Three-dimensional surface imaging for detection of intra-fraction setup variations during radiotherapy of pelvic tumors

Setup accuracy and margins for surface-guided radiotherapy (SGRT) of head, thorax, abdomen, and pelvic target volumes

The goal of the study was to evaluate the inter- and intrafractional patient setup accuracy of target volumes located in the head, thoracic, abdominal, and pelvic regions when using SGRT, by comparing it with that of laser alignment using patient skin marks, and to calculate the corresponding setup margins. A total of 2303 radiotherapy fractions of 183 patients were analyzed. All patients received daily kilovoltage cone-beam computed tomography scans (kV-CBCT) for online verification. From November 2019 until September 2020, patient setup was performed using laser alignment with patient skin marks, and since October 2020, using SGRT. The setup accuracy was measured by the six degrees of freedom (6DOF) corrections based on the kV-CBCT. The corresponding setup margins were calculated using the van Herk formula. Analysis of variance (ANOVA) was used to evaluate the impact of multiple factors on the setup accuracy. The inter-fractional patient setup accuracy was significantly better using SGRT compared to laser alignment with skin marks. The mean three-dimensional vector of the translational setup deviation of tumors located in the thorax, abdomen, and pelvis using SGRT was 3.6 mm (95% confidence interval (CI) 3.3 mm to 3.9 mm) and 4.5 mm using laser alignment with skin marks (95% CI 3.9 mm to 5.2 mm; p = 0.001). Calculation of setup margins for the combined inter- and intra-fractional setup error revealed similar setup margins using SGRT and kV-CBCT once a week compared to laser alignment with skin marks and kV-CBCT every other day. Furthermore, comparable setup margins were found for open-face thermoplastic masks with AlignRT compared to closed-face thermoplastic masks with laser alignment and mask marks. SGRT opens the possibility to reduce the number of CBCTs while maintaining sufficient setup accuracy. The advantage is a reduction of imaging dose and overall treatment time. Open-face thermoplastic masks may be used instead of closed-face thermoplastic masks to increase the patient's comfort.

Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients

Purpose

To find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves' ophthalmopathy (GO).

Methods

Position errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input.

Results

The best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively.

Conclusion

ML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.

Performance assessment of surface-guided radiation therapy and patient setup in head-and-neck and breast cancer patients based on statistical process control

PURPOSE

To assess the effectiveness of SGRT in clinical applications through statistical process control (SPC).

METHODS

Taking the patients' positioning through optical surface imaging (OSI) as a process, the average level of process execution was defined as the process mean. Setup errors detected by cone-beam computed tomography (CBCT) and OSI were extracted for head-and-neck cancer (HNC) and breast cancer patients. These data were used to construct individual and exponentially weighted moving average (EWMA) control charts to analyze outlier fractions and small process shifts from the process mean. Using the control charts and process capability indices derived from this process, the patient positioning-related OSI performance and setup error were analyzed for each patient.

RESULTS

Outlier fractions and small shifts from the process mean that are indicative of setup errors were found to be widely prevalent, with the outliers randomly distributed between fractions. A systematic error of up to 1.6 mm between the OSI and CBCT results was observed in all directions, indicating a significantly degraded OSI performance. Adjusting this systematic error for each patient using setup errors of the first five fractions could effectively mitigate these effects. Process capability analysis following adjustment for systematic error indicated that OSI performance was acceptable (process capability index C_pk = 1.0) for HNC patients but unacceptable (C_pk < 0.75) for breast cancer patients.

CONCLUSION

SPC is a powerful tool for detecting the outlier fractions and process changes. Our application of SPC to patient-specific evaluations validated the suitability of OSI in clinical applications involving patient positioning.

ACR-ASTRO Practice Parameter for Image-guided Radiation Therapy (IGRT)

AIM/OBJECTIVES/BACKGROUND

The American College of Radiology (ACR) and the American Society for Radiation Oncology (ASTRO) have jointly developed the following practice parameter for image-guided radiation therapy (IGRT). IGRT is radiation therapy that employs imaging to maximize accuracy and precision throughout the entire process of treatment delivery with the goal of optimizing accuracy and reliability of radiation therapy to the target, while minimizing dose to normal tissues.

METHODS

The ACR-ASTRO Practice Parameter for IGRT was revised according to the process described on the ACR website ("The Process for Developing ACR Practice Parameters and Technical Standards," www.acr.org/ClinicalResources/Practice-Parametersand-Technical-Standards) by the Committee on Practice Parameters of the ACR Commission on Radiation Oncology in collaboration with the ASTRO. Both societies then reviewed and approved the document.

RESULTS

This practice parameter is developed to serve as a tool in the appropriate application of IGRT in the care of patients with conditions where radiation therapy is indicated. It addresses clinical implementation of IGRT including personnel qualifications, quality assurance standards, indications, and suggested documentation.

