[Introduction of high-precision stereotactic body radiotherapy of lung tumors at Markusovszky Hospital]

INTRODUCTION

A new, modern computed tomograph simulator was installed in the Oncology Department of the Markusovsky University Teaching Hospital from September 2021. The computed tomography simulator not only makes the work of specialists easier with its automatic contouring tool, but is also able to produce four-dimensional computed tomography scans. This facility is essential for radiotherapy of lung and breast cancer patients.

OBJECTIVE

In this paper, we briefly review lung tumors and their treatment options, focusing on radiotherapy requiring high precision. We summarize patient selection criteria, the quality assurance processes for planning and treatment, and the experience gained in treating patients.

METHOD

5 patients were selected for our study. Their disease met the following criteria: 1 nodule, tumor diameter not exceeding 5 cm, patient was inoperable or negated surgery. The planning computed tomography scan was performed with Siemens Somatom go.Sim. At each respiratory phase, the tumor conturs were drawn and then aggregated as an integrated volume and an irradiation plan was prepared on this image. The treatments were performed on a Varian TrueBeam accelerator.

RESULTS

Before each treatment, an adjusting CT scan was taken. The higher dose (4 × 12 Gy) treatment caused a reduction in tumor size on the last adjustment scan.

DISCUSSION

Stereotaxic treatment, which is already available in Szombathely, may be a good alternative in the treatment of patients with inoperable lung cancer. The method is not burdensome for patients: fewer sessions, short treatment time.

CONCLUSION

In the future, we would like to improve the accelerator with a breath capture system, which will allow even more precise treatment. Orv Hetil. 2022; 163(52): 2079-2087.

A Comparison of Isotoxic Dose-escalated Radiotherapy in Lung Cancer with Moderate Deep Inspiration Breath Hold, Mid-ventilation and Internal Target Volume Techniques

AIMS

With interest in normal tissue sparing and dose-escalated radiotherapy in the treatment of inoperable locally advanced non-small cell lung cancer, this study investigated the impact of motion-managed moderate deep inspiration breath hold (mDIBH) on normal tissue sparing and dose-escalation potential and compared this to planning with a four-dimensional motion-encompassing internal target volume or motion-compensating mid-ventilation approach.

MATERIALS AND METHODS

Twenty-one patients underwent four-dimensional and mDIBH planning computed tomography scans. Internal and mid-ventilation target volumes were generated on the four-dimensional scan, with mDIBH target volumes generated on the mDIBH scan. Isotoxic target dose-escalation guidelines were used to generate six plans per patient: three with a target dose cap and three without. Target dose-escalation potential, normal tissue complication probability and differences in pre-specified dose-volume metrics were evaluated for the three motion-management techniques.

RESULTS

The mean total lung volume was significantly greater with mDIBH compared with four-dimensional scans. Lung dose (mean and V21 Gy) and mean heart dose were significantly reduced with mDIBH in comparison with four-dimensional-based approaches, and this translated to a significant reduction in heart and lung normal tissue complication probability with mDIBH. In 20/21 patients, the trial target prescription dose cap of 79.2 Gy was achievable with all motion-management techniques.

CONCLUSION

mDIBH aids lung and heart dose sparing in isotoxic dose-escalated radiotherapy compared with four-dimensional planning techniques. Given concerns about lung and cardiac toxicity, particularly in an era of consolidation immunotherapy, reduced normal tissue doses may be advantageous for treatment tolerance and outcome.

Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy

Purpose.Respiration-induced motion introduces significant positioning uncertainties in radiotherapy treatments for thoracic sites. Accounting for this motion is a non-trivial task commonly addressed with surrogate-based strategies and latency compensating techniques. This study investigates the potential of a new unified probabilistic framework to predict both future target motion in real-time from a surrogate signal and associated uncertainty.Method.A Bayesian approach is developed, based on a Kalman filter theory adapted specifically for surrogate measurements. Breathing motions are collected simultaneously from a lung target, two external surrogates (abdominal and thoracic markers) and an internal surrogate (liver structure) for 9 volunteers during 4 min, in which severe breathing changes occur to assess the robustness of the method. A comparison with an artificial non-linear neural network (NN) is performed, although no confidence interval prediction is provided. A static worst-case scenario and a simple static design are investigated.Results.Although the NN can reduce the prediction errors from thoracic surrogate in some cases, the Bayesian framework outperforms in most cases the NN when using the other surrogates: bias on predictions is reduced by 38% and 16% on average when using respectively the liver and the abdomen for the simple scenario, and by respectively 40% and 31% for the worst-case scenario. The standard deviation of residuals is reduced on average by up to 42%. The Bayesian method is also found to be more robust to increasing latencies. The thoracic marker appears to be less reliable to predict the target position, while the liver shows to be a better surrogate. A statistical test confirms the significance of both observations.Conclusion.The proposed framework predicts both the future target position and the associated uncertainty, which can be valuably used to further assist motion management decisions. Further investigation is required to improve the predictions by using an adaptive version of the proposed framework.

