A method for a priori estimation of best feasible DVH for organs-at-risk: Validation for head and neck VMAT planning

Application and progress of artificial intelligence in radiation therapy dose prediction

Radiation therapy (RT) nowadays is a main treatment modality of cancer. To ensure the therapeutic efficacy of patients, accurate dose distribution is often required, which is a time-consuming and labor-intensive process. In addition, due to the differences in knowledge and experience among participants and diverse institutions, the predicted dose are often inconsistent. In last several decades, artificial intelligence (AI) has been applied in various aspects of RT, several products have been implemented in clinical practice and confirmed superiority. In this paper, we will review the research of AI in dose prediction, focusing on the progress in deep learning (DL).

Multi-centre real-world validation of automated treatment planning for breast radiotherapy

PURPOSE

To present the results of the first multi-centre real-world validation of autoplanning for whole breast irradiation after breast-sparing surgery, encompassing high complexity cases (e.g. with a boost or regional lymph nodes) and a wide range of clinical practices.

METHODS

The 24 participating centers each included 10 IMRT/VMAT/Tomotherapy patients, previously treated with a manually generated plan ('manplan'). There were no restrictions regarding case complexity, planning aims, plan evaluation parameters and criteria, fractionation, treatment planning system or treatment machine/technique. In addition to dosimetric comparisons of autoplans with manplans, blinded plan scoring/ranking was conducted by a clinician from the treating center. Autoplanning was performed using a single configuration for all patients in all centres. Deliverability was verified through measurements at delivery units.

RESULTS

Target dosimetry showed comparability, while reductions in OAR dose parameters were 21.4 % for heart Dmean, 16.7 % for ipsilateral lung Dmean, and 101.9 %, 45.5 %, and 35.7 % for contralateral breast D0.03cc, D5% and Dmean, respectively (all p < 0.001). Among the 240 patients included, the clinicians preferred the autoplan for 119 patients, with manplans preferred for 96 cases (p = 0.01). Per centre there were on average 5.0 ± 2.9 (1SD) patients with a preferred autoplan (range [0-10]), compared to 4.0 ± 2.7 with a preferred manplan ([0,9]). No differences were observed regarding deliverability.

CONCLUSION

The automation significantly reduced the hands-on planning workload compared to manual planning, while also achieving an overall superiority. However, fine-tuning of the autoplanning configuration prior to clinical implementation may be necessary in some centres to enhance clinicians' satisfaction with the generated autoplans.

On the necessity of specialized knowledge-based models for SBRT prostate treatments plans

PURPOSE

Test whether a well-grounded KBP model trained on moderately hypo-fractionated prostate treatments can be used to satisfactorily drive the optimization of SBRT prostate treatments.

MATERIALS AND METHODS

A KBP model (SBRT-model) was developed, trained and validated using the first forty-seven clinically treated VMAT SBRT prostate plans (42.7 Gy/7fx or 36.25 Gy/5fx). The performance and robustness of this model were compared against a high-quality KBP-model (ST-model) that was already clinically adopted for hypo-fractionated (70 Gy/28fx and 60 Gy/20fx) prostate treatments. The two models were compared in terms of their predictions robustness, and the quality of their outcomes were evaluated against a set of reference clinical SBRT plans. Plan quality was assessed using DVH metrics, blinded clinical ranking, and a dedicated Plan Quality Metric algorithm.

RESULTS

The plan libraries of the two models were found to share a high degree of anatomical similarity. The overall quality (APQM%) of the plans obtained both with the ST- and SBRT-models was compatible with that of the original clinical plans, namely (93.7 ± 4.1)% and (91.6 ± 3.9)% vs (92.8.9 ± 3.6)%. Plans obtained with the ST-model showed significantly higher target coverage (PTV V95%): (97.9 ± 0.8)% vs (97.1 ± 0.9)% (p < 0.05). Conversely, plans optimized following the SBRT-model showed a small but not-clinically relevant increase in OAR sparing. ST-model generally provided more reliable predictions than SBRT-model. Two radiation oncologists judged as equivalent the plans based on the KBP prediction, which was also judged better that reference clinical plans.

CONCLUSION

A KBP model trained on moderately fractionated prostate treatment plans provided optimal SBRT prostate plans, with similar or larger plan quality than an embryonic SBRT-model based on a limited number of cases.

Comparative analysis of two dose-volume histogram prediction tools for treatment planning in volumetric-modulated arc therapy: A multi-planner study

The increase in high-precision radiation therapy, particularly volumetric-modulated arc therapy (VMAT), has increased patient numbers and expanded treatment sites. However, a significant challenge in VMAT treatment planning is the inconsistent plan quality among different planners and facilities. This study explored the use of dose-volume histogram (DVH) prediction tools to address these disparities, specifically focusing on RapidPlan (Varian Medical Systems) and PlanIQ (Sun Nuclear). RapidPlan predicts achievable DVHs and automatically generates optimization objectives. While it has demonstrated organ-at-risk (OAR) dose reduction benefits, the quality of the plan used to build its model significantly affects its predictions. On the other hand, PlanIQ offers ease of use and does not require prior model-building. Five planners participated in this study, each creating two treatment plans: one referencing RapidPlan and the other using PlanIQ. The planners had the freedom to adjust parameters while referencing the DVH predictions. The plans were evaluated using "Plan Quality Metric" (PQM) scores to assess the planning target volume excluding the rectum and OARs. The results revealed that RapidPlan-referenced plans often outperformed PlanIQ-based plans, with less interplanner variability. PlanIQ played a pivotal role in the construction of the RapidPlan model. This study is the first to compare plans generated by multiple planners using both tools. This study provides insights into optimizing treatment planning by considering the characteristics of both RapidPlan and PlanIQ.

Knowledge-based model building for treatment planning for prostate cancer using commercial treatment planning quality assurance software tools

This study devised a method to efficiently launch the RapidPlan model for volumetric-modulated arc therapy for prostate cancer in small- and medium-sized facilities using high-quality treatment plans with the PlanIQ software as a reference. Treatment plans were generated for 30 patients with prostate cancer to construct the RapidPlan model using PlanIQ as a reference. In the context of PlanIQ-referenced treatment planning, treatment plans were developed, such that the feasibility dose-volume histogram of each organ-at-risk fell within F ≤ 0.1. For validation of the RapidPlan model, treatment plans were formulated for 20 patients using both RapidPlan and PlanIQ, and the differences were evaluated. The results of RapidPlan model validity assessment revealed that the RapidPlan-produced treatment plans exhibited higher quality in 11 of 20 patients. No significant differences were found between the treatment plans. In conclusion, high-quality treatment plans formulated using PlanIQ as reference facilitated efficient implementation of RapidPlan modeling.

