Lung Stereotactic Body Radiation Therapy Research Articles

Treatment planning and delivery practice of lung SBRT: Results of the 2022 ESTRO physics survey

Background and purposeThe use of Stereotactic Body Radiation Therapy (SBRT) in lung cancer is increasing. However, there is no consensus on the most appropriate treatment planning and delivery practice for lung SBRT. To gauge the range of practice, quantify its variability and identify where consensus might be achieved, ESTRO surveyed the medical physics community. Materials and methodsAn online survey was distributed to ESTRO’s physicist membership in 2022, covering experience, dose and fractionation, target delineation, dose calculation and planning practice, imaging protocols, and quality assurance. ResultsTwo-hundred and forty-four unique answers were collected after data cleaning. Most respondents were from Europe the majority of which had more than 5 years’ experience in SBRT. The large majority of respondents deliver lung SBRT with the VMAT technique on C-arm Linear Accelerators (Linacs) employing daily pre-treatment CBCT imaging. A broad spectrum of fractionation schemes were reported, alongside an equally wide range of dose prescription protocols. A clear preference was noted for prescribing to 95% or greater of the PTV. Several issues emerged regarding the dose calculation algorithm: 22% did not state it while 24% neglected to specify the conditions under which the dose was calculated. Contouring was usually performed on Maximum or Average Intensity Projection images while dose was mainly computed on the latter. No clear indications emerged for plan homogeneity, complexity, and conformity assessment. Approximately 40% of the responders participated in inter-centre credentialing of SBRT in the last five years. Substantial differences emerged between high and low experience centres, with the latter employing less accurate algorithms and older equipment. ConclusionThe survey revealed an evident heterogeneity in numerous aspects of the clinical implementation of lung SBRT treatments. International guidelines and codes of practice might promote harmonisation.

Dynamic anthropomorphic thorax phantom for quality assurance of motion management in radiotherapy

Background and purposeMotion management techniques are important to spare the healthy tissue adequately. However, they are complex and need dedicated quality assurance. The aim of this study was to create a dynamic phantom designed for quality assurance and to replicate a patient’s size, anatomy, and tissue density. Materials and methodsA computed tomography (CT) scan of a cancer patient was used to create molds for the lungs, heart, ribs, and vertebral column via additive manufacturing. A pump system and software were developed to simulate respiratory dynamics. The extent of respiratory motion was quantified using a 4DCT scan. End-to-end tests were conducted to evaluate two motion management techniques for lung stereotactic body radiotherapy (SBRT). ResultsThe chest wall moved between 4 mm and 13 mm anteriorly and 2 mm to 7 mm laterally during the breathing. The diaphragm exhibited superior-inferior movement ranging from 5 mm to 16 mm in the left lung and 10 mm to 36 mm in the right lung. The left lung tumor displaced ± 7 mm superior-inferiorly and anterior-posteriorly. The CT numbers were for lung: −716 ± 108 HU (phantom) and −713 ± 70 HU (patient); bone: 460 ± 20 HU (phantom) and 458 ± 206 HU (patient); soft tissue: 92 ± 9 HU (phantom) and 60 ± 25 HU (patient). The end-to-end testing showed an excellent agreement between the measured and the calculated dose for ion chamber and film dosimetry. ConclusionsThe phantom is recommended for quality assurance, evaluating the institution’s specific planning and motion management strategies either through end-to-end testing or as an external audit phantom.

Motion-Inclusive Treatment Planning to Assess Normal Tissue Dose for Central Lung Stereotactic Body Radiation Therapy