CONCLUSIONS

This practice parameter is a tool to guide clinical use of IGRT and does not make recommendations on site-specific IGRT directives. It focuses on the best practices and principles to consider when using IGRT effectively, especially with the significant increase in imaging data that is now available with IGRT. The clinical benefit and medical necessity of the imaging modality and frequency of IGRT should be assessed for each patient.

[Planning target volume : Management of uncertainties, immobilization, image guided and adaptive radiation therapy]

CLINICAL/METHODICAL ISSUE

As a standard, today's radiation therapy is based on CT images which are used for therapy planning. These images are obtained once before therapy starts and serve as a basis to obtain the position and shape of the target volume. As the patient has to be positioned anew for each fraction, deviations of the tumor position relative to the radiation field but also internal motion of the tumor may occur. These deviations lead to uncertainties, which are taken into account by adding a safety margin around the clinical target volume (CTV) to create the planning target volume (PTV).

STANDARD RADIOLOGICAL METHODS

As a standard today, CT-based treatment planning is used, where images are obtained once prior to therapy. The information on tumor position and shape, which is obtained from these images, is used throughout the whole cycle of radiation therapy without any change. This cycle may last several weeks.

METHODICAL INNOVATIONS

By repeated imaging of the patient in the treatment position prior to each fraction, the position of the tumor can be assessed and corrected for each fraction.

PERFORMANCE

A reduction of positioning uncertainty may be used to reduce the safety margin. This leads to a decreased volume of irradiated normal tissue.

ACHIEVEMENTS

A reduced volume of irradiated normal tissue leads to reduced side effects and provides the opportunity of increased tumor control by dose escalation.

PRACTICAL RECOMMENDATIONS

Before the PTV is reduced, a detailed analysis of the uncertainties for the specific imaging method and radiation technique must be performed.

Recent advances in radiation oncology

Radiotherapy (RT) is very much a technology-driven treatment modality in the management of cancer. RT techniques have changed significantly over the past few decades, thanks to improvements in engineering and computing. We aim to highlight the recent developments in radiation oncology, focusing on the technological and biological advances. We will present state-of-the-art treatment techniques, employing photon beams, such as intensity-modulated RT, volumetric-modulated arc therapy, stereotactic body RT and adaptive RT, which make possible a highly tailored dose distribution with maximum normal tissue sparing. We will analyse all the steps involved in the treatment: imaging, delineation of the tumour and organs at risk, treatment planning and finally image-guidance for accurate tumour localisation before and during treatment delivery. Particular attention will be given to the crucial role that imaging plays throughout the entire process. In the case of adaptive RT, the precise identification of target volumes as well as the monitoring of tumour response/modification during the course of treatment is mainly based on multimodality imaging that integrates morphological, functional and metabolic information. Moreover, real-time imaging of the tumour is essential in breathing adaptive techniques to compensate for tumour motion due to respiration. Brief reference will be made to the recent spread of particle beam therapy, in particular to the use of protons, but also to the yet limited experience of using heavy particles such as carbon ions. Finally, we will analyse the latest biological advances in tumour targeting. Indeed, the effectiveness of RT has been improved not only by technological developments but also through the integration of radiobiological knowledge to produce more efficient and personalised treatment strategies.

Three-dimensional surface and ultrasound imaging for daily IGRT of prostate cancer

Background

Image guided radiotherapy (IGRT) is an essential pre-requisite for delivering high precision radiotherapy. We compared daily variation detected by two non-ionizing imaging modalities (surface imaging and trans-abdominal ultrasound, US) to verify prostate patient setup and internal organ variations.

Methods

Forty patients with organ confined prostate cancer and candidates to curative radiotherapy were enrolled in this prospective study. At each treatment session, after laser alignment, all patients received imaging by a 3D-surface and a 3D-US system. The shifts along the three directions (anterior-posterior AP, cranial-caudal CC, and later-lateral LL) were measured in terms of systematic and random errors. Then, we performed statistical analysis on the differences and the possible correlations between the two modalities.

Results

For both IGRT modalities, surface imaging and US, 1318 acquisitions were collected. According with Shapiro Wilk test, the positioning error distributions were not Gaussian for both modalities.

The differences between the systematic errors detected by the two modalities were statistically significant only in LL direction (p < 0.05), while the differences between the random errors were not statistically significant in any directions.

The 95% confidence interval of the residual errors obtained by subtracting the random errors detected with surface images to those detected with US was included in the range from −7 mm to 7 mm corresponding to the minimum PTV margin adopted in AP direction in our clinical routine.

Conclusions

From our data, it emerges that setup misalignments measured by surface imaging can be predictive of US displacements after the adjustment for systematic errors. Moreover, surface imaging can detect setup errors predictive of registration errors measured by US. This data suggest that the two IGRT modalities could be considered as complementary to each other and could represent a daily “low-cost” and non-invasive IGRT modality in prostate cancer patients.