung cancer patients.

Multicriteria optimization of radiation therapy: Towards empowerment and standardization of reverse planning for head and neck squamous cell carcinoma

PURPOSE

The purpose of this study was to assess if multicriteria optimization could limit interoperator variability in radiation therapy planning and assess if this method could contribute to target volume coverage and sparing of organ at risk for intensity-modulated curative radiation therapy of head and neck cancers.

MATERIAL AND METHODS

We performed a retrospective analysis on 20 patients treated for an oropharyngeal or oral cavity squamous cell carcinoma. We carried out a comparative dosimetric study of manual plans produced with Precision® software, compared with the plans proposed using the multicriteria optimization method (RayStation®). We assessed interoperator reproducibility on the first six patients, and dosimetric contribution in sparing organs at risk using the multicriteria optimization method.

RESULTS

Median age was 69 years, most lesions were oropharyngeal carcinoma (65%), and 35% lesions were stage T3. First, we obtained a high degree of similarity between the four operator measurements for each patient at the level of each organ. Intraclass correlation coefficients were greater than 0.85. Second, we observed a significant dosimetric benefit for contralateral parotid gland, homolateral and contralateral masseter muscles, homolateral and contralateral pterygoid muscles and for the larynx (P<0.05). For the contralateral parotid gland, the mean dose difference between the multicriteria optimization and manual plans was -2.0Gy (P=0.01). Regarding the larynx, the mean dose difference between the two plans was -4.6Gy (P<0.001).

CONCLUSION

Multicriteria optimization is a reproducible technique and faster than manual optimization. It allows dosimetric advantages on organs at risk, especially for those not usually taken into consideration in manual dosimetry. This may lead to improved quality of life.

Dosimetric comparison of hippocampal-sparing technologies in patients with low-grade glioma

Background

Radiotherapy (RT) plays an integral role in the management of low-grade gliomas (LGG). Late toxicity from RT can cause progressive neurocognitive dysfunction. Radiation-induced damage to the hippocampus (HCP) plays a considerable role in memory decline. Advancements in photon planning software have resulted in the development of multi-criteria optimization (MCO) and HyperArc technologies which may improve HCP sparing while maintaining planning target volume (PTV) target coverage.

Methods

Three planning methods for hippocampal sparing (HS) were compared, volumetric modulated arc therapy (VMAT) without HS (VMAT_noHS), VMAT with HS (VMAT_HS), MCO with HS (MCO_HS), and HyperArc with HS (HyperArc_HS).

Results

Twenty-five patients were identified. The contralateral HCP was spared in 16 patients and bilateral HCP in 9 patients with superiorly located tumors. All 3 HS planning techniques showed significant reductions in dose to the spared HCP in contralateral cases but only VMAT_HS and MCO_HS achieved this in bilateral cases (P < .008). Only MCO_HS was superior to VMAT_HS in lowering the dose to both contralateral HCP and bilateral HCP in all measured metrics (P < .008). PTV and OAR (organ at risk) dose constraints were achieved for all plans.

Conclusions

This retrospective dosimetric study demonstrated the feasibility of HS for low-grade glioma. All 3 HS planning techniques achieved significant dose reductions to the spared contralateral hippocampus, but only MCO_HS and VMAT_HS achieved this in bilateral cases. MCO was superior to other planning techniques for sparing both bilateral and contralateral hippocampi.

Influence of optimisation parameters on directly deliverable Pareto fronts explored for prostate cancer

PURPOSE

In inverse radiotherapy treatment planning, the Pareto front is the set of optimal solutions to the multi-criteria problem of adequately irradiating the planning target volume (PTV) while reducing dose to organs at risk (OAR). The Pareto front depends on the chosen optimisation parameters whose influence (clinically relevant versus not clinically relevant) is investigated in this paper.

METHODS

Thirty-one prostate cancer patients treated at our clinic were randomly selected. We developed an in-house Python script that controlled the commercial treatment planning system RayStation to calculate directly deliverable Pareto fronts. We calculated reference Pareto fronts for a given set of objective functions, varying the PTV coverage and the mean dose of the primary OAR (rectum) and fixing the mean doses of the secondary OARs (bladder and femoral heads). We calculated the fronts for different sets of objective functions and different mean doses to secondary OARs. We compared all fronts using a specific metric (clinical distance measure).