PurposeFor lung SBRT, 4DCT is often used to delineate target volumes, whereas organs at risk (OARs) are typically outlined on either average intensity projection (AIP) or mid-ventilation (MidV = 30% phase) images. AIP has been widely adopted as it represents a true average, but image blurring often precludes accurate contouring of critical structures such as central airways. Here we compare AIP vs MidV planning for centrally located tumors via respiratory motion-inclusive (RMI) plans to better evaluate dose delivered throughout the breathing cycle. MethodsIndependently contoured and optimized AIP and MidV plans were created for 16 treatments and rigidly copied to each of the 10 breathing phase-specific CT image sets. Resulting dose distributions were deformably registered back to the MidV image set (used as reference due to clearer depiction of anatomy compared to motion-blurred AIP) and averaged to create RMI plans. Doses to central OARs were compared between plans. ResultsMean absolute dose differences were low for all comparisons (range 0.01 – 2.87 Gy), however, individual plans exhibited differences > 20 Gy. Dose differences >5 Gy were observed most often for plan comparisons involving AIP-based plans (MidV vs AIP 23, AIP RMI vs AIP 12, MidV RMI vs AIP RMI 7, and MidV RMI vs MidV 8 times). Inclusion of respiratory motion reduced large dose differences. Standard OAR thresholds were exceeded up to five times for each plan comparison scenario and always involved PBT D4cc tolerance dose. AIP-based contours were larger by on average 3-15%. ConclusionLarge dose differences were observed when plans with AIP-based contours were compared with MidV-based contours indicating that observed dose differences were likely due to contoured volume differences rather than the effect of motion. Because of blurring with AIP images, MidV RMI-based planning may offer a more accurate method to determine dose to critical OARs in the presence of respiratory motion.

Abstract 7396: Development of deep neural networks that guide more individualized radiotherapy to the lung

Abstract Introduction: Radiotherapy is dependent on an expert-based model that impedes implementation of many potentially useful applications that could aid decision makers. This model is a barrier to a learning healthcare system due to noise introduced by decision-maker variation. Surmounting this barrier will require compelling clinical outcome data and highly useful and accurate information systems. We sought to develop new augmented-intelligence capabilities to better predict both high-dose lung irradiation treatment efficacy (i.e., local tumor control) and toxicity (i.e., radiation-induced pneumonitis). Methods: We curated one of the largest datasets of lung stereotactic body radiotherapy (SBRT) datasets globally (~1200 cases). We input pre-therapy lung computed tomography (CT) images using a bounding box approach that captures the gross tumor volume and adjacent normal lung into a multi-task deep neural network to generate an image fingerprint that predicts local failures after treatment. Separately, we input axial slices from CT scans of the lung into convolutional neural network (CNN) and trained using an outcome per slice method. We then aggregated intra-patient slices into a recurrent neural network (RNN) to estimate individual patient risk for pneumonitis. We validated our findings with the independent study cohort. Risk scores for local failure and pneumonitis were combined with clinical variables to assess the relative contribution of the deep learning-derived image risk scores. Results: A total of 1045 patients met our eligibility criteria. Radiation treatments in patients with high local failure risk scores failed at a significantly higher rate than in patients with low scores; 3-year cumulative incidence of local failure in the internal study cohort was 20·3% (16·0-24·9) in patients with high scores and 5·7% (95% CI 3·5-8·8) in patients with low scores (hazard ratio [HR]=3·64 [95% CI 2·19-6·05], p<0·0001). Models that included the local failure risk score and clinical variables predicted treatment failures with a concordance index (C-index) of 0·72 (95% CI 0·67-0·77), a significant improvement compared with classical radiomics (p<0·0001) or clinical variables (p<0·0001) alone. Radiation pneumonitis risk scores were highly predictive of toxicity, with an accuracy and CI of 0.78 (95% CI 0.72-0.85). A model that includes dosimetry variates (V20), improved the CI to 0.85 (95% CI 0.80-0.90). Conclusions: The image-based deep learning frameworks developed herein represent the first opportunity to use medical images to individualize radiotherapy dose and toxicity predictions. Our results signify a new roadmap for deep learning-guided predictions and treatment guidance in the image-replete and highly standardized discipline of radiation oncology. A phase 2 study assessing machine-derived dose predictions is currently enrolling patients. Citation Format: Mohamed Abazeed. Development of deep neural networks that guide more individualized radiotherapy to the lung [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7396.

