Dosimetric impact of gastrointestinal air column in radiation treatment of pancreatic cancer

Why we should care about gas pockets in online adaptive MRgRT: a dosimetric evaluation

Introduction

Contouring of gas pockets is a time consuming step in the workflow of adaptive radiotherapy. We would like to better understand which gas pockets electronic densitiy should be used and the dosimetric impact on adaptive MRgRT treatment.

Materials and methods

21 CT scans of patients undergoing SBRT were retrospectively evaluated. Anatomical structures were contoured: Gross Tumour Volume (GTV), stomach (ST), small bowel (SB), large bowel (LB), gas pockets (GAS) and gas in each organ respectively STG, SBG, LBG. Average HU in GAS was converted in RED, the obtained value has been named as Gastrointestinal Gas RED (GIGED). Differences of average HU in GAS, STG, SBG and LBG were computed. Three treatment plans were calculated editing the GAS volume RED that was overwritten with: air RED (0.0012), water RED (1.000), GIGED, generating respectively APLAN, WPLAN and the GPLAN. 2-D dose distributions were analyzed by gamma analysis. Parameter called active gas volume (AGV) was calculated as the intersection of GAS with the isodose of 5% of prescription dose.

Results

Average HU value contained in GAS results to be equal to -620. No significative difference was noted between the average HU of gas in different organ at risk. Value of Gamma Passing Rate (GPR) anticorrelates with the AGV for each plan comparison and the threshold value for GPR to fall below 90% is 41, 60 and 139 cc for WPLANvsAPLAN, GPLANvsAPLAN and WPLANvsGPLAN respectively.

Discussions

GIGED is the right RED for Gastrointestinal Gas. Novel AGV is a useful parameter to evaluate the effect of gas pocket on dose distribution.

Image guidance and interfractional anatomical variation in paediatric abdominal radiotherapy

OBJECTIVES

To identify variables predicting interfractional anatomical variations measured with cone-beam CT (CBCT) throughout abdominal paediatric radiotherapy, and to assess the potential of surface-guided radiotherapy (SGRT) to monitor these changes.

METHODS

Metrics of variation in gastrointestinal (GI) gas volume and separation of the body contour and abdominal wall were calculated from 21 planning CTs and 77 weekly CBCTs for 21 abdominal neuroblastoma patients (median 4 years, range: 2 - 19 years). Age, sex, feeding tubes, and general anaesthesia (GA) were explored as predictive variables for anatomical variation. Furthermore, GI gas variation was correlated with changes in body and abdominal wall separation, as well as simulated SGRT metrics of translational and rotational corrections between CT/CBCT.

RESULTS

GI gas volumes varied 74 ± 54 ml across all scans, while body and abdominal wall separation varied 2.0 ± 0.7 mm and 4.1 ± 1.5 mm from planning, respectively. Patients < 3.5 years (p = 0.04) and treated under GA (p < 0.01) experienced greater GI gas variation; GA was the strongest predictor in multivariate analysis (p < 0.01). Absence of feeding tubes was linked to greater body contour variation (p = 0.03). GI gas variation correlated with body (R = 0.53) and abdominal wall (R = 0.63) changes. The strongest correlations with SGRT metrics were found for anterior-posterior translation (R = 0.65) and rotation of the left-right axis (R = -0.36).

CONCLUSIONS

Young age, GA, and absence of feeding tubes were linked to stronger interfractional anatomical variation and are likely indicative of patients benefiting from adaptive/robust planning pathways. Our data suggest a role for SGRT to inform the need for CBCT at each treatment fraction in this patient group.

ADVANCES IN KNOWLEDGE

This is the first study to suggest the potential role of SGRT for the management of internal interfractional anatomical variation in paediatric abdominal radiotherapy.

Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy

Objective. Adaptive radiotherapy workflows require images with the quality of computed tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve the quality of on-board cone beam CT (CBCT) images for dose calculation using deep learning.Approach. We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a challenging application due to the inter-fractional variability in bowel filling and small patient numbers. We introduced to the networks the concept of global residuals only learning and modified the cycleGAN loss function to explicitly promote structural consistency between source and synthetic images. Finally, to compensate for the anatomical variability and address the difficulties in collecting large datasets in the paediatric population, we applied a smart 2D slice selection based on the common field-of-view (abdomen) to our imaging dataset. This acted as a weakly paired data approach that allowed us to take advantage of scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training purposes. We first optimised the proposed framework and benchmarked its performance on a development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen dataset, which included calculating global image similarity metrics, segmentation-based measures and proton therapy-specific metrics.Main results. We found improved performance for our proposed method, compared to a baseline cycleGAN implementation, on image-similarity metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0 ± 16.6 HU proposed versus 58.9 ± 16.8 HU baseline). There was also a higher level of structural agreement for gastrointestinal gas between source and synthetic images measured using the dice similarity coefficient (0.872 ± 0.053 proposed versus 0.846 ± 0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for our method (3.3 ± 2.4% proposed versus 3.7 ± 2.8% baseline).Significance. Our findings indicate that our innovations to the cycleGAN framework improved the quality and structure consistency of the synthetic CTs generated.

Dosimetric effect of the intestinal gas of online adaptive stereotactic body radiotherapy on target and critical organs without online electron density correction for pancreatic cancer

OBJECTIVE

This study aimed to assess the dosimetric effect of intestinal gas of stereotactic magnetic resonance (MR)-guided adaptive radiation therapy (SMART) on target and critical organs for pancreatic cancer without online electron density correction (EDC).

METHODS

Thirty pancreatic cancer patients who underwent online SMART were selected for this study. The treatment time of each stage and the total treatment time were recorded and analyzed. The concerned dose-volume parameters of target and organs-at-risk (OAR) were compared with and without an intestinal gas EDC using the Wilcoxon-signed rank test. Analysis items with p value < 0.05 were considered statistically significant. The relationships between dosimetric differences and intestinal gas volume variations were investigated using the Spearman test.

RESULTS

The average treatment time was 82 min, and the average EDC time was 8 min, which accounted for 10% of the overall treatment time. There were no significant differences in CTV (GTV), PTV, bowel, stomach, duodenum, and skin (p > 0.05) with respect to dose volume parameters. For the D_max of gastrointestinal organs (p = 0.03), the mean dose of the liver (p = 0.002) and kidneys (p = 0.03 and p = 0.04 for the left and right kidneys, respectively), there may be a risk of slight overestimation compared with EDC, and for the D_max of the spinal cord (p = 0.02), there may be a risk of slight underestimation compared with EDC. A weak correlation for D₉₅ in the PTV and D_{0.5 cc} in the duodenum was observed.

CONCLUSION

For patients with similar inter-fractional intestinal gas distribution, EDC had little dosimetric effects on the D_{0.5 cc} of all GI organs and dose volume parameters of target in most plans.

ADVANCES IN KNOWLEDGE

By omitting the EDC of intestinal gas, the online SMART treatment time can be shortened.

Quantifying and Assessing the Dosimetric Impact of Changing Gas Volumes Throughout the Course of VMAT Radiation Therapy of Upper Gastrointestinal Tumors

Purpose

This retrospective patient study assessed the consistency of abdominal gas presence throughout radiation therapy for patients with upper gastrointestinal cancer and determined the impact of variations in gas volume on the calculated dose distribution of volumetric modulated arc therapy.

Methods and Materials

Eight patients with pancreatic cancer were included for analysis. A plan library consisting of 3 reference plans per patient (Ref_0.0, Ref_0.5, and Ref_1.0) was created based on planning computed tomography (CT) with density overrides of 0.0, 0.5, and 1.0 applied to gas volumes, respectively. Corresponding cone beam CT (CBCT) data sets were obtained and density overrides were applied to enable fractional dose calculation. Variation in gas volume relative to initial volume determined from CT was assessed. Dose metrics for targets and organs at risk were compared between the accumulated CBCT dose and the planned dose of the 3 reference plans for each patient.

Results

There was a significant decrease in gas present from CT to treatment CBCT, with a mean decrease in volume of 48.6% for the entire cohort. Dosimetrically, all accumulated target and organ-at-risk parameters, aside from the kidneys, exhibited the smallest mean deviation from the Ref_0.0 plan and largest mean deviation from the Ref_1.0 plan. A statistically significant difference in mean accumulated dose to Ref_0.0 and Ref_1.0 was observed for the dose delivered to 95% of the planning target volume.

Conclusions

Significant variation in gas volumes from CT to treatment can occur throughout volumetric modulated arc therapy for pancreatic cancer. Through the use of a plan library, it was determined that initial assessment of a patient's treatment plan with an assigned gas density of 0.0 provided the most accurate prediction of the accumulated dose.