RESULTS

The in-house script was validated for directly deliverable Pareto front calculations in two and three dimensions. The Pareto fronts depended on the choice of objective functions and fixed mean doses to secondary OARs, whereas the parameters most influencing the front and leading to clinically relevant differences were the dose gradient around the PTV, the weight of the PTV objective function, and the bladder mean dose.

CONCLUSIONS

Our study suggests that for multi-criteria optimisation of prostate treatments using external therapy, dose gradient around the PTV and bladder mean dose are the most influencial parameters.

Data-Driven Dose-Volume Histogram Prediction

Purpose

To evaluate dose-volume histogram (DVH) prediction from prior radiation therapy data.

Methods and Materials

An Oncospace radiation therapy database was constructed including images, structures, and dose distributions for patients with advanced lung cancer. DVH data was queried for total lungs, esophagus, heart, and external body contours. Each query returned DVH data for the N-most similar organs at risk (OARs) based on OAR-to-planning-target-volume (PTV) geometry via the overlap volume histogram (OVH). The DVHs for 5, 20, and 50 of the most similar OVHs were returned for each OAR for each patient. The OVH(0cm) is the relative volume of the OAR overlapping with the PTV, and the OVH(2cm) is the relative volume of the OAR 2 cm away from the PTV. The OVH(cm) and DVH(%) queried from the database were separated into interquartile ranges (IQRs), nonoutlier ranges (NORs) (equal to 3 × IQR), and the average database DVH (DVH-DB) computed from the NOR data. The ability to predict the clinically delivered DVH was evaluated based on percentiles and differences between the DVH-DB and the clinical DVH (DVH-CL) for a varying number of returned patient DVHs for a subset of patients.

Results

The ability to predict the clinically delivered DVH was excellent in the lungs and body; the IQR and NOR were <4% and <16%, respectively, in the lungs and <1% and <5%, respectively, in the body at all distances less than 2 cm from the PTV. For 21/23 patients considered, the differences in lung DVH-DB and DVH-CL were <4.6% and in 14/23 cases, <3%. In esophagus and heart, the ability to predict DVH-CL was weaker, with mean DVH differences >10% for 12/23 esophagi and 10/23 hearts. In esophagus and heart queries, the NOR was often 10% to 100% volume in dose ranges between 0% and 50% of prescription, independent of the number of patients queried.

Conclusions

Using prior data to predict clinical dosimetry is increasingly of interest, but model- and data-driven methods have limitations if based on limited data sets. This study's results showed that prediction may be reasonable in organs containing tumors with known overlap, but for nonoverlapped OARs, planning preference and plan design may dominate the clinical dose.

Implementation of automatic plan optimization in Italy: Status and perspectives

PURPOSE

To investigate and report on the diffusion and clinical use of automated radiotherapy planning systems in Italy and to assess the perspectives of the community of Italian medical physicists involved in radiotherapy on the use of these tools.

MATERIALS AND METHODS

A survey of medical physicists (one per Institute) of 175 radiotherapy centers in Italy was conducted between February 21st and April 1st, 2021. The information collected included the institute's characteristics, plan activity, availability/use of automatic tools and related issues regarding satisfaction, criticisms, expectations, and perceived professional modifications. Responses were analysed, including the impact of a few variables such as the institute type and experience.

RESULTS

125 of the centers (71%) answered the survey, with regional variability (range: 47%-100%); among these, 49% have a TPS with some automatic option. Clinical use of automatic planning is present in 33% of the centers, with 13% applying it in >50% of their plans. Among the 125 responding centres the most used systems are Pinnacle (16%), Raystation (9%) and Eclipse (4%). The majority of participants consider the use of automated techniques to be beneficial, while only 1% do not see any advantage; 83% of respondents see the possibility of enriching their professional role as a potential benefit, while 3% see potential threats.

CONCLUSIONS

Our survey shows that 49% of the responding centres have an automatic planning solution although clinically used in only 33% of the cases. Most physicists consider the use of automated techniques to be beneficial and show a prevalently positive attitude.