Erratum: Effects of respiratory motion on accurate four-dimensional computed tomography acquisition and plan delivery for lung stereotactic body radiation therapy

Erratum: Effects of respiratory motion on accurate four-dimensional computed tomography acquisition and plan delivery for lung stereotactic body radiation therapy

Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.

Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strategy based on the risk of recurrence. In this study, we evaluated the performance of both machine learning models using positron emission tomography (PET) and computed tomography (CT) radiomic features for predicting recurrence after SBRT. Planning CT and PET images of 82 non-small cell lung cancer patients who performed SBRT at our hospital were used. First, tumors were delineated on each CT and PET of each patient, and 111 unique radiomic features were extracted, respectively. Next, the 10 features were selected using three different feature selection algorithms, respectively. Recurrence prediction models based on the selected features and four different machine learning algorithms were developed, respectively. Finally, we compared the predictive performance of each model for each recurrence pattern using the mean area under the curve (AUC) calculated following the 0.632+ bootstrap method. The highest performance for local recurrence, regional lymph node metastasis, and distant metastasis were observed in models using Support vector machine with PET features (mean AUC=0.646), Naive Bayes with PET features (mean AUC=0.611), and Support vector machine with CT features (mean AUC=0.645), respectively. We comprehensively evaluated the performance of prediction model developed for recurrence following SBRT. The model in this study would provide information to predict the recurrence pattern and assist in making treatment strategies.

Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking

Background and purposeMotion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available – fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm – Deep Match. MethodDeep Match consists of four self-definable stages – training-free feature extractor, similarity measurements for location proposal, local refinements, and uncertainty level prediction for constructing a more trustworthy and versatile pipeline. Deep Match was validated on a 10 (38 fractions; 2661 images) patient cohort whose lung tumor was trackable on one X-ray view, while the second view did not offer sufficient conspicuity for tumor tracking using existing methods. The patient cohort was stratified into subgroups based on tumor sizes (<10 mm, 10–15 mm, and >15 mm) and tumor locations (with/without thoracic anatomy overlapping). ResultsOn X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and <3 mm superior/inferior distance (SID) ∼1 mm standard deviation for all the patients. ConclusionDeep Match is a zero-shot learning network that explores the intrinsic neural network benefits without training on patient data. With Deep Match, fiducial-free tracking can be extended to more patients with small tumors and with tumors obscured by overlapping anatomy.

Dosimetric evaluation in Helical TomoTherapy for lung SBRT using Monte Carlo-based independent dose verification software.

To elucidate the dosimetric errors caused by a model-based algorithm in lung stereotactic body radiation therapy (SBRT) with Helical TomoTherapy (HT) using Monte Carlo (MC)-based dose verification software. For 38 plans of lung SBRT, the dose calculation accuracy of a treatment planning system (TPS) of HT was compared with the results of DoseCHECK, the commercial MC-based independent verification software. The following indices were extracted to evaluate the correlation of dosimetric errors: (1) target volume, (2) average computed tomography (CT) value of the planning target volume (PTV) margin, and (3) average CT value of surrounding 2-mm area of the PTV (PTV ring). Receiver operating characteristic (ROC) analyses determined the threshold for 5% of differences in PTV D95% . Then, the 38 plans were classified into two groups using the cutoff values of ROC analysis for these three indices. Dosimetric differences between groups were statistically compared using the Mann-Whitney U test. TPS of HT overestimated by more than 5% in the PTV D95% in 16 of 38 plans. The PTV ring showed the strongest correlation with dosimetric differences. The cutoff value for the target volume, the PTV margin, and the PTV ring was 14.7 cc, -754 HU, and -708 HU, respectively. The area under the curve (AUC) for the target volume, the PTV margin, and the PTV ring were 0.835, 0.878, and 0.932, respectively. Dosimetric errors more than 5% were observed when the PTV volume was less than 15 cc or when the CT value around the target was less than -700 HU. The TPS of HT might overestimate the PTV dose by more than 5% if any the three indices in this study were below threshold. Therefore, independent verification with an MC-based algorithm should be strongly recommended for lung SBRT in HT.

Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy

PurposeThe study aims to develop and validate a combined model for predicting 3-year cancer-specific survival (CSS) in lung cancer patients treated with stereotactic body radiation therapy (SBRT) by integrating clinical and radiomic parameters.MethodsClinical data and pre-treatment CT images were collected from 102 patients treated with lung SBRT. Multivariate logistic regression and the least absolute shrinkage and selection operator were used to determine the clinical and radiomic factors associated with 3-year CSS. Three prediction models were developed using clinical factors, radiomic factors, and a combination of both. The performance of the models was assessed using receiver operating characteristic curve and calibration curve. A nomogram was also created to visualize the 3-year CSS prediction.ResultsWith a 36-month follow-up, 40 patients (39.2%) died of lung cancer and 62 patients (60.8%) survived. Three clinical factors, including gender, clinical stage, and lymphocyte ratio, along with three radiomic features, were found to be independent factors correlated with 3-year CSS. The area under the curve values for the clinical, radiomic, and combined model were 0.839 (95% CI 0.735–0.914), 0.886 (95% CI 0.790–0.948), and 0.914 (95% CI 0.825–0.966) in the training cohort, and 0.757 (95% CI 0.580–0.887), 0.818 (95% CI 0.648–0.929), and 0.843 (95% CI 0.677–0.944) in the validation cohort, respectively. Additionally, the calibration curve demonstrated good calibration performance and the nomogram created from the combined model showed potential for clinical utility.ConclusionA clinical-radiomic model was developed to predict the 3-year CSS for lung cancer patients treated with SBRT.

Intra- and inter-fractional variations of tumors with fiducial markers measured using respiratory-correlated computed tomography images for respiratory gated lung stereotactic body radiation therapy.

This study evaluated the intra- and inter-fractional variation of tumors with fiducial markers (FMs), relative to the tumor-FM distance, to establish how close an FM should be inserted for respiratory-gated stereotactic body radiation therapy (RG-SBRT). Forty-five lung tumors treated with RG-SBRT were enrolled. End-expiratory computed tomography (CT) (CTplan ) and four-dimensional-CT (4D-CT) scans were obtained for planning. End-expiratory CT (CTfr ) scanning was performed before each fraction. The FMs were divided into two groups based on the median tumor-FM distance in the CTplan (Dp ). For the intra-fractional variation, the correlations between the corresponding tumor and FM intra-fractional motions, defined as the centroid coordinates of those in each 0-90% phase, with the 50% phase of 4D-CT as the origin, were calculated in the left-right, anterior-posterior, and superior-inferior directions. Furthermore, the maximum difference in the tumor-FM distance in each phase of 4D-CT scan, based on those in the 50% phase of 4D-CT scan (Dmax ), was obtained. Inter-fractional variation was defined as the maximum distance between the tumors in CTplan and CTfr , when the CT scans were fused based on each FM or vertebra. The median Dp was 26.1mm. While FM intra-fractional motions were significantly and strongly correlated with the tumor intra-fractional motions in only anterior-posterior and superior-inferior directions for the Dp >26mm group, they were significantly and strongly correlated in all directions for the Dp ≤ 26mm group. In all directions, Dmax values of the Dp ≤ 26mm group were lower than those of the Dp >26mm group. The inter-fractional variations based on the Dp ≤ 26mm were smaller than those on the Dp >26mm and on the vertebra in all directions. Regarding intra- and inter-fractional variation, FMs for Dp ≤ 26mm can increase the accuracy for RG-SBRT.

Effects of respiratory motion on accurate four-dimensional computed tomography acquisition and plan delivery for lung stereotactic body radiation therapy

