Quantifying inter-fraction cardiac substructure displacement during radiotherapy via magnetic resonance imaging guidance

Cardiac substructure delineation in radiation therapy - A state-of-the-art review

Delineation of cardiac substructures is crucial for a better understanding of radiation-related cardiotoxicities and to facilitate accurate and precise cardiac dose calculation for developing and applying risk models. This review examines recent advancements in cardiac substructure delineation in the radiation therapy (RT) context, aiming to provide a comprehensive overview of the current level of knowledge, challenges and future directions in this evolving field. Imaging used for RT planning presents challenges in reliably visualising cardiac anatomy. Although cardiac atlases and contouring guidelines aid in standardisation and reduction of variability, significant uncertainties remain in defining cardiac anatomy. Coupled with the inherent complexity of the heart, this necessitates auto-contouring for consistent large-scale data analysis and improved efficiency in prospective applications. Auto-contouring models, developed primarily for breast and lung cancer RT, have demonstrated performance comparable to manual contouring, marking a significant milestone in the evolution of cardiac delineation practices. Nevertheless, several key concerns require further investigation. There is an unmet need for expanding cardiac auto-contouring models to encompass a broader range of cancer sites. A shift in focus is needed from ensuring accuracy to enhancing the robustness and accessibility of auto-contouring models. Addressing these challenges is paramount for the integration of cardiac substructure delineation and associated risk models into routine clinical practice, thereby improving the safety of RT for future cancer patients.

Cardiac motion and its dosimetric impact during radioablation for refractory ventricular tachycardia

INTRODUCTION

Cardiac radioablation (CR) is a noninvasive treatment option for patients with refractory ventricular tachycardia (VT) during which high doses of radiation, typically 25 Gy, are delivered to myocardial scar. In this study, we investigate motion from cardiac cycle and evaluate the dosimetric impact in a cohort of patients treated with CR.

METHODS

This retrospective study included eight patients treated at our institution who had respiratory-correlated and ECG-gated 4DCT scans acquired within 2 weeks of CR. Deformable image registration was applied between maximum systole (SYS) and diastole (DIAS) CTs to assess cardiac motion. The average respiratory-correlated CT (AVG_resp ) was deformably registered to the average cardiac (AVG_cardiac ), SYS, and DIAS CTs, and contours were propagated using the deformation vector fields (DVFs). Finally, the original treatment plan was recalculated on the deformed AVG_resp CT for dosimetric assessment.

RESULTS

Motion magnitudes were measured as the mean (SD) value over the DVFs within each structure. Displacement during the cardiac cycle for all chambers was 1.4 (0.9) mm medially/laterally (ML), 1.6 (1.0) mm anteriorly/posteriorly (AP), and 3.0 (2.8) mm superiorly/inferiorly (SI). Displacement for the 12 distinct clinical target volumes (CTVs) was 1.7 (1.5) mm ML, 2.4 (1.1) mm AP, and 2.1 (1.5) SI. Displacements between the AVG_resp and AVG_cardiac scans were 4.2 (2.0) mm SI and 5.8 (1.4) mm total. Dose recalculations showed that cardiac motion may impact dosimetry, with dose to 95% of the CTV dropping from 27.0 (1.3) Gy on the AVG_resp to 20.5 (7.1) Gy as estimated on the AVG_cardiac .

CONCLUSIONS

Cardiac CTV motion in this patient cohort is on average below 3 mm, location-dependent, and when not accounted for in treatment planning may impact target coverage. Further study is needed to assess the impact of cardiac motion on clinical outcomes.

On the feasibility of cardiac substructure sparing in magnetic resonance imaging guided stereotactic lung radiotherapy

BACKGROUND

Lung stereotactic body radiotherapy (SBRT) has proven an effective treatment for medically inoperable lung tumors, even for (ultra-)central tumors. Recently, there has been growing interest in radiation-induced cardiac toxicity in lung radiotherapy. More specifically, dose to cardiac (sub-)structures (CS) was found to correlate with survival after radiotherapy.

PURPOSE

Our goal is first, to investigate the percentage of patients who require CS sparing in an magnetic resonance imaging guided lung SBRT workflow, and second, to quantify how successful implementation of cardiac sparing would be.

METHODS

The patient cohort consists of 34 patients with stage II-IV lung cancer who were treated with SBRT between 2017 and 2020. A mid-position computed tomography (CT) image was used to create treatment plans for the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) following clinical templates. Under guidance of a cardio-thoracic radiologist, 11 CS were contoured manually for each patient. Dose constraints for five CS were extracted from the literature. Patients were stratified according to their need for cardiac sparing depending on the CS dose in their non-CS constrained MR-linac treatment plans. Cardiac sparing treatment plans (CSPs) were then created and dosimetrically compared with their non-CS constrained treatment plan counterparts. CSPs complied with the departmental constraints and were considered successful when fulfilling all CS constraints, and partially successful if some CS constraints could be fulfilled. Predictors for the need for and feasibility of cardiac sparing were explored, specifically planning target volume (PTV) size, cranio-caudal (CC) distance, 3D distance, and in-field overlap volume histograms (iOVH).