Comparing breath hold versus free breathing irradiation for left-sided breast radiotherapy by PlanIQ™

BACKGROUND

Breast cancer is the most widespread cancer in women and young women worldwide. Moving towards customised radiotherapy, balancing the use of the available technology with the best treatment modality may not be an easy task in the daily routine. This study aims to evaluate the effectiveness of introducing IQ-feasibility into clinical practice to support the decision of free-breathing (FB) versus breath-hold (BH) left-sided breast irradiations, in order to optimise the technology available and the effectiveness of the treatment.

METHODS

Thirty-five patients who received 3D radiotherapy treatment of the left breast in deep-inspiration BH were included in this retrospective study. Computed tomography scans in FB and BH were acquired for each patient; targets contoured in both imaging datasets by an experienced radiation oncologist, and organs at risk delineated using automatic segmentation software were exported to PlanIQ™ (Sun Nuclear Corp.) to generate feasibility dose volume histogram (FDVHs). The dosimetric parameter of BH versus FB FDVH, and BH clinical dataset versus BH FDVH were compared.

RESULTS

A total of 30 patients out of 35 patients analysed, presented for the BH treatments a significant reduction (p < 0.05) in the heart mean dose ([Formula: see text]), volume receiving 5 Gy ([Formula: see text]) and 20 Gy ([Formula: see text]), of 35.7%, 54.5%, and 2.1%, respectively; for the left lung, a lower reduction was registered and significant only for [Formula: see text] (21.4%, p = 0.046). For the remaining five patients, the FDVH cut-off points of heart and lung were superimposable with differences of less than 1%. Heart and left lung dosimetric parameters of the BH clinical plans are located in the difficult zone of the FDVH and differ significantly (p < 0.05) from the corresponding parameters of the FDVH curves delimiting this buffer area between the impossible and feasible zones, respectively.

CONCLUSION

The use of PlanIQTM as a decision-support tool for the FB versus BH treatment delivery modality allows customisation of the treatment technique using the most appropriate technology for each patient enabling accurate management of available technologies.

Differences between professionals in treatment planning for patients with stage III lung cancer using treatment-planning QA software

Background

The quality of treatment planning for stage III non-small cell lung cancer varies within and between facilities due to the different professions involved in planning. Dose estimation parameters were calculated using a feasibility dose-volume histogram (FDVH) implemented in the treatment planning quality assurance software PlanIQ. This study aimed to evaluate differences in treatment planning between occupations using manual FDVH-referenced treatment planning to identify their characteristics.

Materials and methods

The study included ten patients with stage III non-small cell lung cancer, and volumetric-modulated arc therapy was used as the treatment planning technique. Fifteen planners, comprising five radiation oncologists, five medical physicists, and five radiological technologists, developed treatment strategies after referring to the FDVH.

Results

Medical physicists had a higher mean dose at D98% of the planning target volume (PTV) and a lower mean dose at D2% of the PTV than those in other occupations. Medical physicists had the lowest irradiation lung volumes (V5 Gy and V13 Gy) compared to other professions, and radiation oncologists had the lowest V20 Gy and mean lung dose. Radiological technologists had the highest irradiation volumes for dose constraints at all indexes on the normal lung volume.

Conclusions

The quality of the treatment plans developed in this study differed between occupations due to their background expertise, even when an FDVH was used as a reference. Therefore, discussing and sharing knowledge and treatment planning techniques among professionals is essential to determine the optimal treatment plan for each facility and patient.

Toward Systematic Assessment and Improvement of Radiation Therapy Plan Quality of Cooperative Group Trial Submissions: A Report From the Children's Oncology Group

PURPOSE

This study evaluates the quality of plans used for the treatment of patients in the Children's Oncology Group study ACNS1123. Plan quality is quantified based on a scoring system specific to the protocol. In this way, the distribution of plan quality scores is determined that can be used to identify plan quality issues for this study and for future plan quality improvement.

METHODS AND MATERIALS

ACNS1123 stratum 1 patients (70) were evaluated. This included 50 photon and 20 proton plans. Digital Imaging and Communications in Medicine (DICOM) structure and dose data were obtained from the Children's Oncology Group. A commercially available plan quality scoring algorithm was used to create a scoring system we designed using the protocol dosimetric requirements. The whole ventricle and boost planning target volumes (PTVs) could earn a maximum of 70 points, whereas the organs at risk could earn 30 points (total maximum score of 100 points). The scoring algorithm adjusted scores based on the difficulty in achieving the structure dose requirements, which depended on the proximity of the PTVs and the dose gradients achieved relative to the organs at risk. The distribution of plan scores was used to determine the mean, median, and range of scores.

RESULTS

The median adjusted plan quality scores for the 20 proton and 50 photon plans were 83.3 and 86.9, respectively. The range of adjusted scores (maximum to minimum) was 50 points. The average score adjustment was 7.4 points. Photon and proton plans performed almost equally. Average plan quality by individual structure revealed that the brain stem, PTV boost, and cochlea lost the most points.

CONCLUSIONS

This report is the first to systematically analyze overall radiation therapy plan quality scores for an entire cohort of patients treated in a cooperative group clinical trial. The methodology demonstrated a large variation in plan quality in this trial. Future clinical trials could potentially use this method to reduce plan quality variability, which may improve outcomes.

Estimation of the risk of secondary cancer in rectum and bladder after radiation therapy for prostate cancer using a feasibility dose-volume histogram

We investigated the risk of secondary cancers in rectum and bladder for prostate cancer radiotherapy using a feasibility assessment tool. We calculated the risk of secondary cancer by generating a dose-volume histogram based on an ideal dose falloff function (f-value). This study found a smaller f-value was associated with a lower secondary cancer risk in the rectum but a higher risk in the bladder. The study suggests setting the f-value at 0-0.1 as the optimization goal for the rectum and 0.4 for the bladder is reasonable and feasible for reducing the risk of secondary cancer and other adverse events.