AbstractThis study evaluated the impact of respiratory movement on four-dimensional computed tomography (4D-CT) acquisition and radiotherapy delivery. QUASAR™ Respiratory Motion Phantom with nine sinusoidal waveforms that combined three amplitudes (10, 20, and 30 mm) and three breaths per minute (BPMs) (10, 15, and 20) was used. We acquired 4D-CT for nine respective waveforms and measured and compared geometrical factors (volume, shape, and motion) to reference static CT. We optimized and delivered radiotherapy plans on a Vital Beam and evaluated gamma for dose distribution analysis with three acceptance criteria: 3%/3 mm, 3%/3 mm, and 5%/5 mm. Geometrical analysis revealed that an increase in amplitude decreased 4D-CT acquisition accuracy. Geometrical factors for an amplitude of ≤ 20 mm were within tolerance limits, however, amplitude 30 mm resulted in significant deviations over tolerance limits. BPM had a minimal effect on geometrical accuracy when accurate pitch values for respective BPMs were selected for scanning. Dose distribution analysis also indicated that an increase in amplitude decreased the gamma passing rates. Amplitude 30 mm failed to achieve a 95% passing rate for all three gamma evaluation criteria adopted in the present study. Overall, our results demonstrate that respiratory motion has significant effects on the accuracy of both 4D-CT acquisition and planned radiotherapy delivery. Small amplitude of ≤ 20 mm is required to ensure accuracy.

Stereotactic body radiotherapy in lung cancer: a contemporary review.

The treatment of early stage non-small cell lung cancer (NSCLC) has improved enormously in the last two decades. Although surgery is not the only choice, lobectomy is still the gold standard treatment type for operable patients. For inoperable patients stereotactic body radiotherapy (SBRT) should be offered, reaching very high local control and overall survival rates. With SBRT we can precisely irradiate small, well-defined lesions with high doses. To select the appropriate fractionation schedule it is important to determine the size, localization and extent of the lung tumor. The introduction of novel and further developed planning (contouring guidelines, diagnostic image application, planning systems) and delivery techniques (motion management, image guided radiotherapy) led to lower rates of side effects and more conformal target volume coverage. The purpose of this study is to summarize the current developments, randomised studies, guidelines about lung SBRT, with emphasis on the possibility of increasing local control and overall rates in "fit," operable patients as well, so SBRT would be eligible in place of surgery.

Radiological imaging protection: a study on imaging dose used while planning computed tomography for external radiotherapy in Japan.

Previous studies have primarily focused on quality of imaging in radiotherapy planning computed tomography (RTCT), with few investigations on imaging doses. To our knowledge, this is the first study aimed to investigate the imaging dose in RTCT to determine baseline data for establishing national diagnostic reference levels (DRLs) in Japanese institutions. A survey questionnaire was sent to domestic RT institutions between 10 October and 16 December 2021. The questionnaire items were volume computed tomography dose index (CTDIvol), dose-length product (DLP), and acquisition parameters, including use of auto exposure image control (AEC) or image-improving reconstruction option (IIRO) for brain stereotactic irradiation (brain STI), head and neck (HN) intensity-modulated radiotherapy (IMRT), lung stereotactic body radiotherapy (lung SBRT), breast-conserving radiotherapy (breast RT), and prostate IMRT protocols. Details on the use of motion-management techniques for lung SBRT were collected. Consequently, we collected 328 responses. The 75th percentiles of CTDIvol were 92, 33, 86, 23, and 32mGy and those of DLP were 2805, 1301, 2416, 930, and 1158mGy·cm for brain STI, HN IMRT, lung SBRT, breast RT, and prostate IMRT, respectively. CTDIvol and DLP values in institutions that used AEC or IIRO were lower than those without use for almost all sites. The 75th percentiles of DLP in each treatment technique for lung SBRT were 2541, 2034, 2336, and 2730mGy·cm for free breathing, breath holding, gating technique, and real-time tumor tracking technique, respectively. Our data will help in establishing DRLs for RTCT protocols, thus reducing imaging doses in Japan.

Stereotactic body radiotherapy with volumetric intensity-modulated arc therapy and flattening filter-free beams: dosimetric considerations.

