C. Coolens

A margin model to account for respiration-induced tumour motion and its variability

In order to reduce the sensitivity of radiotherapy treatments to organ motion, compensation methods are being investigated such as gating of treatment delivery, tracking of tumour position, 4D scanning and planning of the treatment, etc. An outstanding problem that would occur with all these methods is the assumption that breathing motion is reproducible throughout the planning and delivery process of treatment. This is obviously not a realistic assumption and is one that will introduce errors. A dynamic internal margin model (DIM) is presented that is designed to follow the tumour trajectory and account for the variability in respiratory motion. The model statistically describes the variation of the breathing cycle over time, i.e. the uncertainty in motion amplitude and phase reproducibility, in a polar coordinate system from which margins can be derived. This allows accounting for an additional gating window parameter for gated treatment delivery as well as minimizing the area of normal tissue irradiated. The model was illustrated with abdominal motion for a patient with liver cancer and tested with internal 3D lung tumour trajectories. The results confirm that the respiratory phases around exhale are most reproducible and have the smallest variation in motion amplitude and phase (approximately 2 mm). More importantly, the margin area covering normal tissue is significantly reduced by using trajectory-specific margins (as opposed to conventional margins) as the angular component is by far the largest contributor to the margin area. The statistical approach to margin calculation, in addition, offers the possibility for advanced online verification and updating of breathing variation as more data become available.

The susceptibility of IMRT dose distributions to intrafraction organ motion: An investigation into smoothing filters derived from four dimensional computed tomography data

This study investigated the sensitivity of static planning of intensity-modulated beams (IMBs) to intrafraction deformable organ motion and assessed whether smoothing of the IMBs at the treatment-planning stage can reduce this sensitivity. The study was performed with a 4D computed tomography (CT) data set for an IMRT treatment of a patient with liver cancer. Fluence profiles obtained from inverse-planning calculations on a standard reference CT scan were redelivered on a CT scan from the 4D data set at a different part of the breathing cycle. The use of a nonrigid registration model on the 4D data set additionally enabled detailed analysis of the overall intrafraction motion effects on the IMRT delivery during free breathing. Smoothing filters were then applied to the beam profiles within the optimization process to investigate whether this could reduce the sensitivity of IMBs to intrafraction organ motion. In addition, optimal fluence profiles from calculations on each individual phase of the breathing cycle were averaged to mimic the convolution of a static dose distribution with a motion probability kernel and assess its usefulness. Results from nonrigid registrations of the CT scan data showed a maximum liver motion of 7mm in superior-inferior direction for this patient. Dose-volume histogram (DVH) comparison indicated a systematic shift when planning treatment on a motion-frozen, standard CT scan but delivering over a full breathing cycle. The ratio of the dose to 50% of the normal liver to 50% of the planning target volume (PTV) changed up to 28% between different phases. Smoothing beam profiles with a median-window filter did not overcome the substantial shift in dose due to a difference in breathing phase between planning and delivery of treatment. Averaging of optimal beam profiles at different phases of the breathing cycle mainly resulted in an increase in dose to the organs at risk (OAR) and did not seem beneficial to compensate for organ motion compared with using a large margin. Additionally, the results emphasized the need for 4D CT scans when aiming to reduce the internal margin (IM). Using only a single planning scan introduces a systematic shift in the dose distribution during delivery. Smoothing beam profiles either based on a single scan or over the different breathing phases was not beneficial for reducing this shift.

Active breathing control for patients receiving mediastinal radiation therapy for lymphoma: Impact on normal tissue dose

PURPOSE Active breathing control (ABC) is emerging as a tool to reduce heart and lung dose for lymphoma patients receiving mediastinal radiation therapy (RT). The objective of this study was to report our early institutional experience with this technique, with emphasis on quantifying the changes in normal tissue dose and exploring factors that could be used to select patients with the greatest benefit. METHODS AND MATERIALS Patients receiving mediastinal involved-field RT (IFRT) for lymphoma were eligible. The ABC was performed using a moderate deep-inspiration breath-hold (mDIBH) technique. All patients were replanned with free-breathing (FB) computed tomographic data sets and comparisons of lung, cardiac, and female breast tissue doses were made between mDIBH and FB plans. Logistic regression models were used to identify factors associated with improvement in mean lung and heart dose with mDIBH. RESULTS Forty-seven patients were analyzed; the majority (87.2%) had Hodgkin lymphoma. Median prescribed dose was 30 Gy (range, 20-36 Gy), with 78.7% of cases being treated with parallel-opposed beams. The use of mDIBH significantly improved average mean lung dose (FB: 11.0 Gy; mDIBH: 9.5 Gy; P < .0001), lung V20 (28% vs 22%; P < .0001), and mean heart dose (14.3 Gy vs 11.8 Gy; P = .003), but increased the mean breast dose (FB: 3.0 Gy; mDIBH 3.6 Gy; P = .0005). The magnitude of diaphragmatic excursion on the inhale scan was significantly associated with dosimetric improvement in both heart and lung dose with mDIBH. CONCLUSIONS Mediastinal IFRT for lymphoma delivered with mDIBH can significantly reduce lung and heart dose compared with FB, although not for all patients, and may increase breast dose in females. Its implementation is achievable in both adult and pediatric populations. Further work is necessary to better predict which patients benefit from this technique.

