Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data

PurposeA three-dimensional deep generative adversarial network (GAN) was used to predict dose distributions for locally advanced head and neck cancer radiotherapy. Given the labor- and time-intensive nature of manual planning target volume (PTV) and organ-at-risk (OAR) segmentation, we investigated whether dose distributions could be predicted without the need for fully segmented datasets.Materials and methodsGANs were trained/validated/tested using 320/30/35 previously segmented CT datasets and treatment plans. The following input combinations were used to train and test the models: CT-scan only (C); CT+PTVboost/elective (CP); CT+PTVs+OARs+body structure (CPOB); PTVs+OARs+body structure (POB); PTVs+body structure (PB). Mean absolute errors (MAEs) for the predicted dose distribution and mean doses to individual OARs (individual salivary glands, individual swallowing structures) were analyzed.ResultsFor the five models listed, MAEs were 7.3 Gy, 3.5 Gy, 3.4 Gy, 3.4 Gy, and 3.5 Gy, respectively, without significant differences among CP-CPOB, CP-POB, CP-PB, among CPOB-POB. Dose volume histograms showed that all four models that included PTV contours predicted dose distributions that had a high level of agreement with clinical treatment plans. The best model CPOB and the worst model PB (except model C) predicted mean dose to within ±3 Gy of the clinical dose, for 82.6%/88.6%/82.9% and 71.4%/67.1%/72.2% of all OARs, parotid glands (PG), and submandibular glands (SMG), respectively. The R2 values (0.17/0.96/0.97/0.95/0.95) of OAR mean doses for each model also indicated that except for model C, the predictions correlated highly with the clinical dose distributions. Interestingly model C could reasonably predict the dose in eight patients, but on average, it performed inadequately.ConclusionWe demonstrated the influence of the CT scan, and PTV and OAR contours on dose prediction. Model CP was not statistically different from model CPOB and represents the minimum data statistically required to adequately predict the clinical dose distribution in a group of patients

National Protocol for Model-Based Selection for Proton Therapy in Head and Neck Cancer

In the Netherlands, the model-based approach is used to identify patients with head and neck cancer who may benefit most from proton therapy in terms of prevention of late radiation-induced side effects in comparison with photon therapy. To this purpose, a National Indication Protocol Proton therapy for Head and Neck Cancer patients (NIPP-HNC) was developed, which has been approved by the health care authorities. When patients qualify according to the guidelines of the NIPP-HNC, proton therapy is fully reimbursed. This article describes the procedures that were followed to develop this NIPP-HNC and provides all necessary information to introduce model-based selection for patients with head and neck cancer into routine clinical practice.</p

Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning

Knowledge-based planning solutions have brought significant improvements in treatment planning. However, the performance of a proton-specific knowledge-based planning model in creating knowledge-based plans (KBPs) with beam angles differing from those used to train the model remains unexplored. We used a previously validated RapidPlanPT model and scripting to create nine KBPs, one with default and eight with altered beam angles, for 10 recent oropharynx cancer patients. The altered-angle plans were compared against the default-angle ones in terms of grade 2 dysphagia and xerostomia normal tissue complication probability (NTCP), mean doses of several organs at risk, and dose homogeneity index (HI). As KBP could be suboptimal, a proof of principle automatic iterative optimizer (AIO) was added with the aim of reducing the plan NTCP. There were no statistically significant differences in NTCP or HI between default- and altered-angle KBPs, and the altered-angle plans showed a &lt;1% reduction in NTCP. AIO was able to reduce the sum of grade 2 NTCPs in 66/90 cases with mean a reduction of 3.5 &plusmn; 1.8%. While the altered-angle plans saw greater benefit from AIO, both default- and altered-angle plans could be improved, indicating that the KBP model alone was not completely optimal to achieve the lowest NTCP. Overall, the data showed that the model was robust to the various beam arrangements within the range described in this analysis

Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning

Knowledge-based planning solutions have brought significant improvements in treatment planning. However, the performance of a proton-specific knowledge-based planning model in creating knowledge-based plans (KBPs) with beam angles differing from those used to train the model remains unexplored. We used a previously validated RapidPlanPT model and scripting to create nine KBPs, one with default and eight with altered beam angles, for 10 recent oropharynx cancer patients. The altered-angle plans were compared against the default-angle ones in terms of grade 2 dysphagia and xerostomia normal tissue complication probability (NTCP), mean doses of several organs at risk, and dose homogeneity index (HI). As KBP could be suboptimal, a proof of principle automatic iterative optimizer (AIO) was added with the aim of reducing the plan NTCP. There were no statistically significant differences in NTCP or HI between default-and altered-angle KBPs, and the altered-angle plans showed a <1% reduction in NTCP. AIO was able to reduce the sum of grade 2 NTCPs in 66/90 cases with mean a reduction of 3.5 ± 1.8%. While the altered-angle plans saw greater benefit from AIO, both default-and altered-angle plans could be improved, indicating that the KBP model alone was not completely optimal to achieve the lowest NTCP. Overall, the data showed that the model was robust to the various beam arrangements within the range described in this analysis

Is accurate contouring of salivary and swallowing structures necessary to spare them in head and neck VMAT plans?

