Adaptive radiotherapy for NSCLC patients: utilizing the principle of energy conservation to evaluate dose mapping operations

Development of a multi-layer quality assurance program to evaluate the uncertainty of deformable dose accumulation in adaptive radiotherapy

PURPOSE

Deformable dose accumulation (DDA) has uncertainties which impede the implementation of DDA-based adaptive radiotherapy (ART) in clinic. The purpose of this study is to develop a multi-layer quality assurance (MLQA) program to evaluate uncertainties in DDA.

METHODS

A computer program is developed to generate a pseudo-inverse displacement vector field (DVF) for each deformable image registration (DIR) performed in Accuray's PreciseART. The pseudo-inverse DVF is first used to calculate a pseudo-inverse consistency error (PICE) and then implemented in an energy and mass congruent mapping (EMCM) method to reconstruct a deformed dose. The PICE is taken as a metric to estimate DIR uncertainties. A pseudo-inverse dose agreement rate (PIDAR) is used to evaluate the consequence of the DIR uncertainties in DDA and the principle of energy conservation is used to validate the integrity of dose mappings. The developed MLQA program was tested using the data collected from five representative cancer patients treated with tomotherapy.

RESULTS

DIRs were performed in PreciseART to generate primary DVFs for the five patients. The fidelity index and PICE of these DVFs on average are equal to 0.028 mm and 0.169 mm, respectively. With the criteria of 3 mm/3% and 5 mm/5%, the PIDARs of the PreciseART-reconstructed doses are 73.9 ± 4.4% and 87.2 ± 3.3%, respectively. The PreciseART and EMCM-based dose reconstructions have their deposited energy changed by 5.6 ± 3.9% and 2.6 ± 1.5% in five GTVs, and by 9.2 ± 7.8% and 4.7 ± 3.6% in 30 OARs, respectively.

CONCLUSIONS

A pseudo-inverse map-based EMCM program has been developed to evaluate DIR and dose mapping uncertainties. This program could also be used as a sanity check tool for DDA-based ART.

Adaptive Radiation Therapy in the Treatment of Lung Cancer: An Overview of the Current State of the Field

Lung cancer treatment is constantly evolving due to technological advances in the delivery of radiation therapy. Adaptive radiation therapy (ART) allows for modification of a treatment plan with the goal of improving the dose distribution to the patient due to anatomic or physiologic deviations from the initial simulation. The implementation of ART for lung cancer is widely varied with limited consensus on who to adapt, when to adapt, how to adapt, and what the actual benefits of adaptation are. ART for lung cancer presents significant challenges due to the nature of the moving target, tumor shrinkage, and complex dose accumulation because of plan adaptation. This article presents an overview of the current state of the field in ART for lung cancer, specifically, probing topics of: patient selection for the greatest benefit from adaptation, models which predict who and when to adapt plans, best timing for plan adaptation, optimized workflows for implementing ART including alternatives to re-simulation, the best radiation techniques for ART including magnetic resonance guided treatment, algorithms and quality assurance, and challenges and techniques for dose reconstruction. To date, the clinical workflow burden of ART is one of the major reasons limiting its widespread acceptance. However, the growing body of evidence demonstrates overwhelming support for reduced toxicity while improving tumor dose coverage by adapting plans mid-treatment, but this is offset by the limited knowledge about tumor control. Progress made in predictive modeling of on-treatment tumor shrinkage and toxicity, optimizing the timing of adaptation of the plan during the course of treatment, creating optimal workflows to minimize staffing burden, and utilizing deformable image registration represent ways the field is moving toward a more uniform implementation of ART.

Dosimetric comparison of dose accumulation between rigid registration and deformation registration in intensity-modulated radiation therapy for large volume non-small cell lung cancer

Background

To evaluate the cumulative dose to the target volumes and organs at risk (OARs) after replanning during intensity-modulated radiation therapy (IMRT) for large volume non-small cell lung cancer (NSCLC) based on rigid registration and deformation registration technologies.

Methods

Thirty patients with large volume NSCLC who were treated with IMRT were selected, and two four-dimensional computed tomography (4DCT) scans were acquired before radiotherapy and after 20 fractions of radiotherapy. The initial treatment plan (Plan₁) based on the average density projection CT (CT_1-avg) of the first 4DCT images and the second treatment plan (Plan₂) based on CT_2-avg of the second 4DCT images were calculated. Then, the dose distributions of Plan₂ and Plan₁ were accumulated based on rigid and deformation registration technologies to obtain Plan_rig and Plan_def, respectively. Finally, the volume changes of the gross tumor volume (GTV) and OARs between the two CT scans, and the dose-volume parameters among Plan₁, Plan₂, Plan_rig and Plan_def were compared.