Automated Voxel-Based Analysis of Volumetric Dynamic Contrast-Enhanced CT Data Improves Measurement of Serial Changes in Tumor Vascular Biomarkers

OBJECTIVES Development of perfusion imaging as a biomarker requires more robust methodologies for quantification of tumor physiology that allow assessment of volumetric tumor heterogeneity over time. This study proposes a parametric method for automatically analyzing perfused tissue from volumetric dynamic contrast-enhanced (DCE) computed tomography (CT) scans and assesses whether this 4-dimensional (4D) DCE approach is more robust and accurate than conventional, region-of-interest (ROI)-based CT methods in quantifying tumor perfusion with preliminary evaluation in metastatic brain cancer. METHODS AND MATERIALS Functional parameter reproducibility and analysis of sensitivity to imaging resolution and arterial input function were evaluated in image sets acquired from a 320-slice CT with a controlled flow phantom and patients with brain metastases, whose treatments were planned for stereotactic radiation surgery and who consented to a research ethics board-approved prospective imaging biomarker study. A voxel-based temporal dynamic analysis (TDA) methodology was used at baseline, at day 7, and at day 20 after treatment. The ability to detect changes in kinetic parameter maps in clinical data sets was investigated for both 4D TDA and conventional 2D ROI-based analysis methods. RESULTS A total of 7 brain metastases in 3 patients were evaluated over the 3 time points. The 4D TDA method showed improved spatial efficacy and accuracy of perfusion parameters compared to ROI-based DCE analysis (P<.005), with a reproducibility error of less than 2% when tested with DCE phantom data. Clinically, changes in transfer constant from the blood plasma into the extracellular extravascular space (Ktrans) were seen when using TDA, with substantially smaller errors than the 2D method on both day 7 post radiation surgery (±13%; P<.05) and by day 20 (±12%; P<.04). Standard methods showed a decrease in Ktrans but with large uncertainty (111.6 ± 150.5) %. CONCLUSIONS Parametric voxel-based analysis of 4D DCE CT data resulted in greater accuracy and reliability in measuring changes in perfusion CT-based kinetic metrics, which have the potential to be used as biomarkers in patients with metastatic brain cancer.

Combinational use of conformal and intensity-modulated beams in radiotherapy planning

Intensity-modulated (IM) beam profiles computed by inverse-planning systems tend to be complex and may have multiple spatial minima and maxima. In addition to the structure originating from the treatment objectives, beam profiles might contain stochastic structure or noise and numerical artefacts, which present certain practical difficulties. The combinational use of conformal and intensity-modulated beams could be a different method of making the total fluence distribution less noisy and deliverable without compromising the advantages of IMRT. The investigation of this possibility provided the basis for this paper. A treatment-planning study was performed to compare plans combining modulated and unmodulated beams with a 5-field, equally spaced, full IMRT plan for treating the prostate and seminal vesicles in three patients. Beam angles for this study were 0 degrees, 72 degrees, 144 degrees, 216 degrees and 288 degrees. Additionally, a study was performed on a patient with a different beam arrangement (36 degrees, 108 degrees, 180 degrees, 252 degrees, 324 degrees) from the first study to test the obtained results. This study has demonstrated that it is possible to substitute up to two conformal beams in the originally full IMRT plan when carefully selecting the conformal beam angles. Making the anterior beam (0 degrees) and an anterior oblique beam (between 0 degrees and 90 degrees) conformal leads to a reduction in the total number of monitor units and segments of about 15% and 39%, respectively. Additionally, these two open fields can be used for simpler treatment verification.

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.

Quantitative Imaging in Radiation Oncology: An Emerging Science and Clinical Service

Radiation oncology has long required quantitative imaging approaches for the safe and effective delivery of radiation therapy. The past 10 years has seen a remarkable expansion in the variety of novel imaging signals and analyses that are starting to contribute to the prescription and design of the radiation treatment plan. These include a rapid increase in the use of magnetic resonance imaging, development of contrast-enhanced imaging techniques, integration of fluorinated deoxyglucose-positron emission tomography, evaluation of hypoxia imaging techniques, and numerous others. These are reviewed with an effort to highlight challenges related to quantification and reproducibility. In addition, several of the emerging applications of these imaging approaches are also highlighted. Finally, the growing community of support for establishing quantitative imaging approaches as we move toward clinical evaluation is summarized and the need for a clinical service in support of the clinical science and delivery of care is proposed.