Enhancing Radiotherapy for Locally Advanced Non-Small Cell Lung Cancer Patients with iCE, a Novel System for Automated Multi-Criterial Treatment Planning Including Beam Angle Optimization

In this study, the novel iCE radiotherapy treatment planning system (TPS) for automated multi-criterial planning with integrated beam angle optimization (BAO) was developed, and applied to optimize organ at risk (OAR) sparing and systematically investigate the impact of beam angles on radiotherapy dose in locally advanced non-small cell lung cancer (LA-NSCLC). iCE consists of an in-house, sophisticated multi-criterial optimizer with integrated BAO, coupled to a broadly used commercial TPS. The in-house optimizer performs fluence map optimization to automatically generate an intensity-modulated radiotherapy (IMRT) plan with optimal beam angles for each patient. The obtained angles and dose-volume histograms are then used to automatically generate the final deliverable plan with the commercial TPS. For the majority of 26 LA-NSCLC patients, iCE achieved improved heart and esophagus sparing compared to the manually created clinical plans, with significant reductions in the median heart D_mean (8.1 vs. 9.0 Gy, p = 0.02) and esophagus D_mean (18.5 vs. 20.3 Gy, p = 0.02), and reductions of up to 6.7 Gy and 5.8 Gy for individual patients. iCE was superior to automated planning using manually selected beam angles. Differences in the OAR doses of iCE plans with 6 beams compared to 4 and 8 beams were statistically significant overall, but highly patient-specific. In conclusion, automated planning with integrated BAO can further enhance and individualize radiotherapy for LA-NSCLC.

Assessment of a Contralateral Esophagus-Sparing Technique in Locally Advanced Lung Cancer Treated With High-Dose Chemoradiation: A Phase 1 Nonrandomized Clinical Trial

Importance

Severe acute esophagitis occurs in up to 20% of patients with locally advanced lung cancer treated with chemoradiation therapy to at least 60 Gy once daily and represents a dose-limiting toxic event associated with poor outcomes.

Objective

To assess whether formalized sparing of the contralateral esophagus (CE) is associated with reduced risk of severe acute esophagitis.

Design, Setting, and Participants

This single-center phase 1 nonrandomized clinical trial assessing an empirical CE-sparing technique enrolled patients from July 2015 to January 2019. In total, 27 patients with locally advanced non-small cell lung carcinoma (with or without solitary brain metastasis) or limited-stage small cell lung carcinoma with gross tumor within 1 cm of the esophagus were eligible.

Interventions

Intensity-modulated radiation therapy to 70 Gy at 2 Gy/fraction concurrent with standard chemotherapy with or without adjuvant durvalumab. The esophageal wall contralateral to gross tumor was contoured as an avoidance structure to guide a steep dose falloff gradient. Target coverage was prioritized over CE sparing, and 99% of internal and planning target volumes had to be covered by 70 Gy and at least 63 Gy, respectively.

Main Outcomes and Measures

The primary end point was the rate of at least grade 3 acute esophagitis as assessed by Common Terminology Criteria for Adverse Events, version 4.

Results

Of 27 patients enrolled, 25 completed chemoradiation therapy. Nineteen patients had non-small cell lung carcinoma, and 6 had small cell lung carcinoma. The median age at diagnosis was 67 years (range, 51-81 years), and 15 patients (60%) were men. Thirteen patients (52%) had stage IIIA cancer, 10 (40%) had stage IIIB cancer, and 2 (8%) had stage IV cancer. The median CE maximum dose was 66 Gy (range, 44-71 Gy); the median volume of CE receiving at least 55 Gy was 1.4 cm3 (range, 0-5.3 cm3), and the median volume of CE receiving at least 45 Gy was 2.7 cm3 (range, 0-9.2 cm3). The median combined percentage of lung receiving at least 20 Gy was 25% (range, 11%-37%). The median follow-up was 33.3 months (range, 11.1-52.2 months). Among the 20 patients who had treatment breaks of 0 to 3 days and were thus evaluable for the primary end point, the rate of at least grade 3 esophagitis was 0%. Other toxic events observed among all 25 patients included 7 (28%) with grade 2 esophagitis, 3 (12%) with at least grade 2 pneumonitis (including 1 with grade 5), and 2 (8%) with at least grade 3 cardiac toxic event (including 1 with grade 5). There was no isolated local tumor failure. The 2-year progression-free survival rate was 57% (95% CI, 33%-75%), and the 2-year overall survival rate was 67% (95% CI, 45%-82%).