Feasibility-guided automated planning for stereotactic treatments of prostate cancer

Significant improvements in plan quality using automated planning have been previously demonstrated. The aim of this study was to develop an optimal automated class solution for stereotactic radiotherapy (SBRT) planning of prostate cancer using the new Feasibility module implemented in the pinnacle evolution. Twelve patients were retrospectively enrolled in this planning study. Five plans were designed for each patient. Four plans were automatically generated using the 4 proposed templates for SBRT optimization implemented in the new pinnacle evolution treatment planning systems, differing for different settings of dose-fallout (low, medium, high and veryhigh). Based on the obtained results, the fifth plan (feas) was generated customizing the template with the optimal criteria obtained from the previous step and integrating in the template the "a-priori" knowledge of OARs sparing based on the Feasibility module, able to estimate the best possible dose-volume histograms of OARs before starting optimization. Prescribed dose was 35 Gy to the prostate in 5 fractions. All plans were generated with a full volumetric-modulated arc therapy arc and 6MV flattening filter-free beams, and optimized to ensure the same target coverage (95% of the prescription dose to 98% of the target). Plans were assessed according to dosimetric parameters and planning and delivery efficiency. Differences among the plans were evaluated using a Kruskal-Wallis 1-way analysis of variance. The requests for more aggressive objectives for dose falloff parameters (from low to veryhigh) translated in a statistically significant improvement of dose conformity, but at the expense of a dose homogeneity. The best automated plans in terms of best trade-off between target coverage and OARs sparing among the 4 plans automatically generated by the SBRT module were the high plans. The veryhigh plans reported a significant increase of high-doses to prostate, rectum, and bladder that was considered dosimetrically and clinically unacceptable. The feas plans were optimized on the basis on high plans, reporting significant reduction of rectum irradiation; Dmean, and V18 decreased by 19% to 23% (p = 0.031) and 4% to 7% (p = 0.059), respectively. No statistically significant differences were found in femoral heads and penile bulb irradiation for all dosimetric metrics. feas plans showed a significant increase of MU/Gy (mean: 368; p = 0.004), reflecting an increased level of fluence modulation. Thanks to the new efficient optimization engines implemented in pinnacle evolution (L-BFGS and layered graph), mean planning time was decreased to less than 10 minutes for all plans and all techniques. The integration of dose-volume histograms a-priori knowledge provided by the feasibility module in the automated planning process for SBRT planning has shown to significantly improve plan quality compared to generic protocol values as inputs.

Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy

BACKGROUND AND PURPOSE

Clinical knowledge-based planning (KBP) models dedicated to prostate radiotherapy treatment may require periodical updates to remain relevant and to adapt to possible changes in the clinic. This study proposes a paired comparison of two different update approaches through a longitudinal analysis.

MATERIALS AND METHODS

A clinically validated KBP model for moderately hypofractionated prostate therapy was periodically updated using two approaches: one was targeted at achieving the biggest library size (M_t), while the other one at achieving the highest mean sample quality (R_t). Four subsequent updates were accomplished. The goodness, robustness and quality of the outcomes were measured and compared to those of the common ancestor. Plan quality was assessed through the Plan Quality Metric (PQM) and plan complexity was monitored.

RESULTS

Both update procedures allowed for an increase in the OARs sparing between +3.9 % and +19.2 % compared to plans generated by a human planner. Target coverage and homogeneity slightly reduced [-0.2 %;-14.7 %] while plan complexity showed only minor changes. Increasing the sample size resulted in more reliable predictions and improved goodness-of-fit, while increasing the mean sample quality improved the outcomes but slightly reduced the models reliability.

CONCLUSIONS

Repeated updates of clinical KBP models can enhance their robustness, reliability and the overall quality of automatically generated plans. The periodical expansion of the model sample accompanied by the removal of the unacceptable low quality plans should maximize the benefits of the updates while limiting the associated workload.

Using feasibility dose-volume histograms to reduce intercampus plan quality variability for head-and-neck cancer

The purpose of this work is to objectively assess variability of intercampus plan quality for head-and-neck (HN) cancer and to test utility of a priori feasibility dose-volume histograms (FDVHs) as planning dose goals. In this study, 109 plans treated from 2017 to 2019 were selected, with 52 from the main campus and 57 from various regional centers. For each patient, the planning computed tomography images and contours were imported into a commercial program to generate FDVHs with a feasibility value (f-value) ranging from 0.0 to 0.5. For 10 selected organs-at-risk (OARs), we used the Dice similarity coefficient (DSC) to quantify the overlaps between FDVH and clinically achieved DVH of each OAR and determined the f-value associated with the maximum DSC (labeled as f-max). Subsequently, 10 HN plans from the regional centers were replanned with planning dose goals guided by FDVHs. The clinical and feasibility-guided auto-planning (FgAP) plans were evaluated using our institutional criteria. Among plans from the main campus and regional centers, the median f-max values were statistically significantly different (p < 0.05) for all OARs except for the left parotid (p = 0.622), oral cavity (p = 0.057), and mandible (p = 0.237). For the 10 FgAP plans, the median values of f-max were 0.21, compared to 0.37 from the clinical plans. With comparable dose coverage to the tumor volumes, the significant differences (p < 0.05) in the median f-max and corresponding dose reduction (shown in parenthesis) for the spinal cord, larynx, supraglottis, trachea, and esophagus were 0.27 (8.5 Gy), 0.3 (7.6 Gy), 0.19 (5.9 Gy), 0.19 (8.9 Gy), and 0.12 (4.0 Gy), respectively. In conclusion, the FDVH prediction is an objective quality assurance tool to evaluate the intercampus plan variability. This tool can also provide guideline in planning dose goals to further improve plan quality.

Organ-at-risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT

OBJECTIVE

To quantify the clinical performance of a machine learning (ML) algorithm for organ-at-risk (OAR) dose prediction for lung stereotactic body radiation therapy (SBRT) and estimate the treatment planning benefit from having upfront access to these dose predictions.

METHODS

ML models were trained using multi-center data consisting of 209 patients previously treated with lung SBRT. Two prescription levels were investigated, 50 Gy in five fractions and 54 Gy in three fractions. Models were generated using a gradient-boosted regression tree algorithm using grid searching with fivefold cross-validation. Twenty patients not included in the training set were used to test OAR dose prediction performance, ten for each prescription. We also performed blinded re-planning based on OAR dose predictions but without access to clinically delivered plans. Differences between predicted and delivered doses were assessed by root-mean square deviation (RMSD), and statistical differences between predicted, delivered, and re-planned doses were evaluated with one-way analysis of variance (ANOVA) tests.

RESULTS

ANOVA tests showed no significant differences between predicted, delivered, and replanned OAR doses (all p ≥ 0.36). The RMSD was 2.9, 3.9, 4.3, and 1.7Gy for max dose to the spinal cord, great vessels, heart, and trachea, respectively, for 50 Gy in five fractions. Average improvements of 1.0, 1.4, and 2.0 Gy were seen for spinal cord, esophagus, and trachea max doses in blinded replans compared to clinically delivered plans with 54 Gy in three fractions, and 1.8, 0.7, and 1.5 Gy, respectively, for the esophagus, heart and bronchus max doses with 50 Gy in five fractions. Target coverage was similar with an average PTV V100% of 94.7% for delivered plans compared to 97.3% for blinded re-plans for 50 Gy in five fractions, and respectively 98.4% versus 99.2% for 54 Gy in three fractions.

CONCLUSION

This study validated ML-based OAR dose prediction for lung SBRT, showing potential for improved OAR dose sparing and more consistent plan quality using dose predictions for patient-specific planning guidance.

Plan quality in radiotherapy treatment planning - Review of the factors and challenges

A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.

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.