Background and purpose: Current standards for organ-at-risk (OAR) contouring encourage anatomical accuracy which can be resource intensive. Certain OARs may be suitable for alternative delineation strategies. We investigated whether simplified salivary and swallowing structure contouring can still lead to good OAR sparing in automated head and neck cancer (HNC) plans. Materials and methods: For 15 HNC patients, knowledge-based plans (KBPs) using RapidPlan™ were created using: (1) standard clinical contours for all OARs (benchmark-plans), (2) automated knowledge-based contours for the salivary glands, with standard contours for the remaining OARs (SS-plans) and (3) simplified contours (SC-plans) consisting of quick-to-draw tubular structures to account for the oral cavity, salivary glands and swallowing muscles. Individual clinical OAR contours in a RapidPlan™ model were combined to create composite salivary/swallowing structures. These were matched to tube-contours to create SC-plans. All plans were compared based on dose to anatomically accurate clinical OAR contours. Results: Salivary gland delineation in SS-plans required on average 2 min, compared with 7 min for manual delineation of all tubular-contours. Automated atlas-based contours overlapped with, on average, 71% of clinical salivary gland contours while tube-contours overlapped with 95%/75%/93% of salivary gland/oral cavity/swallowing structure contours. On average, SC-plans were comparable to benchmark-plans and SS-plans, with average differences in composite salivary and swallowing structure dose ≤2 Gy and <1 Gy respectively. Conclusions: Simplified-contours could be created quickly and resulted in clinically acceptable HNC VMAT plans. They can be combined with automated planning to facilitate the implementation of advanced radiotherapy, even when resources are limited

Single-fraction 34 Gy Lung Stereotactic Body Radiation Therapy Using Proton Transmission Beams: FLASH-dose Calculations and the Influence of Different Dose-rate Methods and Dose/Dose-rate Thresholds

Purpose: Research suggests that in addition to the dose-rate, a dose threshold is also important for the reduction in normal tissue toxicity with similar tumor control after ultrahigh dose-rate radiation therapy (UHDR-RT). In this analysis we aimed to identify factors that might limit the ability to achieve this “FLASH”-effect in a scenario attractive for UHDR-RT (high fractional beam dose, small target, few organs-at-risk): single-fraction 34 Gy lung stereotactic body radiation therapy. Methods and Materials: Clinical volumetric-modulated arc therapy (VMAT) plans, intensity modulated proton therapy (IMPT) plans and transmission beam (TB) plans were compared for 6 small and 1 large lung lesion. The TB-plan dose-rate was calculated using 4 methods and the FLASH-percentage (percentage of dose delivered at dose-rates ≥40/100 Gy/s and ≥4/8 Gy) was determined for various variables: a minimum spot time (minST) of 0.5/2 ms, maximum nozzle current (maxN) of 200/40 0nA, and 2 gantry current (GC) techniques (energy-layer based, spot-based [SB]). Results: Based on absolute doses 5-beam TB and VMAT-plans are similar, but TB-plans have higher rib, skin, and ipsilateral lung dose than IMPT. Dose-rate calculation methods not considering scanning achieve FLASH-percentages between ∼30% to 80%, while methods considering scanning often achieve <30%. FLASH-percentages increase for lower minST/higher maxN and when using SB GC instead of energy-layer based GC, often approaching the percentage of dose exceeding the dose-threshold. For the small lesions average beam irradiation times (including scanning) varied between 0.06 to 0.31 seconds and total irradiation times between 0.28 to 1.57 seconds, for the large lesion beam times were between 0.16 to 1.47 seconds with total irradiation times of 1.09 to 5.89 seconds. Conclusions: In a theoretically advantageous scenario for FLASH we found that TB-plan dosimetry was similar to that of VMAT, but inferior to that of IMPT, and that decreasing minST or using SB GC increase the estimated amount of FLASH. For the appropriate machine/delivery parameters high enough dose-rates can be achieved regardless of calculation method, meaning that a possible FLASH dose-threshold will likely be the primary limiting factor

Verifying tumor position during stereotactic body radiation therapy delivery using (limited-arc) cone beam computed tomography imaging

BACKGROUND AND PURPOSE: Proof of tumor position during stereotactic body radiotherapy (SBRT) delivery is desirable. We investigated if cone-beam CT (CBCT) scans reconstructed from (collimated) fluoroscopic kV images acquired during irradiation could show the dominant tumor position. MATERIALS AND METHODS: Full-arc CBCT scans were reconstructed using FDK filtered back projection from 38kV fluoroscopy datasets (16 patients) continuously acquired during volumetric modulated spine SBRT. CBCT-CT match values were compared to the average spine offset values found using template matching+triangulation of the individual kV images. Multiple limited-arc CBCTs were reconstructed from fluoroscopic images acquired during lung SBRT of an anthropomorphic thorax phantom using 20-180° arc lengths and for 3 breath-hold lung SBRT patients. RESULTS: Differences between 3D CBCT-CT match results and average spine offsets found using template matching+triangulation were 0.1±0.1mm for all directions (range: 0.0-0.5mm). For limited-arc CBCTs of the thorax phantom, the automatic 3D CBCT-CT match results for arc lengths of 80-180° were ≤1mm. 20° CBCT reconstruction still allowed for positional verification in 2D. CONCLUSIONS: (Limited-arc) CBCT reconstructions of kV images acquired during irradiation can identify the dominant position of the tumor during treatment delivery