Automatic delineation of cardiac substructures using a region-based fully convolutional network

PURPOSE

Radiation dose to specific cardiac substructures, such as the atria and ventricles, has been linked to post-treatment toxicity and has shown to be more predictive of these toxicities than dose to the whole heart. A deep learning-based algorithm for automatic generation of these contours is proposed to aid in either retrospective or prospective dosimetric studies to better understand the relationship between radiation dose and toxicities.

METHODS

The proposed method uses a mask-scoring regional convolutional neural network (RCNN) which consists of five major subnetworks: backbone, regional proposal network (RPN), RCNN head, mask head, and mask-scoring head. Multiscale feature maps are learned from computed tomography (CT) via the backbone network. The RPN utilizes these feature maps to detect the location and region-of-interest (ROI) of all substructures, and the final three subnetworks work in series to extract structural information from these ROIs. The network is trained using 55 patient CT datasets, with 22 patients having contrast scans. Threefold cross validation (CV) is used for evaluation on 45 datasets, and a separate cohort of 10 patients are used for holdout evaluation. The proposed method is compared to a 3D UNet.

RESULTS

The proposed method produces contours that are qualitatively similar to the ground truth contours. Quantitatively, the proposed method achieved average Dice score coefficients (DSCs) for the whole heart, chambers, great vessels, coronary arteries, the valves of the heart of 0.96, 0.94, 0.93, 0.66, and 0.77 respectively, outperforming the 3D UNet, which achieved DSCs of 0.92, 0.87, 0.88, 0.48, and 0.59 for the corresponding substructure groups. Mean surface distances (MSDs) between substructures segmented by the proposed method and the ground truth were <2 mm except for the left anterior descending coronary artery and the mitral and tricuspid valves, and <5 mm for all substructures. When dividing results into noncontrast and contrast datasets, the model performed statistically significantly better in terms of DSC, MSD, centroid mean distance (CMD), and volume difference for the chambers and whole heart with contrast. Notably, the presence of contrast did not statistically significantly affect coronary artery segmentation DSC or MSD. After network training, all substructures and the whole heart can be segmented on new datasets in less than 5 s.

CONCLUSIONS

A deep learning network was trained for automatic delineation of cardiac substructures based on CT alone. The proposed method can be used as a tool to investigate the relationship between cardiac substructure dose and treatment toxicities.

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Unethical not to Investigate Radiotherapy for COVID-19

The primum non nocere letter by Boon et al. urged caution and careful examination of the evidence and logistics of low-dose radiotherapy in COVID-19 patients. This is exactly what was requested in March and what has occurred since late April 2020 when the first phase I/II clinical trial was approved at the Winship Cancer Institute, Emory University Hospital. The preprint of day-7 interim results by the investigators concluded, "In a small pilot trial of 5 oxygen-dependent patients with COVID-19 pneumonia, low-dose whole-lung radiation led to rapid improvement in clinical status, encephalopathy, and radiographic infiltrates without acute toxicity or worsening the cytokine storm. Low-dose whole-lung radiation appears to be safe, shows early promise of efficacy, and warrants larger prospective trials." Preliminary results from another clinical trial gave similar results. In conclusion, the authors believe it would be unethical not to investigate radiotherapy as a potential remedy against COVID-19 induced pneumonia.

Development and prospective in-patient proof-of-concept validation of a surface photogrammetry + CT-based volumetric motion model for lung radiotherapy

PURPOSE

We develop and validate a motion model that uses real-time surface photogrammetry acquired concurrently with four-dimensional computed tomography (4DCT) to estimate respiration-induced changes within the entire irradiated volume, over arbitrarily many respiratory cycles.