Volumetric dose extension for isodose tuning

PURPOSE

To develop a method that can extend dose from two isodose surfaces (isosurfaces) to the entire patient volume, and to demonstrate its application in radiotherapy plan isodose tuning.

METHODS

We hypothesized that volumetric dose distribution can be extended from two isosurfaces-the 100% isosurface and a reference isosurface--with the distances to these two surfaces (L_ref ) as extension variables. The extension function is modeled by a three-dimensional lookup table (LUT), where voxel dose values from clinical plans are binned by three indexes: L₁₀₀ , L_ref and D_ref (reference dose level). The mean and standard deviation of voxel doses in each bin are calculated and stored in LUT. Volumetric dose extension is performed voxelwisely by indexing the LUT with the L₁₀₀ , L_ref and D_ref of each query voxel. The mean dose stored in the corresponding bin is filled into the query voxel as extended dose, and the standard deviation be filled voxelwisely as the uncertainty of extension result. We applied dose extension in isodose tuning, which aims to tune volumetric dose distribution by isosurface dragging. We adopted extended dose as an approximate dose estimation, and combined it with dose correction strategy to achieve accurate dose tuning.

RESULTS

We collected 32 post-operative prostate volumetric modulated arc therapy (VMAT) cases and built the LUT and its associated uncertainties from the doses of 27 cases. The dose extension method was tested on five cases, whose dose distributions were defined as ground truth (GT). We extended the doses from 100% and 50% GT isosurfaces to the entire volume, and evaluated the accuracy of extended doses. The 5 mm/5% gamma passing rate (GPR) of extended doses are 92.0%. The mean error is 4.5%, which is consistent to the uncertainty estimated by LUT. The dose difference in 90.5% of voxels is within two sigma and 97.5% in three sigma. The calculation time is less than two seconds. To simulate plan isodose tuning, we optimized a dose with less sparing on rectum (than GT dose) and defined it as a "base dose"-the dose awaiting isosurface dragging. In front-end, the simulated isodose tuning is conducted as such that the base dose was given to plan tuner, and its 50% isosurface would be dragged to the desired position (position of 50% isosurface in GT dose). In back-end, the output of isodose tuning is obtained by 1) extending dose from the desired isosurfaces and viewed the extended dose as an approximate dose, 2) obtaining a correction map from the base dose, and 3) applying the correction map to the extended dose. The accuracy of output-extended dose with correction-was 97.2% in GPR (3 mm/3%) and less than 1% in mean dose difference. The total calculation time is less than two seconds, which allows for interactive isodose tuning.

CONCLUSIONS

We developed a dose extension method that generates volumetric dose distribution from two surfaces. The application of dose extension is in interactive isodose tuning. The distance-based LUT fashion and correction strategy guarantee the computation efficiency and accuracy in isodose tuning. This article is protected by copyright. All rights reserved.

Evaluation of two independent dose prediction methods to personalize the automated radiotherapy planning process for prostate cancer

Background and purpose

Currently, automatic approaches for radiotherapy planning are widely used, however creation of high quality treatment plans is still challenging. In this study, two independent dose prediction methods were used to personalize the initial settings for the automated planning template for optimizing prostate cancer treatment plans. This study evaluated the dose metrics of these plans comparing both methods with the current clinical automated prostate cancer treatment plans.

Material and methods

Datasets of 20 high-risk prostate cancer treatment plans were taken from our clinical database. The prescription dose for these plans was 70 Gy given in fractions of 2.5 Gy. Plans were replanned using the current clinical automated treatment and compared with two personalized automated planning methods. The feasibility dose volume histogram (FDVH) and modified filter back projection (mFBP) methods were used to calculate independent dose predictions. Parameters for the initial objective values of the planning template were extracted from these predictions and used to personalize the optimization of the automated planning process.

Results

The current automated replanned clinical plans and the automated plans optimized with the personalized template methods fulfilled the clinical dose criteria. For both methods a reduction in the average mean dose of the rectal wall was found, from 22.5 to 20.1 Gy for the FDVH and from 22.5 to 19.6 Gy for the mFBP method.

Conclusions

With both dose-prediction methods the initial settings of the template could be personalized. Hereby, the average dose to the rectal wall was reduced compared to the standard template method.

Clinical Use of A Priori Knowledge of OAR Sparing During Radiotherapy Treatment for Oropharyngeal Cancer: Dosimetric and Patient Reported Outcome Improvements

PURPOSE

To prospectively assess dosimetric and clinical impacts of treatment planners having a priori knowledge of the maximum achievable dose sparing for organs at risk for patients with oropharynx cancer receiving IMRT.

METHODS AND MATERIALS

We examined oropharynx cancer patients treated on prospective clinical trials from February 2012 to April 2019 at our institution. A tool that generates estimates of maximum achievable dose sparing for organs at risk (Feasibility-DVH; FDVH) was used clinically starting July 2016. Patients were divided into cohorts:pre-FDVH (i.e. baseline) and post-FDVH (i.e. FDVH-guided). Doses received by various OARs were compared to those estimated to be achievable per FDVH and that difference was defined as the 'excess of feasible dose'. Patient reported outcomes (PRO) questionnaires were completed at 3, 6 and 12 months post-treatment. The baseline and FDVH-guided cohorts were compared in terms of excess of feasible dose, plan quality metrics, and post-RT PRO assessments.

RESULTS

One hundred thirty-nine patients were included in the analysis (60-baseline cohort, 79-FDVH-guided cohort). The FDVH-guided cohort had lower excess of feasible dose to the contralateral parotid by 4.1Gy, the ipsilateral parotid by 10.6Gy, the larynx by 4.3Gy, the oral cavity by 1.5Gy, and the contralateral submandibular gland by 0.4Gy. Plan quality metrics were similar between the cohorts. Less variation of excess of feasible dose was seen in the FDVH-guided cohort for the parotid glands and contralateral submandibular gland (p<0.05). The average post-RT PROs were better in the FVHD cohort vs baseline (particularly at the 6 month time point for dry mouth frequency, sticky saliva, meal enjoyment, severity of pain, and EAT10 composite (swallowing); p<0.05).

CONCLUSIONS

Use of FDVH was associated with improved and less variable OAR sparing for clinically delivered plans. FDVH-guided patients had improved PROs compared to baseline with a variety of outcomes significantly improved at 6 months post-treatment.

Management and work-up procedures of patients with head and neck malignancies treated by radiation

Radiotherapy alone or in association with systemic treatment plays a major role in the treatment of head and neck tumours, either as a primary treatment or as a postoperative modality. The management of these tumours is multidisciplinary, requiring particular care at every treatment step. We present the update of the recommendations of the French Society of Radiation Oncology on the radiotherapy of head and neck tumours from the imaging work-up needed for optimal selection of treatment volume, to optimization of the dose distribution and delivery.

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.

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.