Results

Compared with those on the first CT, the mean GTV and heart volume on the second CT decreased by 44.2% and 5.5%, respectively, while the mean volumes of the ipsilateral lung, contralateral lung and total lung increased by 5.2%, 6.2% and 5.8%, respectively. The differences in the above volume parameters between the two CT scans were statistically significant (P<0.05). Compared with those in Plan₁, the D₉₅, D₉₈ and V_100% values of the IGTV (GTV fusion of 10 CT phases) and planning target volume (PTV) in Plan₂ did not change significantly (P>0.05), and those of Plan_rig and Plan_def decreased slightly (P<0.05). The dose-volume parameters of the spinal cord, heart, ipsilateral lung and total lung in Plan₂, Plan_rig and Plan_def were significantly lower than those in Plan₁ (P<0.05). Among these parameters, V₃₀ and the mean dose to the heart in Plan₂, Plan_rig and Plan_def decreased by 27.3%, 16.5%, and 15.3% and 15.2%, 6.6%, and 5.6% compared to those in Plan₁, respectively; V₂₀ and the mean dose to the total lung in Plan₂, Plan_rig and Plan_def decreased by 15.6%, 4.5%, and 3.7% and 15.7%, 6.2%, and 5.1% compared to those in Plan₁, respectively. Some dose-volume parameters (including D₉₅ and D₉₈ to the target volume, V₄₀ of the heart, V₂₀ and the mean dose to the ipsilateral lung and the total lung) of Plan_def were slightly higher than those in Plan_rig (P<0.05). The Dice similarity coefficients (DSCs) of the OARs after deformation registration were significantly higher than those after rigid registration (P<0.05).

Conclusions

The dose-volume parameters of OARs in Plan₂ were noticeably different from those in Plan₁, so all of these parameters have large deviations in evaluating the actual dose to the OARs. And, the dose-volume parameters obtained by deformation registration can better predict the actual dose than those obtained by rigid registration.

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.

Deformable Registration for Dose Accumulation

As deformable image registration makes its way into the clinical routine, the summation of doses from fractionated treatment regimens to evaluate cumulative doses to targets and healthy tissues is also becoming a frequently utilized tool in the context of image-guided adaptive radiotherapy. Accounting for daily geometric changes using deformable image registration and dose accumulation potentially enables a better understanding of dose-volume-effect relationships, with the goal of translation of this knowledge to personalization of treatment, to further enhance treatment outcomes. Treatment adaptation involving image deformation requires patient-specific quality assurance of the image registration and dose accumulation processes, to ensure that uncertainties in the 3D dose distributions are identified and appreciated from a clinical relevance perspective. While much research has been devoted to identifying and managing the uncertainties associated with deformable image registration and dose accumulation approaches, there are still many unanswered questions. Here, we provide a review of current deformable image registration and dose accumulation techniques, and related clinical application. We also discuss salient issues that need to be deliberated when applying deformable algorithms for dose mapping and accumulation in the context of adaptive radiotherapy and response assessment.

Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy

Adaptive radiotherapy emerged over 20 years ago and is now an established clinical practice in a number of organ sites. No one solution for adaptive therapy exists. Rather, adaptive radiotherapy is a process which combines multiple tools for imaging, assessment of need for adaptation, treatment planning, and quality assurance of this process. Workflow is therefore a critical aspect to ensure safe, effective, and efficient implementation of adaptive radiotherapy. In this work, we discuss the tools for online and offline adaptive radiotherapy and introduce workflow concepts for these types of adaptive radiotherapy. Common themes and differences between the workflows are introduced and controversies and areas of active research are discussed.

Online daily assessment of dose change in head and neck radiotherapy without dose-recalculation

BACKGROUND

Head and neck cancers are commonly treated with radiation therapy, but due to possible volume changes, plan adaptation may be required during the course of treatment. Currently, plan adaptations consume significant clinical resources. Existing methods to evaluate the need for plan adaptation requires deformable image registration (DIR) to a new CT simulation or daily cone beam CT (CBCT) images and the recalculation of the dose distribution. In this study, we explore a tool to assist the decision for plan adaptation using a CBCT without re-computation of dose, allowing for rapid online assessment.