Feasibility of 4D perfusion CT imaging for the assessment of liver treatment response following SBRT and sorafenib

Objectives To evaluate the feasibility of 4-dimensional perfusion computed tomography (CT) as an imaging biomarker for patients with hepatocellular carcinoma and metastatic liver disease. Methods and materials Patients underwent volumetric dynamic contrast-enhanced CT on a 320-slice scanner before and during stereotactic body radiation therapy and sorafenib, and at 1 and 3 months after treatment. Quiet free breathing was used in the CT acquisition and multiple techniques (rigid or deformable registration as well as outlier removal) were applied to account for residual liver motion. Kinetic modeling was performed on a voxel-by-voxel basis in the gross tumor volume and normal liver resulting in 3-dimensional parameter maps of blood perfusion, capillary permeability, blood volume, and mean transit time. Perfusion characteristics in the tumor and adjacent liver were correlated with radiation dose distributions to evaluate dose-response. Paired t tests assessed change in spatial and histogram parameters from baseline to different time points during and after treatment. Technique reproducibility as well as the impact of arterial and portal vein input functions was also investigated using intra- and inter-subject variance and Bland-Altman analysis. Results Quantitative perfusion parameters were reproducible (±5.7%; range, 2%-10%) depending on tumor/normal liver type and kinetic parameter. Statistically significant reductions in tumor perfusion were measurable over the course of treatment and as early as 1 week after sorafenib administration (P < .05). Marked liver parenchyma perfusion reduction was seen with a strong dose-response effect (R2 = 0.95) that increased significantly over the course treatment. Conclusions The proposed methodology demonstrated feasibility of evaluating spatiotemporal changes in liver tumor perfusion and normal liver function following antiangiogenic therapy and radiation treatment warranting further evaluation of biomarker prognostication.

Multimodality Functional Imaging in Radiation Therapy Planning: Relationships between Dynamic Contrast-Enhanced MRI, Diffusion-Weighted MRI, and 18F-FDG PET

OBJECTIVES Biologically guided radiotherapy needs an understanding of how different functional imaging techniques interact and link together. We analyse three functional imaging techniques that can be useful tools for achieving this objective. MATERIALS AND METHODS The three different imaging modalities from one selected patient are ADC maps, DCE-MRI, and 18F-FDG PET/CT, because they are widely used and give a great amount of complementary information. We show the relationship between these three datasets and evaluate them as markers for tumour response or hypoxia marker. Thus, vascularization measured using DCE-MRI parameters can determine tumour hypoxia, and ADC maps can be used for evaluating tumour response. RESULTS ADC and DCE-MRI include information from 18F-FDG, as glucose metabolism is associated with hypoxia and tumour cell density, although 18F-FDG includes more information about the malignancy of the tumour. The main disadvantage of ADC maps is the distortion, and we used only low distorted regions, and extracellular volume calculated from DCE-MRI can be considered equivalent to ADC in well-vascularized areas. CONCLUSION A dataset for achieving the biologically guided radiotherapy must include a tumour density study and a hypoxia marker. This information can be achieved using only MRI data or only PET/CT studies or mixing both datasets.

SU-D-303-02: Impact of Arterial Input Function Selection and T10 Correction On DCE-MRI Tumour Response Prediction Using Compared to Volumetric DCE CT

Purpose: To evaluate the impact of individualized magnitude and phase signal arterial input function (AIF) measurements as well as voxel-based pre-contrast T10 relaxation on tumour perfusion metrics from DCE-MRI compared to DCE-CT using a common 4D temporal dynamic analysis (TDA) method. Methods: Nine patients with 13 brain metastases underwent volumetric DCE-CT (Toshiba, Aquilion ONE) and DCE-MRI (IMRIS 3T Verio) at baseline then 7 and 21 days post-radiosurgery. Voxel-based whole brain TDA was performed on all data using in-house software producing kinetic parameters AUC, Ktrans, Kep, and Vb (using the Modified Tofts model). AIF susceptibility was investigated in DCE-CT by selecting the AIF (from internal carotid artery) or VIF (from Sagittal Sinus). In DCE-MRI an individual Magnitude or Phase-based AIF (Sagittal Sinus) was compared to population-based AIF together with susceptibility to voxel-based T10 maps instead of a constant of 2400 msec. Absolute DCE-MRI values were compared to DCE-CT by Pearson correlation. Results: No significant difference in median Ktrans (0.048 +/−0.03 s-1) or AUC (2785.5 +/−1143.6 HU.s) was found between individual AIF and VIF-based DCE CT analyses. Using individual Magnitude VIF or Phase-based AIF for DCE-MRI (T10 2400 ms) resulted in higher Ktrans values (0.181 +/−0.11 vs. 0.121 +/−0.099 s-1). This is likely resulting from the smaller AIF peak since the population AIF (which more closely resembles CT) correlates better to DCE-CT metrics. Using voxel-based T10 maps caused further statistically-significant increase in Ktrans and AUC (p<0.0006) that could be contributed to a lower median T10 value (1572 +/−594, n=41). Conclusion: This preliminary data highlights the stability of DCE-CT calculations as well as susceptibility of DCE-MRI Ktrans measurements to various imaging factors, including AIF selection and T10 values used for modeling. Efforts to improve voxel-based T10 map calculations are being explored to further explain discrepancies between analysis methods. Brain Tumor Foundation of Canada and Ontario Institute of Cancer Research