Systematic quantitative evaluation of Plan-IQ for intensity-modulated radiation therapy after modified radical mastectomy

Radiotherapy (RT) is one of the main treatment strategies of breast cancer. It is challenging to design RT plans that can completely cover the target area while protecting organs at risk (OAR). The Plan-IQ feasibility tool can estimate the best sparing dose of OAR before optimizing the Plan. A systematic quantitative evaluation of the quality change of intensity-modulated radiation therapy (IMRT) using the Plan-IQ feasibility tool was performed for modified radical mastectomy in this study. We selected 50 patients with breast cancer treated with IMRT. All patients received the same dose in the planning target volume (PTV). The plans are categorized into two groups, with each patient having one plan in each group: the clinically accepted normal plan group (NP group) and the repeat plan group (RP group). An automated planning strategy was generated using a Plan-IQ feasibility dose volume histogram (FDVH) in RP group. These plans were assessed according to the dosimetry parameters. A detailed scoring strategy was based on the RTOG9804 report and 2018 National Comprehensive Cancer Network guidelines, combined with clinical experience. PTV coverage in both groups was achieved at 100% of the prescribed dose. Except for the thyroid coverage, the dose limit of organs at risk (OAR) in RP group was significantly better than that in NP group. In the scoring analysis, the total scores of RP group decreased compared to that of NP group (P < 0.05), and the individual scores of PTV and OAR significantly changed. PTV scores in RP group decreased (P < 0.01); however, OAR scores improved (P < 0.01). The Plan-IQ FDVH was useful for evaluating a class solution for IMRT planning. Plan-IQ can automatically help physicians design the best OAR protection plan, which sacrifices part of PTV, but still meets clinical requirements.

Evaluation approach for whole dose distribution in clinical cases using spherical projection and spherical harmonics expansion: spherical coefficient tensor and score method

Whole dose distribution results from well-conceived treatment plans including patient-specific (location, size and shape of tumor, etc.) and facility-specific (clinical policy and goal, equipment, etc.) information. To evaluate the whole dose distribution efficiently and effectively, we propose a method to apply spherical projection and real spherical harmonics (SH) expansion, thus leading to the expanded coefficients as a rank-2 tensor, SH coefficient tensor, for every patient-specific dose distribution. To verify the feature of this tensor, we introduce Isomap from the manifold learning method and multi-dimensional scaling (MDS). Subsequently, we obtained the MDS distance representing similarity, η, and the SH score, ζ, which is a Frobenius norm of the SH coefficient tensor. These were then validated in the intensity-modulated radiation therapy (IMRT) data sets of: (i) 375 mixing treated regions, (ii) 135 head and neck (HN), and (iii) 132 prostate cases, respectively. The MDS map indicated that the SH coefficient tensor enabled a quantitative feature extraction of whole dose distributions. In particular, the SH score systematically detected irregular cases as the deviation higher than +1.5 standard deviations (SD) from the average case, which matched up with clinically irregular case that required very complicated dose distributions. In summary, the proposed SH coefficient tensor is a useful representation of the whole dose distribution. The SH score from the SH coefficient tensor is a convenient and simple criterion used to characterize the entire dose distributions, which is not dependent on the data set.

A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials

Background and purpose

The efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient‐specific anatomy. This study examined these two techniques for dose‐volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge‐based planning (KBP) technique and another first principle (FP) technique.

Methods

This analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi‐institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure.

Results

For the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re‐optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ‐at‐risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy.

Conclusions

Both methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles.

Medical Physics Practice Guideline (MPPG) 11.a: Plan and chart review in external beam radiotherapy and brachytherapy

A therapeutic medical physicist is responsible for reviewing radiation therapy treatment plans and patient charts, including initial treatment plans and new chart review, on treatment chart (weekly) review, and end of treatment chart review for both external beam radiation and brachytherapy. Task group report TG 275 examined this topic using a risk‐based approach to provide a thorough analysis and guidance for best practice. Considering differences in resources and workflows of various clinical practice settings, the Professional Council of the American Association of Physicists in Medicine assembled this task group to develop a practice guideline on the same topic to provide a minimum standard that balances an appropriate level of safety and resource utilization. This medical physics practice guidelines (MPPG) thus provides a concise set of recommendations for medical physicists and other clinical staff regarding the review of treatment plans and patient charts while providing specific recommendations about who to be involved, and when/what to check in the chart review process. The recommendations, particularly those related to the initial plan review process, are critical for preventing errors and ensuring smooth clinical workflow. We believe that an effective review process for high‐risk items should include multiple layers with collective efforts across the department. Therefore, in this report, we make specific recommendations for various roles beyond medical physicists. The recommendations of this MPPG have been reviewed and endorsed by the American Society of Radiologic Technologists and the American Association of Medical Dosimetrists.

Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study

Background

In radiation oncology, automation of treatment planning has reported the potential to improve plan quality and increase planning efficiency. We performed a comprehensive dosimetric evaluation of the new Personalized algorithm implemented in Pinnacle³ for full planning automation of VMAT prostate cancer treatments.

Material and Methods

Thirteen low-risk prostate (without lymph-nodes irradiation) and 13 high-risk prostate (with lymph-nodes irradiation) treatments were retrospectively taken from our clinical database and re-optimized using two different automated engines implemented in the Pinnacle treatment system. These two automated engines, the currently used Autoplanning and the new Personalized are both template-based algorithms that use a wish-list to formulate the planning goals and an iterative approach able to mimic the planning procedure usually adopted by experienced planners. In addition, the new Personalized module integrates a new engine, the Feasibility module, able to generate an “a priori” DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually generated (MP) and automated plans generated with both Autoplanning (AP) and Personalized engines (Pers) were performed using dose-volume histogram metrics and conformity indexes. Three different normal tissue complication probabilities (NTCPs) models were used for rectal toxicity evaluation. The planning efficiency and the accuracy of dose delivery were assessed for all plans.

Results

For similar targets coverage, Pers plans reported a significant increase of dose conformity and less irradiation of healthy tissue, with significant dose reduction for rectum, bladder, and femurs. On average, Pers plans decreased rectal mean dose by 11.3 and 8.3 Gy for low-risk and high-risk cohorts, respectively. Similarly, the Pers plans decreased the bladder mean doses by 7.3 and 7.6 Gy for low-risk and high-risk cohorts, respectively. The integral dose was reduced by 11–16% with respect to MP plans. Overall planning times were dramatically reduced to about 7 and 15 min for Pers plans. Despite the increased complexity, all plans passed the 3%/2 mm γ-analysis for dose verification.

Conclusions

The Personalized engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues for prostate cancer patients. The Feasibility “a priori” DVH prediction module provided OARs dose sparing well beyond the clinical objectives. The new Pinnacle Personalized algorithms outperformed the currently used Autoplanning ones as solution for treatment planning automation.