Conclusions and Relevance

This phase 1 nonrandomized clinical trial found that the CE-sparing technique was associated with reduced risk of esophagitis among patients treated uniformly with chemoradiation therapy (to 70 Gy), with no grade 3 or higher esophagitis despite tumor within 1 cm of the esophagus. This technique may be translated into clinical practice.

Trial Registration

ClinicalTrials.gov Identifier: NCT02394548.

Dosimetric Evaluation of Fractionated Stereotactic Radiation Therapy for Skull Base Meningiomas Using HyperArc and Multicriteria Optimization

Purpose

Treatment planning of skull based meningiomas can be difficult due to the irregular shaped target volumes and proximity to critical optic structures. This study evaluated the use of HyperArc (HA) radiosurgery optimization and delivery in conjunction with multicriteria optimization (MCO) to create conformal and efficient treatment plans for conventionally fractionated radiation therapy to difficult base-of-skull (BOS) lesions.

Methods and Materials

Twelve patients with BOS meningioma were retrospectively planned with HA-specific optimization algorithm, stereotactic normal tissue objective (SRS-NTO), and conventional automatic normal tissue objective to evaluate normal brain sparing (mean dose and V20 Gy). MCO was used on both SRS-NTO and automatic normal tissue objective plans to further decrease organ-at-risk doses and target dose maximum to within clinically acceptable constraints. Delivery efficiency was evaluated based on planned monitor units.

Results

The SRS-NTO in HA can be used to improve the mid- and low-dose spread to normal brain tissue in the irradiation of BOS meningiomas. Improvement in normal brain sparing can be seen in larger, more irregular shaped lesions and less so in smaller spherical targets. MCO can be used in conjunction with the SRS-NTO to reduce target dose maximum and dose to organ at risk without sacrificing the gain in normal brain sparing.

Conclusions

HA can be beneficial both in treatment planning by using the SRS-NTO and in delivery efficiency through the decrease in monitor units and automated delivery.

Approximation of dose quality indicator values in multi-criteria optimized (MCO) volumetric modulated arc therapy (VMAT) treatment planning using trilinear dose interpolation

PURPOSE

To approximate dose-volume histogram (DVH) based quality indicators in volumetric modulated arc therapy (VMAT) planning using multi-criteria optimization (MCO) with a low number of composite optimization parameters.

METHODS

The solution space for VMAT optimization with a low number of composite optimization parameters is approximated by trilinear dose inter- polation and prediction of dose-volume-histogram (DVH) based plan quality indicator values. To assess the approximation quality a diverse dataset of 44 cranial and 18 spine patient geometries was chosen. Optimization results are governed by three composite parameters focusing on target-organ-at-risk- (OAR)-trade-off, overall healthy tissue sparing, and delivery/quality assurance complexity. 21,266 optimized dose distributions were pre-calculated and the numerical values for a choice of 10 DVH points, referred to as plan quality indicators, were stored to serve as ground truth. Using a subset of 8 and 27 pre-calculated optimization results, dose distributions for unknown parameter values were approximated by trilinear interpolation. The resulting quality indicator values were compared to the previously obtained exact solutions.

RESULTS

The magnitude of the deviation between exact and approximated values varied largely with respect to patient geometry and the criterion under investigation. Approximation with 27 pre-calculated results yielded lower deviations than approximation with 8 results, at the cost of a higher pre-calculation workload.

CONCLUSIONS

Solution space approximation via trilinear dose interpolation in VMAT treatment planning governed by composite optimization parameters is possible without further knowledge of the internal implementation of the underlying optimizer. Maximum average deviations between approxi- mation and actual values of characteristic dose quality indicators below 1% (cranial) and 8% (spine) allow for a quick qualitative assessment of the possible solution landscape.

Automated Intensity Modulated Radiation Therapy Treatment Planning for Cervical Cancer Based on Convolution Neural Network

Purpose:

To develop and evaluate an automatic intensity-modulated radiation therapy (IMRT) program for cervical cancer, including a Convolution Neural Network (CNN)-based prediction model and an automated optimization strategy.

