Focused VHEE (very high energy electron) beams and dose delivery for radiotherapy applications

This paper presents the first demonstration of deeply penetrating dose delivery using focused very high energy electron (VHEE) beams using quadrupole magnets in Monte Carlo simulations. We show that the focal point is readily modified by linearly changing the quadrupole magnet strength only. We also present a weighted sum of focused electron beams to form a spread-out electron peak (SOEP) over a target region. This has a significantly reduced entrance dose compared to a proton-based spread-out Bragg peak (SOBP). Very high energy electron (VHEE) beams are an exciting prospect in external beam radiotherapy. VHEEs are less sensitive to inhomogeneities than proton and photon beams, have a deep dose reach and could potentially be used to deliver FLASH radiotherapy. The dose distributions of unfocused VHEE produce high entrance and exit doses compared to other radiotherapy modalities unless focusing is employed, and in this case the entrance dose is considerably improved over existing radiations. We have investigated both symmetric and asymmetric focusing as well as focusing with a range of beam energies.

An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy

INTRODUCTION

Auto contouring models help consistently define volumes and reduce clinical workload. This study aimed to evaluate the cross acquisition of a Magnetic Resonance (MR) deep learning auto contouring model for organ at risk (OAR) delineation in head and neck radiotherapy.

METHODS

Two auto contouring models were evaluated using deep learning contouring expert (DLCExpert) for OAR delineation: a CT model (modelCT) and an MR model (modelMRI). Models were trained to generate auto contours for the bilateral parotid glands and submandibular glands. Auto-contours for modelMRI were trained on diagnostic images and tested on 10 diagnostic, 10 MR radiotherapy planning (RTP), eight MR-Linac (MRL) scans and, by modelCT, on 10 CT planning scans. Goodness of fit scores, dice similarity coefficient (DSC) and distance to agreement (DTA) were calculated for comparison.

RESULTS

ModelMRI contours improved the mean DSC and DTA compared with manual contours for the bilateral parotid glands and submandibular glands on the diagnostic and RTP MRs compared with the MRL sequence. There were statistically significant differences seen for modelMRI compared to modelCT for the left parotid (mean DTA 2.3 v 2.8 mm), right parotid (mean DTA 1.9 v 2.7 mm), left submandibular gland (mean DTA 2.2 v 2.4 mm) and right submandibular gland (mean DTA 1.6 v 3.2 mm).

CONCLUSION

A deep learning MR auto-contouring model shows promise for OAR auto-contouring with statistically improved performance vs a CT based model. Performance is affected by the method of MR acquisition and further work is needed to improve its use with MRL images.

Inter- and intra-fractional stability of rectal gas in pelvic cancer patients during MRIgRT

PURPOSE

Due to the electron return effect (ERE) during magnetic resonance imaging guided radiotherapy (MRIgRT), rectal gas during pelvic treatments can result in hot spots of over-dosage in the rectal wall. Determining the clinical impact of this effect on rectal toxicity requires estimation of the amount and mobility (and stability) of rectal gas during treatment. We therefore investigated the amount of rectal gas and local inter- and intra-fractional changes of rectal gas in pelvic cancer patients.

METHODS

To estimate the volume of gas present at treatment planning, the rectal gas contents in the planning computed tomography (CT) scans of 124 bladder, 70 cervical and 2180 prostate cancer patients were calculated. To estimate inter- and intra-fractional variations in rectal gas, 174 and 131 T2-w MRIs for six cervical and eleven bladder cancer patients were used. These scans were acquired during four scan-sessions (~20-25 min each) at various time-points. Additionally, 258 T2-w MRIs of the first five prostate cancer patients treated using MRIgRT at our center, acquired during each fraction, were analyzed. Rectums were delineated on all scans. The area of gas within the rectum delineations was identified on each MRI slice using thresholding techniques. The area of gas on each slice of the rectum was used to calculate the inter- and intra-fractional group mean, systematic and random variations along the length of the rectum. The cumulative dose perturbation as a result of the gas was estimated. Two approaches were explored: accounting or not accounting for the gas at the start of the scan-session.

RESULTS

Intra-fractional variations in rectal gas are small compared to the absolute volume of rectal gas detected for all patient groups. That is, rectal gas is likely to remain stable for periods of 20-25 min. Larger volumes of gas and larger variations in gas volume were observed in bladder cancer patients compared with cervical and prostate cancer patients. For all patients, local cumulative dose perturbations per beam over an entire treatment in the order of 60 % were estimated when gas had not been accounted for in the daily adaption. The calculated dose perturbation over the whole treatment was dramatically reduced in all patients when accounting for the gas in the daily set-up image.