Lung dose reduction in patients with stage III non-small-cell lung cancer using software that estimates patient-specific dose reduction feasibility

PURPOSE

In radiotherapy, the dose to organs-at-risk must be kept as low as possible to preserve their function. We aimed to determine the acceptable f-value upper bound of the Feasibility DVH in the PlanIQ software to achieve dose reduction to the normal lung in patients with stage III non-small cell lung cancer.

METHODS

By using the Feasibility DVH, the f-values corresponding to the pulmonary dosimetric parameters of each treatment plan for 11 patients were calculated. The acceptable f-value upper bound was defined as the value that added one standard deviation of the f-value to the mean. Additionally, the treatment plan for additional fourteen patients was designed to evaluate the effectiveness of the acceptable f-value upper bound for the normal lung dose reduction. The value beyond the acceptable f-value upper bound was judged as inadequate dose reduction.

RESULTS

The acceptable f-value upper bound was different for dosimetric parameters (range, 0.22-0.26). These values were < 0.5, which is typically used as the acceptable f-value upper bound. Evaluation by the treatment plan of fourteen patients detected that the f-value of the normal lung of five patients exceeded the acceptable f-value upper bound, and the replanning was able to reduce the dose of the normal lung.

CONCLUSIONS

We could efficiently reduce the normal-lung dose using the acceptable f-value upper bound calculated in this study and provide an effective acceptable f-value upper bound of the normal lung dose in the lung cancer radiotherapy.

A cooperative game solution approach for intensity modulated radiation therapy design: Nash Social Welfare optimization

We study the fluency map optimization problem in Intensity Modulated Radiation Therapy (IMRT) from a cooperative game theory point of view. We consider the cancerous and healthy organs in a patient's body as players of a game, where cancerous organs seek to eliminate the cancerous cells and healthy organs seek to receive no harm. The goal is to balance the trade-offs between the utility of players by forming a grand coalition between them. We do so by proposing a methodology that solves a few convex optimization problems in order to transform the fluency map optimization problem into a bargaining game. To solve the bargaining game, we employ the concept of Nash Social Welfare (NSW) optimization due to the desirable efficiency and fairness properties of its outcomes. The proposed NSW optimization is convex and can be solved by powerful commercial solvers such as CPLEX. An additional advantage of the proposed approach is that it has a new control lever for the fluency map optimization, the so-called negotiation powers, which enables practitioners to put more emphasis on an organ by changing its negotiation power. To show the efficacy of our proposed methodology, we apply it to the TG-119 case and a liver case. We compare our proposed approach with a state-of-the-art approach through creating Dose Volume Histograms.

Personalized automation of treatment planning in head-neck cancer: A step forward for quality in radiation therapy?

PURPOSE

To perform a comprehensive dosimetric and clinical evaluation of the new Pinnacle Personalized automated planning system for complex head-and-neck treatments.

METHODS

Fifteen consecutive head-neck patients were enrolled. Radiotherapy was prescribed using VMAT with simultaneous integrated boost strategy. Personalized planning integrates the Feasibility engine able to supply an "a priori" DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually-generated (MP) and automated (AP) plans was performed using dose-volume histograms and a blinded clinical evaluation by two radiation oncologists. Planning time between MP and AP was compared. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array.

RESULTS

For similar targets coverage, AP plans reported less irradiation of healthy tissue, with significant dose reduction for spinal cord, brainstem and parotids. On average, the mean dose to parotids and maximal doses to spinal cord and brainstem were reduced by 13-15% (p < 0.001), 9% (p < 0.001) and 16% (p < 0.001), respectively. The integral dose was reduced by 16% (p < 0.001). The dose conformity for the three PTVs was significantly higher with AP plans (p < 0.001). The two oncologists chose AP plans in more than 80% of cases. Overall planning times were reduced to <30 min for automated optimization. All AP plans passed the 3%/2 mm γ-analysis by more than 95%.

CONCLUSION

Complex head-neck plans created using Personalized automated engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues. The Feasibility module allowed OARs dose sparing well beyond the clinical objectives.

Refining complex re-irradiation dosimetry through feasibility benchmarking and analysis for informed treatment planning

PURPOSE/OBJECTIVES

The purpose of this study is to dually evaluate the effectiveness of PlanIQ in predicting the viability and outcome of dosimetric planning in cases of complex re-irradiation as well as generating an equivalent plan through Pinnacle integration. The study also postulates that a possible strength of PlanIQ lies in mitigating pre-optimization uncertainties tied directly to dose overlap regions where re-irradiation is necessary.

METHODS

A retrospective patient selection (n = 20) included a diverse range of re-irradiation cases to be planned using Pinnacle auto-planning with PlanIQ integration. A consistent planning template was developed and applied across all cases. Direct plan comparisons of manual plans against feasibility-produced plans were performed by physician(s) with dosimetry recording relevant proximal OAR and planning timeline data.

RESULTS AND DISCUSSION

All re-irradiation cases were successfully predicted to be achievable per PlanIQ analyses with three cases (3/20) necessitating 95% target coverage conditions, previously exhibited in the manually planned counterparts, and determined acceptable under institutional standards. At the same time, PlanIQ consistently produced plans of equal or greater quality to the previously manually planned re-irradiation across all (20/20) trials (P = 0.05). Proximal OAR exhibited similar to slightly improved maximum point doses from feasibility-based planning with the largest advantages gained found within the subset of cranial and spine overlap cases, where improvements upward of 10.9% were observed. Mean doses to proximal tissues were found to be a statistically significant (P < 0.05) 5.0% improvement across the entire study. Documented planning times were markedly less than or equal to the time contributed to manual planning across all cases.

CONCLUSION

Initial findings indicate that PlanIQ effectively provides the user clear feasibility feedback capable of facilitating decision-making on whether re-irradiation dose objectives and prescription dose coverage are possible at the onset of treatment planning thus eliminating possible trial and error associated with some manual planning. Introducing model-based prediction tools into planning of complex re-irradiation cases yielded positive outcomes on the final treatment plans.

An Automated Workflow to Improve Efficiency in Radiation Therapy Treatment Planning by Prioritizing Organs at Risk

PURPOSE

Manual delineation (MD) of organs at risk (OAR) is time and labor intensive. Auto-delineation (AD) can reduce the need for MD, but because current algorithms are imperfect, manual review and modification is still typically used. Recognizing that many OARs are sufficiently far from important dose levels that they do not pose a realistic risk, we hypothesize that some OARs can be excluded from MD and manual review with no clinical effect. The purpose of this study was to develop a method that automatically identifies these OARs and enables more efficient workflows that incorporate AD without degrading clinical quality.