Methods:

A CNN deep learning model was trained to predict a patient-specify set of IMRT objectives based on overlap volume histograms (OVH) and high-quality plan of previous patients. A total of 140 cervical cancer patients were enrolled in this study, including 100 patients in the training set, 20 patients in the validation set and 20 patients in the testing set. The input of this model was OVH data and the output were values of IMRT plan objectives. For patients in the testing set, the set of planning objectives were predicted by the CNN model and used to automatically generate IMRT plans. Meanwhile, manual plans of these patients were generated by 1 beginner planner and 1 senior planner respectively. Finally, dose distribution, dosimetric parameters and planning time were analyzed. In addition, the 3 types of plans were blinded compared and ranked by an experienced oncologist.

Results:

There were almost no statistically differences among these 3 types of plans in target coverage and dose conformity. Dose homogeneity were slightly decreased while the average dose and parameters for most organs-at-risk (OARs) were decreased in automatic plans. Especially in comparison with manual plans by the beginner planner, V₄₀ of bladder and rectum decreased 6.3% and 12.3%, while mean dose of rectum and marrow were 1.1 Gy and 1.8 Gy lower with automatic plans (either P < 0.017). In the blinded comparison, automatic plans were chosen as best plan in 14 cases.

Conclusions:

For cervical cancer, automatic IMRT plans optimized from the CNN generated objectives have superior dose sparing without compromising of target dose. It significantly reduced the planning time.

Automated Radiotherapy Planning for Patient-Specific Exploration of the Trade-Off Between Tumor Dose Coverage and Predicted Radiation-Induced Toxicity-A Proof of Principle Study for Prostate Cancer

Background: Currently, radiation-oncologists generally evaluate a single treatment plan for each patient that is possibly adapted by the planner prior to final approval. There is no systematic exploration of patient-specific trade-offs between planning aims, using a set of treatment plans with a-priori defined (slightly) different balances. To this purpose, we developed an automated workflow and explored its use for prostate cancer. Materials and Methods: For each of the 50 study patients, seven plans were generated, including the so-called clinical plan, with currently clinically desired ≥99% dose coverage for the low-dose planning target volume (PTVLow). The six other plans were generated with different, reduced levels of PTVLow coverage, aiming at reductions in rectum dose and consequently in predicted grade≥2 late gastro-intestinal (GI) normal tissue complication probabilities (NTCPs), while keeping other dosimetric differences small. The applied NTCP model included diabetes as a non-dosimetric predictor. All plans were generated with a clinically applied, in-house developed algorithm for automated multi-criterial plan generation. Results: With diabetes, the average NTCP reduced from 24.9 ± 4.5% for ≥99% PTVLow coverage to 17.3 ± 2.6% for 90%, approaching the NTCP (15.4 ± 3.0%) without diabetes and full PTVLow coverage. Apart from intended differences in PTVLow coverage and rectum dose, other differences between the clinical plan and the six alternatives were indeed minor. Obtained NTCP reductions were highly patient-specific (ranging from 14.4 to 0.1%), depending on patient anatomy. Even for patients with equal NTCPs in the clinical plan, large differences were found in NTCP reductions. Conclusions: A clinically feasible workflow has been proposed for systematic exploration of patient-specific trade-offs between various treatment aims. For each patient, automated planning is used to generate a limited set of treatment plans with well-defined variations in the balances between the aims. For prostate cancer, trade-offs between PTVLow coverage and predicted GI NTCP were explored. With relatively small coverage reductions, significant NTCP reductions could be obtained, strongly depending on patient anatomy. Coverage reductions could also make up for enhanced NTCPs related to diabetes as co-morbidity, again dependent on the patient. The proposed system can play an important role in further personalization of patient care.

Automatic Planning for Nasopharyngeal Carcinoma Based on Progressive Optimization in RayStation Treatment Planning System

OBJECTIVE

To evaluate and quantify the planning performance of automatic planning (AP) with manual planning (MP) for nasopharyngeal carcinoma in the RayStation treatment planning system (TPS).

METHODS

A progressive and effective design method for AP of nasopharyngeal carcinoma was realized through automated scripts in this study. A total of 30 patients with nasopharyngeal carcinoma with initial treatment was enrolled. The target coverage, conformity index (CI), homogeneity index (HI), organs at risk sparing, and the efficiency of design and execution were compared between automatic and manual volumetric modulated arc therapy (VMAT) plans.