CONCLUSION

Rectal gas in pelvic cancer patients is likely to remain stable over the course of an MRIgRT fraction, and also likely to reappear in the same location in multiple fractions, and can therefore result in clinically relevant over-dosage in the rectal wall. The over-dosage is reduced when accounting for gas in the daily adaption.

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy

To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed from limited information. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can however lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs (n = 142) of recently-treated childhood cancer patients were gathered, their organs at risk were segmented, and 300 artificial Wilms' tumor plans were sampled automatically. Each artificial plan was automatically emulated on the 142 CTs, resulting in 42,600 3D dose distributions from which dose-volume metrics were derived. Anatomical features were extracted from digitally reconstructed radiographs simulated from the CTs to resemble historical radiographs. Further, patient and radiotherapy plan features typically available from historical treatment records were collected. An evolutionary ML algorithm was then used to link features to dose-volume metrics. Besides 5-fold cross validation, a further evaluation was done on an independent dataset of five CTs each associated with two clinical plans. Cross-validation resulted in mean absolute errors ≤ 0.6 Gy for organs completely inside or outside the field. For organs positioned at the edge of the field, mean absolute errors ≤ 1.7 Gy for [Formula: see text], ≤ 2.9 Gy for [Formula: see text], and ≤ 13% for [Formula: see text] and [Formula: see text], were obtained, without systematic bias. Similar results were found for the independent dataset. To conclude, we proposed a novel organ dose reconstruction method that uses ML models to predict dose-volume metric values given patient and plan features. Our approach is not only accurate, but also efficient, as the setup of a surrogate is no longer needed.

Evaluation of Ultra-low-dose Paediatric Cone-beam Computed Tomography for Image-guided Radiotherapy

AIMS

In image-guided radiotherapy, daily cone-beam computed tomography (CBCT) is rarely applied to children due to concerns over imaging dose. Simulating low-dose CBCT can aid clinical protocol design by allowing visualisation of new scan protocols in patients without delivering additional dose. This work simulated ultra-low-dose CBCT and evaluated its use for paediatric image-guided radiotherapy by assessment of image registration accuracy and visual image quality.

MATERIALS AND METHODS

Ultra-low-dose CBCT was simulated by adding the appropriate amount of noise to projection images prior to reconstruction. This simulation was validated in phantoms before application to paediatric patient data. Scans from 20 patients acquired at our current clinical protocol (0.8 mGy) were simulated for a range of ultra-low doses (0.5, 0.4, 0.2 and 0.125 mGy) creating 100 scans in total. Automatic registration accuracy was assessed in all 100 scans. Inter-observer registration variation was next assessed for a subset of 40 scans (five scans at each simulated dose and 20 scans at the current clinical protocol). This subset was assessed for visual image quality by Likert scale grading of registration performance and visibility of target coverage, organs at risk, soft-tissue structures and bony anatomy.

RESULTS

Simulated and acquired phantom scans were in excellent agreement. For patient scans, bony atomy registration discrepancies for ultra-low-dose scans fell within 2 mm (translation) and 1° (rotation) compared with the current clinical protocol, with excellent inter-observer agreement. Soft-tissue registration showed large discrepancies. Bone visualisation and registration performance reached over 75% acceptability (rated 'well' or 'very well') down to the lowest doses. Soft-tissue visualisation did not reach this threshold for any dose.

CONCLUSION

Ultra-low-dose CBCT was accurately simulated and evaluated in patient data. Patient scans simulated down to 0.125 mGy were appropriate for bony anatomy set-up. The large dose reduction could allow for more frequent (e.g. daily) image guidance and, hence, more accurate set-up for paediatric radiotherapy.

Impact of Peer Review in Reducing Uncertainty in the Definition of the Lung Target Volume Among Trainee Oncologists

AIMS

To evaluate the impact of peer review and contouring workshops on reducing uncertainty in target volume delineation for lung cancer radiotherapy.