METHODS

A research, couch-mounted, VisionRT (VRT) system was used to acquire optical surface data (15 Hz, ROI = 15 × 20 cm² ) from the thoraco-abdominal surface of a consented lung SBRT patient, concurrently with their standard-of-care 4DCT. The end-exhalation phase from the 4DCT was regarded as reference and for each remaining phase, deformation vector fields (DVFs) with respect to the reference phase were computed. To reduce dimensionality, the first two principal components (PCs) of the matrix of nine DVFs were calculated. In parallel, ten phase-averaged VRT surfaces were created. Surface DVFs and corresponding PCs were computed. A principal least squares regression was used to relate the PCs of surface DVF to those of volume DVFs, establishing a relationship between time-varying surface and the underlying time-varying volume. Proof-of-concept validation was performed during each treatment fraction by concurrently acquiring 30 s time series of real-time surface data and "ground truth" kV fluoroscopic data (FL). A ray-tracing algorithm was used to create a digitally reconstructed fluorograph (DRF), and motion trajectories of high-contrast, soft-tissue, anatomical features in the DRF were compared with those from kV FL.

RESULTS

For five of the six fluoroscopic acquisition sessions, the model out-performed 4DCT in predicting contour Dice coefficient with respect to fluoroscopy-derived contours. Similarly, the model exhibited a marked improvement over 4DCT for patch positions on the diaphragm. Model patch position errors varied from 5 to -15 mm while 4DCT errors ranged between 5 and -22.4 mm. For one fluoroscopic acquisition, a marked change in the a priori internal-external correlation resulted in model errors comparable to those of 4DCT.

CONCLUSIONS

We described the development and a proof-of-concept validation for a volumetric motion model that uses surface photogrammetry to correlate the time-varying thoraco-abdominal surface to the time-varying internal thoraco-abdominal volume. These early results indicate that the proposed approach can result in a marked improvement over 4DCT. While limited by the duration of the fluoroscopic acquisitions as well as the resolution of the acquired images, the DRF-based proof-of-concept technique developed here is model-agnostic, and therefore, has the potential to be used as an in-patient validation tool for other volumetric motion models.

Correlation of Regional Lung Ventilation and Gas Transfer to Red Blood Cells: Implications for Functional-Avoidance Radiation Therapy Planning

PURPOSE

To investigate the degree to which lung ventilation and gas exchange are regionally correlated, using the emerging technology of hyperpolarized (HP)-129Xe magnetic resonance imaging (MRI).

METHODS AND MATERIALS

Hyperpolarized-129Xe MRI studies were performed on 17 institutional review board-approved human subjects, including 13 healthy volunteers, 1 emphysema patient, and 3 non-small cell lung cancer patients imaged before and approximately 11 weeks after radiation therapy (RT). Subjects inhaled 1 L of HP-129Xe mixture, followed by the acquisition of interleaved ventilation and gas exchange images, from which maps were obtained of the relative HP-129Xe distribution in three states: (1) gaseous, in lung airspaces; (2) dissolved interstitially, in alveolar barrier tissue; and (3) transferred to red blood cells (RBCs), in the capillary vasculature. The relative spatial distributions of HP-129Xe in airspaces (regional ventilation) and RBCs (regional gas transfer) were compared. Further, we investigated the degree to which ventilation and RBC transfer images identified similar functional regions of interest (ROIs) suitable for functionally guided RT. For the RT patients, both ventilation and RBC functional images were used to calculate differences in the lung dose-function histogram and functional effective uniform dose.

RESULTS

The correlation of ventilation and RBC transfer was ρ = 0.39 ± 0.15 in healthy volunteers. For the RT patients, this correlation was ρ = 0.53 ± 0.02 before treatment and ρ = 0.39 ± 0.07 after treatment; for the emphysema patient it was ρ = 0.24. Comparing functional ROIs, ventilation and RBC transfer demonstrated poor spatial agreement: Dice similarity coefficient = 0.50 ± 0.07 and 0.26 ± 0.12 for the highest-33%- and highest-10%-function ROIs in healthy volunteers, and in RT patients (before treatment) these were 0.58 ± 0.04 and 0.40 ± 0.04. The average magnitude of the differences between RBC- and ventilation-derived functional effective uniform dose, fV20Gy, fV10Gy, and fV5Gy were 1.5 ± 1.4 Gy, 4.1% ± 3.8%, 5.0% ± 3.8%, and 5.3% ± 3.9%, respectively.

CONCLUSION

Ventilation may not be an effective surrogate for true regional lung function for all patients.