RESULTS

The results of the 2 design methods met the clinical dose requirement. The differences in D95 between the 2 groups in PTV1 and PTV2 showed statistical significance, and the MPs are higher than APs, but the difference in absolute dose was only 0.21% and 0.16%. The results showed that the conformity index of planning target volumes (PTV1, PTV2, PTVnd and PGTVnx+rpn [PGTVnx and PGTVrpn]), homogeneity index of PGTVnx+rpn, and HI of PTVnd in APs are better than that in MPs. For organs at risk, the APs are lower than the MPs, and the difference was statistically significant (P < .05). The manual operation time in APs was 83.21% less than that in MPs, and the computer processing time was 34.22% more.

CONCLUSION

IronPython language designed by RayStation TPS has clinical application value in the design of automatic radiotherapy plan for nasopharyngeal carcinoma. The dose distribution of tumor target and organs at risk in the APs was similar or better than those in the MPs. The time of manual operation in the plan design showed a sharp reduction, thus significantly improving the work efficiency in clinical application.

Treatment planning for proton therapy: what is needed in the next 10 years?

Treatment planning is the process where the prescription of the radiation oncologist is translated into a deliverable treatment. With the complexity of contemporary radiotherapy, treatment planning cannot be performed without a computerized treatment planning system. Proton therapy (PT) enables highly conformal treatment plans with a minimum of dose to tissues outside the target volume, but to obtain the most optimal plan for the treatment, there are a multitude of parameters that need to be addressed. In this review areas of ongoing improvements and research in the field of PT treatment planning are identified and discussed. The main focus is on issues of immediate clinical and practical relevance to the PT community highlighting the needs for the near future but also in a longer perspective. We anticipate that the manual tasks performed by treatment planners in the future will involve a high degree of computational thinking, as many issues can be solved much better by e.g. scripting. More accurate and faster dose calculation algorithms are needed, automation for contouring and planning is required and practical tools to handle the variable biological efficiency in PT is urgently demanded just to mention a few of the expected improvements over the coming 10 years.

Effectiveness of Multi-Criteria Optimization-based Trade-Off exploration in combination with RapidPlan for head & neck radiotherapy planning

Background

A new strategy is introduced combining the use of Multi-Criteria Optimization-based Trade-Off Exploration (TO) and RapidPlan™ (RP) for the selection of optimisation parameters that improve the trade-off between sparing of organs at risk (OAR) and target coverage for head and neck radiotherapy planning. Using both approaches simultaneously; three different workflows were proposed for the optimisation process of volumetric-modulated arc therapy (VMAT) plans. The generated plans were compared to the clinical plans and the plans that resulted using RP and TO individually.

Methods

Twenty clinical VMAT plans previously administered were selected. Five additional plans were created for each patient: a clinical plan further optimised with TO (Clin+TO); two plans generated by in-house built RP models, RP_1 with the model built with VMAT clinical plans and RP_TO with the model built with VMAT plans optimised by TO. Finally, these last two plans were additionally optimised with TO for the creation of the plans RP_1 + TO and RP_TO⁺ respectively. The TO management was standardised to maximise the sparing of the parotid glands without compromising a clinically acceptable PTV coverage. Resulting plans were inter-compared based on dose-volume parameters for OAR and PTVs, target homogeneity, conformity, and plans complexity and deliverability.

Results

The plans optimised using TO in combination with RP showed significantly improved OAR sparing while maintaining comparable target dose coverage to the clinical plans. The largest OAR sparing compared to the clinical plans was achieved by the RP_TO⁺ plans, which reported a mean parotid dose average of 15.0 ± 4.6 Gy vs 22.9 ± 5.5 Gy (left) and 17.1 ± 5.0 Gy vs 24.8 ± 5.8 Gy (right). However, at the same time, RP_TO⁺ showed a slight dose reduction for the 99% volume of the nodal PTV and an increase for the 95% (when comparing to the clinical plans 76.0 ± 1.2 vs 77.4 ± 0.6 and 80.9 ± 0.9 vs 79.7 ± 0.4) but remained within clinical acceptance. Plans optimised with RP and TO combined, showed an increase in complexity but were proven to be deliverable.

Conclusion

The use of TO combined with RP during the optimisation of VMAT plans enhanced plan quality the most. For the RP_TO⁺ plans, acceptance of a slight deterioration in nodal PTV allowed the largest reduction in OAR dose to be achieved.

Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations

Radiotherapy treatment planning of complex radiotherapy techniques, such as intensity modulated radiotherapy and volumetric modulated arc therapy, is a resource-intensive process requiring a high level of treatment planner intervention to ensure high plan quality. This can lead to variability in the quality of treatment plans and the efficiency in which plans are produced, depending on the skills and experience of the operator and available planning time. Within the last few years, there has been significant progress in the research and development of intensity modulated radiotherapy treatment planning approaches with automation support, with most commercial manufacturers now offering some form of solution. There is a rapidly growing number of research articles published in the scientific literature on the topic. This paper critically reviews the body of publications up to April 2018. The review describes the different types of automation algorithms, including the advantages and current limitations. Also included is a discussion on the potential issues with routine clinical implementation of such software, and highlights areas for future research.

Multicriteria plan optimization in the hands of physicians: a pilot study in prostate cancer and brain tumors

BACKGROUND

The purpose of this study was to demonstrate the feasibility of physician driven planning in intensity modulated radiotherapy (IMRT) with a multicriteria optimization (MCO) treatment planning system and template based plan optimization. Exploiting the full planning potential of MCO navigation, this alternative planning approach intends to improve planning efficiency and individual plan quality.

METHODS

Planning was retrospectively performed on 12 brain tumor and 10 post-prostatectomy prostate patients previously treated with MCO-IMRT. For each patient, physicians were provided with a template-based generated Pareto surface of optimal plans to navigate, using the beam angles from the original clinical plans. We compared physician generated plans to clinically delivered plans (created by dosimetrists) in terms of dosimetric differences, physician preferences and planning times.

RESULTS

Plan qualities were similar, however physician generated and clinical plans differed in the prioritization of clinical goals. Physician derived prostate plans showed significantly better sparing of the high dose rectum and bladder regions (p(D1) < 0.05; D1: dose received by 1% of the corresponding structure). Physicians' brain tumor plans indicated higher doses for targets and brainstem (p(D1) < 0.05). Within blinded plan comparisons physicians preferred the clinical plans more often (brain: 6:3 out of 12, prostate: 2:6 out of 10) (not statistically significant). While times of physician involvement were comparable for prostate planning, the new workflow reduced the average involved time for brain cases by 30%. Planner times were reduced for all cases. Subjective benefits, such as a better understanding of planning situations, were observed by clinicians through the insight into plan optimization and experiencing dosimetric trade-offs.

CONCLUSIONS

We introduce physician driven planning with MCO for brain and prostate tumors as a feasible planning workflow. The proposed approach standardizes the planning process by utilizing site specific templates and integrates physicians more tightly into treatment planning. Physicians' navigated plan qualities were comparable to the clinical plans. Given the reduction of planning time of the planner and the equal or lower planning time of physicians, this approach has the potential to improve departmental efficiencies.

Potential proton and photon dose degradation in advanced head and neck cancer patients by intratherapy changes

Purpose

Evaluation of dose degradation by anatomic changes for head‐and‐neck cancer (HNC) intensity‐modulated proton therapy (IMPT) relative to intensity‐modulated photon therapy (IMRT) and identification of potential indicators for IMPT treatment plan adaptation.

Methods

For 31 advanced HNC datasets, IMPT and IMRT plans were recalculated on a computed tomography scan (CT) taken after about 4 weeks of therapy. Dose parameter changes were determined for the organs at risk (OARs) spinal cord, brain stem, parotid glands, brachial plexus, and mandible, for the clinical target volume (CTV) and the healthy tissue outside planning target volume (PTV). Correlation of dose degradation with target volume changes and quality of rigid CT matching was investigated.

Results

Recalculated IMPT dose distributions showed stronger degradation than the IMRT doses. OAR analysis revealed significant changes in parotid median dose (IMPT) and near maximum dose (D_1ml) of spinal cord (IMPT, IMRT) and mandible (IMPT). OAR dose parameters remained lower in IMPT cases. CTV coverage (V_95%) and overdose (V_107%) deteriorated for IMPT plans to (93.4 ± 5.4)% and (10.6 ± 12.5)%, while those for IMRT plans remained acceptable. Recalculated plans showed similarly decreased PTV conformity, but considerable hotspots, also outside the PTV, emerged in IMPT cases. Lower CT matching quality was significantly correlated with loss of PTV conformity (IMPT, IMRT), CTV homogeneity and coverage (IMPT). Target shrinkage correlated with increased dose in brachial plexus (IMRT, IMPT), hotspot generation outside the PTV (IMPT) and lower PTV conformity (IMRT).