MATERIALS AND METHODS

Data from two lung cancer target volume delineation courses were analysed. In total, 22 trainees in clinical oncology working across different UK centres attended these courses with priori experience in lung cancer radiotherapy. The courses were made up of short presentations and contouring practice sessions. The participants were divided into two groups and asked to first individually delineate (IND) and then individually peer review (IPR) the contours of another participant. The contours were discussed with an expert panel consisting of two consultant clinical oncologists and a consultant radiologist. Contours were analysed quantitatively by measuring the volume and local distance standard deviation (localSD) from the reference expert consensus contour and qualitatively through visual analysis. Feedback from the participants was obtained using a questionnaire.

RESULTS

All participants applied minor editing to the contours during IPR, leading to a non-statistically significant reduction in the mean delineated volume (IND = 140.92 cm3, IPR = 125.26 cm3, P = 0.211). The overall interobserver variation was similar, with a localSD of 0.33 cm and 0.38 cm for the IND and IPR, respectively (P = 0.848). Six participants (29%) carried out correct major changes by either including tumour or excluding healthy tissue. One participant (5%) carried out an incorrect edit by excluding parts of the tumour, while another observer failed to identify a major contour error. The participants' level of confidence in target volume delineation increased following the course and identified the discussions with the radiologist and colleagues as the most important highlights of the course.

CONCLUSION

IPR could improve target volume delineation quality among trainee oncologists by identifying most major contour errors. However, errors were also introduced after IPR, suggesting the need to further discuss major changes with a multidisciplinary team.

Experimental verification the electron return effect around spherical air cavities for the MR-Linac using Monte Carlo calculation

PURPOSE

Dose deposition around unplanned air cavities during magnetic resonance-guided radiotherapy (MRgRT) is influenced by the electron return effect (ERE). This is clinically relevant for gas forming close to or inside organs at risk (OARs) that lie in the path of a single beam, for example, intestinal track during pelvic treatment. This work aims to verify Monte Carlo calculations that predict the dosimetric effects of ERE around air cavities. For this, we use GafChromic EBT3 film inside poly-methyl methacrylate (PMMA) -air phantoms.

METHOD

Four PMMA phantoms were produced. Three of the phantoms contained centrally located spherical air cavities (0.5, 3.5, 7.5 cm diameter), and one phantom contained no air. The phantoms were split to sandwich GafChromic EBT3 film in the center. The phantoms were irradiated on an Elekta Unity system using a single 10 × 10 cm2 7-MV photon beam under the influence of a 1.5-T transverse magnetic field. The measurements were replicated using the Elekta Monaco treatment planning system (TPS). Gamma analysis with pass criteria 3%/3 mm was used to compare the measured and calculated dose distributions. We also consider 3%/2 mm, 2%/3 mm, and 2%/2 mm pass criteria for interest.

RESULTS

The gamma analysis showed that >95% of the points agreed between the TPS-calculated and measured dose distributions, using 3%/3 mm criteria. The phantom containing the largest air cavity had the lowest agreement, with most of the disagreeing points lying inside the air cavity (dose to air region).

CONCLUSIONS

The dose effects due to ERE around spherical air cavities are being calculated in the TPS with sufficient accuracy for clinical use.

Non-linear registration improves statistical power to detect hippocampal atrophy in aging and dementia

OBJECTIVE

To compare the performance of different methods for determining hippocampal atrophy rates using longitudinal MRI scans in aging and Alzheimer's disease (AD).

BACKGROUND

Quantifying hippocampal atrophy caused by neurodegenerative diseases is important to follow the course of the disease. In dementia, the efficacy of new therapies can be partially assessed by measuring their effect on hippocampal atrophy. In radiotherapy, the quantification of radiation-induced hippocampal volume loss is of interest to quantify radiation damage. We evaluated plausibility, reproducibility and sensitivity of eight commonly used methods to determine hippocampal atrophy rates using test-retest scans.

MATERIALS AND METHODS

Manual, FSL-FIRST, FreeSurfer, multi-atlas segmentation (MALF) and non-linear registration methods (Elastix, NiftyReg, ANTs and MIRTK) were used to determine hippocampal atrophy rates on longitudinal T1-weighted MRI from the ADNI database. Appropriate parameters for the non-linear registration methods were determined using a small training dataset (N = 16) in which two-year hippocampal atrophy was measured using test-retest scans of 8 subjects with low and 8 subjects with high atrophy rates. On a larger dataset of 20 controls, 40 mild cognitive impairment (MCI) and 20  AD patients, one-year hippocampal atrophy rates were measured. A repeated measures ANOVA analysis was performed to determine differences between controls, MCI and AD patients. For each method we calculated effect sizes and the required sample sizes to detect one-year volume change between controls and MCI (NCTRL_MCI) and between controls and AD (NCTRL_AD). Finally, reproducibility of hippocampal atrophy rates was assessed using within-session rescans and expressed as an average distance measure DAve, which expresses the difference in atrophy rate, averaged over all subjects. The same DAve was used to determine the agreement between different methods.