METHODS AND MATERIALS

Preliminary dose map estimates were generated for n = 10 patients with head and neck cancers using only prescription and target-volume information. Conservative estimates of clinical OAR objectives were computed using AD structures with spatial expansion buffers to account for potential delineation uncertainties. OARs with estimated dose metrics below clinical tolerances were deemed low priority and excluded from MD and/or manual review. Final plans were then optimized using high-priority MD OARs and low-priority AD OARs and compared with reference plans generated using all MD OARs. Multiple different spatial buffers were used to accommodate different potential delineation uncertainties.

RESULTS

Sixty-seven out of 201 total OARs were identified as low-priority using the proposed methodology, which permitted a 33% reduction in structures requiring manual delineation/review. Plans optimized using low-priority AD OARs without review or modification met all planning objectives that were met when all MD OARs were used, indicating clinical equivalence.

CONCLUSIONS

Prioritizing OARs using estimated dose distributions allowed a substantial reduction in required MD and review without affecting clinically relevant dosimetry.

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.

Personalized setting of plan parameters using feasibility dose volume histogram for auto-planning in Pinnacle system

Purpose

The personalized setting of plan parameters in the Auto‐Planning module of the Pinnacle treatment planning system (TPS) using the PlanIQ feasibility tool was evaluated for lung cancer conventional fractionated radiotherapy (CFRT).

Materials and method

We reviewed the records of ten patients with lung cancer who were treated with volumetric modulated arc therapy (VMAT). Three plans were designed for each patient: the clinically accepted manual plan (MP) and two automatic plans including one generated using the generic plan parameters in technique script (AP1) and the other generated using personalized plan parameters derived based on feasibility dose volume histogram (FDVH) in PlanIQ (AP2). The plans were assessed according to the dosimetric parameters, monitor units, and planning time. A plan quality metric (PQM) was defined according to the clinical requirements for plan assessment.

Results

AP2 achieved better lung sparing than AP1 and MP. The PQM value of AP2 (52.5 ± 14.3) was higher than those of AP1 (49.2 ± 16.2) and MP (44.8 ± 16.9) with P < 0.05. The monitor units of AP2 (585.9 ± 142.9 MU) was higher than that of AP1 (511.1 ± 136.5 MU) and lower than that of MP (632.8 ± 143.8 MU) with p < 0.05. The planning time of AP2 (33.2 ± 4.8 min) was slightly higher than that of AP1 (28.2 ± 4.0 min) and substantially lower than that of MP (72.9 ± 28.5 min) with P < 0.05.

Conclusions

The Auto‐Planning module of the Pinnacle system using personalized plan parameters suggested by the PlanIQ Feasibility tool provides superior quality for lung cancer plans, especially in terms of lung sparing. The time consumption of Auto‐Planning was slightly higher with the personalized parameters compared to that with the generic parameters, but significantly lower than that for the manual plan.

Evaluation of Elements Spine SRS Plan Quality for SRS and SBRT Treatment of Spine Metastases

Purpose: The Elements Spine Stereotactic Radiosurgery treatment planning system uses automated volumetric modulated arc radiotherapy that can provide a highly conformal dose distribution to targets, which can provide superior sparing of the spinal cord. This study compares the dosimetric quality of Elements plans with the clinical plans of 20 spine stereotactic radiosurgery/stereotactic body radiation therapy (SRS/SBRT) patients treated at our institution.

Methods: Twenty spine SRS/SBRT patients who were clinically treated at our institution were replanned using the automated Elements planning workflow with prespecified templates. Elements automatically evaluates the size and shape of the target to determine if splitting the PTV into simplistic subvolumes, each treated by their own arc(s), would increase conformity and spinal cord sparing. The conformity index, gradient index, PTV D_5%, and maximum and mean cord dose were evaluated for the Elements and clinical plans. Treatment delivery efficiency was also analyzed by comparing the total number of monitor units and the modulation factor. Wilcoxon rank-sum tests were performed on the statistics.

Results: Elements split the PTV for 50% of cases, requiring four or six arcs. Overall, Elements plans were found to be superior to clinical plans in conformity index, gradient index, and maximum cord dose. The PTV D_5% and cord mean dose for the Elements plans trended higher and lower, respectively. The numbers of monitor units and modulation factor were also higher for Elements plans, although the differences were not significant.

Conclusion: Automated Elements plans achieved superior conformity and cord dose sparing compared to clinical plans and PTV splitting successfully improved spinal cord sparing.

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works.

Evaluation of plan quality improvements in PlanIQ-guided Autoplanning

Aim

Philips recently integrated PlanIQ with Autoplan® in Pinnacle³ TPS (V16.2). The objective of the present work is to quantitatively demonstrate how this integration improves the plan quality.

Background

Pinnacle³ Autoplan® is the tool that generates the treatment plans with clinically acceptable plan quality with less manual intervention. In the recent past, a new tool called PlanIQ (Sun Nuclear Corp.) was introduced for a priori estimation of the best possible sparing of an organ at risk (OAR) for a given patient anatomy. Philips has recently integrated PlanIQ tool with Autoplan® for a seamless and efficient planning workflow.

Materials and methods

We have performed this evaluation in Pinnacle³ TPS (V.16.2) for the VMAT treatment technique. All plans were created using Varian True beam machine with the dual arc technique. Basically, we created two sets of VMAT plans using 6 MV photons. In the first set of VMAT plans (AP_RTOG), we used OAR goals from either RTOG guidelines to perform optimization using Autoplan®. Subsequently, we exported the same dataset to the PlanIQ system to perform feasibility analysis on the OAR goals. These newly obtained OAR goals from PlanIQ were used to generate the other set of plans (AP_PlanIQ plans). We compared the dosimetric results from these two sets of plans in five cases, such as brain, head & neck, lung, abdomen and prostate.

Results

We compared the dosimetric results for AP_RTOG and AP_PlanIQ plans. We used RTOG guidelines to evaluate the plans and observed that while both sets of plans were meeting the RTOG guidelines in terms of OAR sparing, the AP_PlanIQ plans were significantly better in terms of OAR sparing as compared to AP_RTOG plans without any compromise in the target coverage.

Conclusion

The results indicate that, although Autoplan helps achieve the user-defined goals without much manual intervention, the plan quality (OAR sparing) can be significantly improved without taking many iterative steps when PlanIQ suggested clinical goals are used in the Autoplan-based optimization.

Advances in knowledge

At present, there are no published material available about the efficacy of the integration of PlanIQ with Autoplanning®. In the present work, our objective is to evaluate the improvements in plan quality resulting from this integration.

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.

Real-time interactive planning for radiotherapy of head and neck cancer with volumetric modulated arc therapy

Background and purpose

Planning complex radiotherapy treatments can be inefficient, with large variation in plan quality. In this study we evaluated plan quality and planning efficiency using real-time interactive planning (RTIP) for head and neck (HN) volumetric modulated arc therapy (VMAT).