RESULTS

47% of the patients (16 out of 34) were in need of cardiac sparing. A successful CSP could be created for 62.5% (10 out of 16) of these patients. Partially successful CSPs still complied with two to four CS constraints. No significant difference in dose to organs at risk (OARs) or targets was identified between CSPs and the corresponding non-CS constrained MR-linac plans. The need for cardiac sparing was found to correlate with distance in the CC direction between target and all of the individual CS (Mann-Whitney U-test p-values <10^-6 ). iOVHs revealed that complying with dose constraints for CS is primarily determined by in-plane distance and secondarily by PTV size.

CONCLUSION

We demonstrated that CS can be successfully spared in lung SBRT on the MR-linac for most of this patient cohort, without compromising doses to the tumor or to other OARs. CC distance between the target and CS can be used to predict the need for cardiac sparing. iOVHs, in combination with PTV size, can be used to predict if cardiac sparing will be successful for all constrained CS except the left ventricle.

Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans

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Cardiotoxicity is a common complication of lung cancer radiotherapy.

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Segmentation of cardiac substructures is time-consuming and challenging.

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Deep learning segmentation tools can perform this task in 3D and 4D scans.

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Performance is high when assessed geometrically, dosimetrically and clinically.

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Auto-segmentation tools may accelerate clinical workflows and enable research.

Background

Emerging data suggest that dose-sparing several key cardiac regions is prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are challenging to contour due to their complex geometry, poor soft tissue definition on computed tomography (CT) and cardiorespiratory motion artefact. A neural network was previously trained to generate the cardiac substructures using three-dimensional radiotherapy planning CT scans (3D-CT). In this study, the performance of that tool on the average intensity projection from four-dimensional (4D) CT scans (4D-AVE), now commonly used in lung radiotherapy, was evaluated.

Materials and Methods

The 4D-AVE of n=20 patients completing radiotherapy for lung cancer 2015–2020 underwent manual and automated cardiac substructure segmentation. Manual and automated substructures were compared geometrically and dosimetrically. Two senior clinicians also qualitatively assessed the auto-segmentation tool’s output.

Results

Geometric comparison of the automated and manual segmentations exhibited high levels of similarity across parameters, including volume difference (11.8% overall) and Dice similarity coefficient (0.85 overall), and were consistent with 3D-CT performance. Differences in mean (median 0.2 Gy, range −1.6–0.3 Gy) and maximum (median 0.4 Gy, range −2.2–0.9 Gy) doses to substructures were generally small. Nearly all structures (99.5 %) were deemed to be appropriate for clinical use without further editing.

Conclusions

Cardiac substructure auto-segmentation using a deep learning-based tool trained on a 3D-CT dataset was feasible on the 4D-AVE scan, meaning this tool is suitable for use on 4D-CT radiotherapy planning scans. Application of this tool would increase the practicality of routine clinical cardiac substructure delineation, and enable further cardiac radiation effects research.

Proof-of-concept for x-ray based real-time image guidance during cardiac radioablation

Cardiac radioablation offers non-invasive treatments for refractory arrhythmias. However, treatment delivery for this technique remains challenging. In this paper, we introduce the first method for real-time image guidance during cardiac radioablation for refractory atrial fibrillation on a standard linear accelerator. Our proposed method utilizes direct diaphragm tracking on intrafraction images to estimate the respiratory component of cardiac substructure motion. We compare this method to treatment scenarios without real-time image guidance using the 4D-XCAT digital phantom. Pre-treatment and intrafraction imaging was simulated for 8 phantoms with unique anatomies programmed using cardiorespiratory motion from healthy volunteers. As every voxel in the 4D-XCAT phantom is labelled precisely according to the corresponding anatomical structure, this provided ground-truth for quantitative evaluation. Tracking performance was compared to the ground-truth for simulations with and without real-time image guidance using the left atrium as an exemplar target. Differences in target volume size, mean volumetric coverage, minimum volumetric coverage and geometric error were recorded for each simulation. We observed that differences in target volume size were statistically significant (p < 0.001) across treatment scenarios and that real-time image guidance enabled reductions in target volume size ranging from 11% to 24%. Differences in mean and minimum volumetric coverage were statistically insignificant (bothp = 0.35) while differences in geometric error were statistically significant (p = 0.039). The results of this study provide proof-of-concept for x-ray based real-time image guidance during cardiac radioablation.