RESULTS

Except for MALF, all methods detected a significant group difference between CTRL and AD, but none could find a significant difference between the CTRL and MCI. FreeSurfer and MIRTK required the lowest sample sizes (FreeSurfer: NCTRL_MCI = 115, NCTRL_AD = 17 with DAve = 3.26%; MIRTK: NCTRL_MCI = 97, NCTRL_AD = 11 with DAve = 3.76%), while ANTs was most reproducible (NCTRL_MCI = 162, NCTRL_AD = 37 with DAve = 1.06%), followed by Elastix (NCTRL_MCI = 226, NCTRL_AD = 15 with DAve = 1.78%) and NiftyReg (NCTRL_MCI = 193, NCTRL_AD = 14 with DAve = 2.11%). Manually measured hippocampal atrophy rates required largest sample sizes to detect volume change and were poorly reproduced (NCTRL_MCI = 452, NCTRL_AD = 87 with DAve = 12.39%). Atrophy rates of non-linear registration methods also agreed best with each other.

DISCUSSION AND CONCLUSION

Non-linear registration methods were most consistent in determining hippocampal atrophy and because of their better reproducibility, methods, such as ANTs, Elastix and NiftyReg, are preferred for determining hippocampal atrophy rates on longitudinal MRI. Since performances of non-linear registration methods are well comparable, the preferred method would mostly depend on computational efficiency.

Inter-observer variation of hippocampus delineation in hippocampal avoidance prophylactic cranial irradiation

BACKGROUND

Hippocampal avoidance prophylactic cranial irradiation (HA-PCI) techniques have been developed to reduce radiation damage to the hippocampus. An inter-observer hippocampus delineation analysis was performed and the influence of the delineation variability on dose to the hippocampus was studied.

MATERIALS AND METHODS

For five patients, seven observers delineated both hippocampi on brain MRI. The intra-class correlation (ICC) with absolute agreement and the generalized conformity index (CI_gen) were computed. Median surfaces over all observers' delineations were created for each patient and regional outlining differences were analysed. HA-PCI dose plans were made from the median surfaces and we investigated whether dose constraints in the hippocampus could be met for all delineations.

RESULTS

The ICC for the left and right hippocampus was 0.56 and 0.69, respectively, while the CI_gen ranged from 0.55 to 0.70. The posterior and anterior-medial hippocampal regions had most variation with SDs ranging from approximately 1 to 2.5 mm. The mean dose (D_mean) constraint was met for all delineations, but for the dose received by 1% of the hippocampal volume (D_1%) violations were observed.

CONCLUSION

The relatively low ICC and CI_gen indicate that delineation variability among observers for both left and right hippocampus was large. The posterior and anterior-medial border have the largest delineation inaccuracy. The hippocampus D_mean constraint was not violated.

Benefit of using motion compensated reconstructions for reducing inter-observer and intra-observer contouring variation for organs at risk in lung cancer patients

BACKGROUND AND PURPOSE

In lung cancer patients, accuracy in contouring is hampered by image artefacts introduced by respiratory motion. With the widespread introduction of 4DCT there is additional uncertainty caused by the use of different reconstruction techniques which will influence contour definition. This work aims to assess both inter- and intra-observer contour variation on average and motion compensated (mid-position) reconstructions.

MATERIAL AND METHODS

Eight early stage non-small cell lung cancer patients that received 4DCT were selected and these scans were reconstructed as average and motion compensated datasets. 5 observers contoured the organs at risk (trachea, oesophagus, proximal bronchial tree, heart and brachial plexus) for each patient and each reconstruction. Contours were compared against a STAPLE volume with distance to agreement metrics. Intra-observer variation was assessed by redelineation after 4 months.

RESULTS

The inter-observer variation was significantly smaller using the motion compensated datasets for the trachea (p = 0.006) and proximal bronchial tree (p = 0.004). For intra-observer variation, a reduction in contour variation was seen across all organs at risk in using motion compensated reconstructions.

CONCLUSIONS

This work shows that there is benefit in using motion compensated reconstructions for reducing both inter-observer and intra-observer contouring variations for organs at risk in lung cancer patients.