Materials and methods

RTIP allows manipulation of dose volume histograms (DVHs) in real-time to assess achievable planning target volume (PTV) coverage and organ at risk (OAR) sparing. For 20 HN patients previously treated with VMAT, RTIP was used to minimize OAR dose while maintaining PTV coverage. RTIP DVHs were used to guide VMAT optimization. Dosimetric differences between RTIP-assisted plans and original clinical plans were assessed. Five blinded radiation oncologists indicated their preference for each PTV, OAR and overall plan. To assess efficiency, ten patients were planned de novo by experienced and novice planners and a RTIP user.

Results

The average planning time with RTIP was <20 min, and most plans required only one optimization. All 20 RTIP plans were preferred by a majority of oncologists due to improvements in OAR sparing. The average maximum dose to the spinal cord was reduced by 10.5 Gy (from 49.5 to 39.0 Gy), and the average mean doses for the oral cavity, laryngopharynx, contralateral parotid and submandibular glands were reduced by 3.5 Gy (39.1-35.7 Gy), 6.8 Gy (42.5-35.7 Gy), 1.7 Gy (17.0-15.3 Gy) and 3.3 Gy (22.9-19.5 Gy), respectively.

Conclusions

Incorporating RTIP into clinical workflows may increase both planning efficiency and OAR sparing.

Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning

PURPOSE

Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output parameters. Here, we use planning target volume (PTV)-only treatment plans of the patients (i.e., the achievable dose distribution in the absence of organs-at-risk (OAR) constraints) to estimate the potentially achievable quality of treatment plans and establish a machine learning-based DVH prediction framework with the use of the dosimetric metric as model input parameters.

METHODS

A support vector regression (SVR) approach was used as the backbone of our machine learning model. A database containing volumetric modulated arc therapy (VMAT) plans of 63 prostate cancer patients were used. For each patient, the PTV-only plan was generated first. A correlative relationship between the OAR DVH of the PTV-only plan (model input) and the corresponding DVH of the clinical treatment plan (CTP) (model output) was then established with the 53 training cases. The prediction model was tested by the validation cohort of ten cases.

RESULTS

For the training cohort, the checks of dosimetric endpoints (DEs) indicated that 52 of 53 plans (98%) were within the 10% error bound for bladder, and 45 of 53 plans (85%) were within the 10% error bound for rectum. In the validation tests, 92% and 96% of the DEs were within the 10% error bounds for bladder and rectum, respectively, and eight of ten validation plans (80%) were within the 10% error bound for both the bladder and rectum. The sum of absolute residuals (SAR) achieved a mean of 0.034 ± 0.028 and 0.046 ± 0.021 for the bladder and rectum, respectively.

CONCLUSIONS

A novel dosimetric features-driven machine learning model with the use of PTV-only plan has been established for DVH prediction. The framework is capable of efficiently generating best achievable DVHs for VMAT planning.

Advanced Treatment Planning

Treatment planning for protons and heavier ions is adapting technologies originally developed for photon dose optimization, but also has to meet its particular challenges. Since the quality of the applied dose is more sensitive to geometric uncertainties, treatment plan robust optimization has a much more prominent role in particle therapy. This has led to specific planning tools, approaches, and research into new formulations of the robust optimization problems. Tools for solution space navigation and automatic planning are also being adapted to particle therapy. These challenges become even greater when detailed models of relative biological effectiveness (RBE) are included into dose optimization, as is required for heavier ions.

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.

NCTN clinical trial standardization for radiotherapy through IROC and CIRO

Evidence-based practice is the cornerstone of modern medicine. Randomized clinical trials across multiple institutions are the gold standard for modern evidence collection. National Cancer Trials Network (NCTN) instruments the clinical trials through the new infrastructure for improvements in cancer treatment. Radiation therapy is an integral component of cancer treatment and is involved in many of the NCTN clinical trials. Radiotherapy is experiencing exciting developments in new treatment modalities and multi-modality image guidance. One of NCTN network groups NRG Oncology brings together the research areas of the National Surgical Adjuvant Breast and Bowel Project (NSABP), the Radiation Therapy Oncology Group (RTOG), and the Gynecologic Oncology Group (GOG). The Imaging and Radiation Oncology Core (IROC) and Center for Innovation in Radiation Oncology(CIRO) of NRG Oncology complement each other's functions in development and implementation of the new radiotherapy and imaging technologies in clinical trials with standardization and other strategies for quality. The standardization process is the essential step to make the data collected for clinical trials of high quality, interoperable, and reusable.

Efficiently train and validate a RapidPlan model through APQM scoring

PURPOSE

The aim of this study was to propose and validate an intuitive method for training and to validate knowledge-based planning (KBP) systems based on a patient-specific plan quality scoring.

METHODS

A sample of 80 clinical plans of prostate cancer patients were ranked on the basis of the Adjusted Plan Quality Metric (APQM%). This quality metric was computed normalizing the Plan Quality Metric (PQM%) score to the best possible OAR sparing estimated by the Feasibility DVH (FDVH) algorithm. Two different plan libraries were created, purging all the plans below the first quartile or below the median the APQM% distribution. These libraries were used to populate and train two RapidPlan models: respectively, the APMQ_25% and the APMQ_50% models. No further refinements or actions were undertaken on these two models. Their performances were benchmarked against another two RapidPlan models. An Uncleaned model, which was populated and trained with the initial sample of 80 plans, and a Cleaned model, obtained through the standard iterative cleaning and refinement process suggested by the vendor and in literature. The outcomes of a planning test based on 20 patients within the training library (closed loop) and 20 patients outside of the training library (open-loop) were compared through various DVH metrics and the PQM% score.

RESULTS

The selection through APQM% thresholding roughly preserves the geometric variety of the Cleaned model; only the APMQ_50% model showed a modest broadness reduction. The models generated through APQM% thresholding showed target coverage and OARs sparing equal or superior to the Uncleaned and Cleaned models both for the closed- and the open-loop tests. No significant differences were found between the four models. PQM% analysis ranked the overall plan quality as: 86.5 ± 6.5% APQM_50% , 83.1 ± 5.9% APQM_25% , 80.39 ± 10.6% Cleaned and 79.4 ± 8.5% Uncleaned in the closed-loop test; 84.9 ± 7.6% APQM_50% , 82.6 ± 7.9% APQM_25% , 80.39 ± 10.6% Cleaned and 79.4 ± 8.5% Uncleaned in the open-loop test.

CONCLUSIONS

Forward feeding a RapidPlan model through a thresholding selection based on APQM% is proven to produce equal or better results than a model based on a manually and iteratively refined population. A tighter APQM% threshold turns approximately into a higher average quality of plans generated with RapidPlan. A trade-off must be found between the mean quality of the KBP library and its numerosity. The proposed KBP feeding method helps the KBP user, because it makes the model refinement more intuitive and less time consuming.