Conclusions

The study underlines the necessity of precise positioning and monitoring of anatomy changes, especially in IMPT which might require adaptation more often. Since OAR doses remained typically below constraints, IMPT plan adaptation will be indicated by target dose degradations.

Using measurable dosimetric quantities to characterize the inter-structural tradeoff in inverse planning

Traditional inverse planning relies on the use of weighting factors to balance the conflicting requirements of different structures. Manual trial-and-error determination of weighting factors has long been recognized as a time-consuming part of treatment planning. The purpose of this work is to develop an inverse planning framework that parameterizes the dosimetric tradeoff among the structures with physically meaningful quantities to simplify the search for clinically sensible plans. In this formalism, instead of using weighting factors, the permissible variation range of the prescription dose or dose volume histogram (DVH) of the involved structures are used to characterize the 'importance' of the structures. The inverse planning is then formulated into a convex feasibility problem, called the dosimetric variation-controlled model (DVCM), whose goal is to generate plans with dosimetric or DVH variations of the structures consistent with the pre-specified values. For simplicity, the dosimetric variation range for a structure is extracted from a library of previous cases which possess similar anatomy and prescription. A two-phase procedure (TPP) is designed to solve the model. The first phase identifies a physically feasible plan to satisfy the prescribed dosimetric variation, and the second phase automatically improves the plan in case there is room for further improvement. The proposed technique is applied to plan two prostate cases and two head-and-neck cases and the results are compared with those obtained using a conventional CVaR approach and with a moment-based optimization scheme. Our results show that the strategy is able to generate clinically sensible plans with little trial and error. In all cases, the TPP generates a very competitive plan as compared to those obtained using the alternative approaches. Particularly, in the planning of one of the head-and-neck cases, the TPP leads to a non-trivial improvement in the resultant dose distribution-the fractional volumes receiving a dose above 20 Gy for the spinal cord are reduced by more than 40% when compared to the alternative schemes, while maintaining the same PTV coverage. With physically more meaningful modeling of the inter-structural tradeoff, the reported technique enables us to substantially reduce the need for trial-and-error adjustment of the model parameters. The new formalism also opens new opportunities for incorporating prior knowledge to facilitate the treatment planning process.

Fully automated VMAT treatment planning for advanced-stage NSCLC patients

PURPOSE

To develop a fully automated procedure for multicriterial volumetric modulated arc therapy (VMAT) treatment planning (autoVMAT) for stage III/IV non-small cell lung cancer (NSCLC) patients treated with curative intent.

MATERIALS AND METHODS

After configuring the developed autoVMAT system for NSCLC, autoVMAT plans were compared with manually generated clinically delivered intensity-modulated radiotherapy (IMRT) plans for 41 patients. AutoVMAT plans were also compared to manually generated VMAT plans in the absence of time pressure. For 16 patients with reduced planning target volume (PTV) dose prescription in the clinical IMRT plan (to avoid violation of organs at risk tolerances), the potential for dose escalation with autoVMAT was explored.

RESULTS

Two physicians evaluated 35/41 autoVMAT plans (85%) as clinically acceptable. Compared to the manually generated IMRT plans, autoVMAT plans showed statistically significant improved PTV coverage (V_95% increased by 1.1% ± 1.1%), higher dose conformity (R₅₀ reduced by 12.2% ± 12.7%), and reduced mean lung, heart, and esophagus doses (reductions of 0.9 Gy ± 1.0 Gy, 1.5 Gy ± 1.8 Gy, 3.6 Gy ± 2.8 Gy, respectively, all p < 0.001). To render the six remaining autoVMAT plans clinically acceptable, a dosimetrist needed less than 10 min hands-on time for fine-tuning. AutoVMAT plans were also considered equivalent or better than manually optimized VMAT plans. For 6/16 patients, autoVMAT allowed tumor dose escalation of 5-10 Gy.

CONCLUSION

Clinically deliverable, high-quality autoVMAT plans can be generated fully automatically for the vast majority of advanced-stage NSCLC patients. For a subset of patients, autoVMAT allowed for tumor dose escalation.