METHODS

This study involved 18 head and neck cancer patients treated with CBCT image guidance who had their treatment plan modified based on a new CT simulation (ReCT). Dose changes were estimated using different methods and compared to the current gold standard of using DIR between the planning CT scan (PCT) and ReCT with recomputed dose. The first and second methods used DIR between the PCT and daily CBCT with the planned dose or recalculated dose from the ReCT respectively, with the dose transferred to the CBCT using rigid registration. The necessity of plan adaptation was assessed by the change in dose to 95% of the planning target volume (D95) and mean dose to the parotids.

RESULTS

The treatment plans were adapted clinically for all 18 patients but only 7 actually needed an adaptation yielding 11 unnecessary adaptations. Applying a method using the daily CBCT with the planned dose distribution would have yielded only four unnecessary adaptations and no missed adaptations: a significant improvement from that done clinically.

CONCLUSION

Using the DIR between the planning CT and daily CBCT can flag cases for plan adaptation before every fraction while not requiring a new re-planning CT scan and dose recalculation.

Utilization of a hybrid finite-element based registration method to quantify heterogeneous tumor response for adaptive treatment for lung cancer patients

Tumor response to radiation treatment (RT) can be evaluated from changes in metabolic activity between two positron emission tomography (PET) images. Activity changes at individual voxels in pre-treatment PET images (PET1), however, cannot be derived until their associated PET-CT (CT1) images are appropriately registered to during-treatment PET-CT (CT2) images. This study aimed to investigate the feasibility of using deformable image registration (DIR) techniques to quantify radiation-induced metabolic changes on PET images. Five patients with non-small-cell lung cancer (NSCLC) treated with adaptive radiotherapy were considered. PET-CTs were acquired two weeks before RT and 18 fractions after the start of RT. DIR was performed from CT1 to CT2 using B-Spline and diffeomorphic Demons algorithms. The resultant displacements in the tumor region were then corrected using a hybrid finite element method (FEM). Bitmap masks generated from gross tumor volumes (GTVs) in PET1 were deformed using the four different displacement vector fields (DVFs). The conservation of total lesion glycolysis (TLG) in GTVs was used as a criterion to evaluate the quality of these registrations. The deformed masks were united to form a large mask which was then partitioned into multiple layers from center to border. The averages of SUV changes over all the layers were 1.0  ±  1.3, 1.0  ±  1.2, 0.8  ±  1.3, 1.1  ±  1.5 for the B-Spline, B-Spline  +  FEM, Demons and Demons  +  FEM algorithms, respectively. TLG changes before and after mapping using B-Spline, Demons, hybrid-B-Spline, and hybrid-Demons registrations were 20.2%, 28.3%, 8.7%, and 2.2% on average, respectively. Compared to image intensity-based DIR algorithms, the hybrid FEM modeling technique is better in preserving TLG and could be useful for evaluation of tumor response for patients with regressing tumors.

Quantitative evaluation of the performance of different deformable image registration algorithms in helical, axial, and cone-beam CT images using a mobile phantom

The goal of this project is to investigate quantitatively the performance of different deformable image registration algorithms (DIR) with helical (HCT), axial (ACT), and cone-beam CT (CBCT). The variations in the CT-number values and lengths of well-known targets moving with controlled motion were evaluated. Four DIR algorithms: Demons, Fast-Demons, Horn-Schunck and Lucas-Kanade were used to register intramodality CT images of a mobile phantom scanned with different imaging techniques. The phantom had three water-equivalent targets inserted in a low-density foam with different lengths (10-40 mm) and moved with adjustable motion amplitudes (0-20 mm) and frequencies (0-0.5 Hz). The variations in the CT-number level, volumes and shapes of these targets were measured from the spread-out of the CT-number distributions. In CBCT, most of the DIR algorithms were able to produce the actual lengths of the mobile targets; however, the CT-number values obtained from the DIR algorithms deviated from the actual CT-number of the targets. In HCT, the DIR algorithms were successful in deforming the images of the mobile targets to the images of the stationary targets producing the CT-number values and lengths of the targets for motion amplitudes <20 mm. Similarly in ACT, all DIR algorithms produced the actual CT-number values and lengths of the stationary targets for low-motion amplitudes <15 mm. The optical flow-based DIR algorithms such as the Horn-Schunck and Lucas-Kanade performed better than the Demons and Fast-Demons that are based on attraction forces particularly at large motion amplitudes. In conclusion, most of the DIR algorithms did not reproduce well the CT-number values and lengths of the targets in images that have artifacts induced by large motion amplitudes. The deviations in the CT-number values and variations in the volume of the mobile targets in the deformed CT images produced by the different DIR algorithms need to be considered carefully in the treatment planning for accurate dose calculation dose coverage of the tumor, and sparing of critical structures.