The present study comparatively evaluates the impact of energy-matched flattening filter-free (FFF) photon beams with different energy levels on the physical-dosimetric quality of lung and liver stereotactic body radiotherapy (SBRT) treatment plans. For this purpose, 54different lung and liver lesions from 44patients who had already received SBRT combined with volumetric modulated arc therapy (VMAT) were included in this retrospective planning study. Planning computed tomography scans already available were used for the renewed planning with 6MV, 6MV-FFF, 10MV, and 10MV-FFF under constant planning objectives. The treatment delivery data, dosimetric distributions, and dose-volume histograms as well as parameters such as the conformity index and gradient indices were the basis for the evaluation and comparison of treatment plans. Asignificant reduction of beam-on time (BOT) was achieved due to the high dose rates of FFF beams. In addition, we showed that for FFF beams compared to flattened beams of the same energy level, smaller planning target volumes (PTV) require fewer monitor units (MU) than larger PTVs. An equal to slightly superior target volume coverage and sparing of healthy tissue as well as organs at risk in both lung and liver lesions were found. Significant differences were seen mainly in the medium to lower dose range. We found that FFF beams together with VMAT represent an excellent combination for SBRT of lung or liver lesions with shortest BOT for 10MV-FFF but significant dose savings for 6MV-FFF in lung lesions.

A novel lung SBRT treatment planning: Inverse VMAT plan with leaf motion limitation to ensure the irradiation reproducibility of a moving target

This study exposed the implementation of a novel technique (VMATLSL) for the planning of moving targets in lung stereotactic body radiation therapy (SBRT). This new technique has been compared to static conformal radiotherapy (3D-CRT), volumetric-modulated arc therapy (VMAT), and dynamic conformal arc (DCA). The rationale of this study was to lower geometric complexity (54.9% lower than full VMAT) and hence ensure the reproducibility of the treatment delivery by reducing the risk for interplay errors induced by respiratory motion. Dosimetry metrics were studied with a cohort of 30 patients. Our results showed that leaf speed limitation provided conformal number (CN) close to the VMAT (median CN of VMATLSL is 0.78 vs 0.82 for full VMAT) and was a significant improvement on 3D-CRT and DCA with segment-weight optimized (respectively 0.55 and 0.57). This novel technique is an alternative to VMAT or DCA for lung SBRT treatments, combining independence from the patient's breathing pattern, from the size and amplitude of the lesion, free from interplay effect, and with dosimetry metrics close to the best that could be achieved with full VMAT.

Pectoralis Major Radiation Myonecrosis After Lung Stereotactic Body Radiation Therapy

Pectoralis Major Radiation Myonecrosis After Lung Stereotactic Body Radiation Therapy

Feasibility of fast, four-dimensional computed tomography-based O-ring LINAC plans for lung stereotactic body radiotherapy in patients with poor performance status

PurposeWe aimed to retrospectively analyzed the feasibility of fast four-dimensional computed tomography (4DCT)-based O-ring LINAC treatment for patients with an average respiratory amplitude was&amp;lt; 0.5 cm and who cannot endure long treatment times due to poor performance status in lung 4D-stereotactic body radiotherapy (SBRT).MethodsThis study included data of 38 patients who received lung 4D-SBRT and had average respiratory amplitude&amp;lt; 0.5 cm in the full phase. C-arm LINAC plans were based on 4DCT data obtained at phase values ranging from 20–70% using a C-arm LINAC. O-ring LINAC plans were retrospectively established based on 4DCT data obtained at phase values of 0–90% using an O-ring LINAC. The conformity index (CI), homogeneity index (HI), and gradient measurement of the planning target volumes (PTV) were analyzed to compare dosimetric data between C-arm LINAC and O-ring LINAC plans. Organs at risk were analyzed in accordance with the Radiation Therapy Oncology Group 0915 protocol. Treatment delivery time and total monitor units were analyzed to compare the efficiency of treatment delivery. Statistical comparisons were performed using the Wilcoxon signed-rank test (P&amp;lt; 0.05).ResultsFor the PTV, there was no significant difference in the CI or HI between C-arm LINAC and O-ring LINAC plans. For organs-at-risk, all plans met the criteria for dose constraint. There was a significant difference between C-arm LINAC and O-ring LINAC plans except in the spinal cord. Treatment delivery time was 92% longer for C-arm LINAC plans than for O-ring LINAC plans. The total MU value for C-arm LINAC plans was 9.6% higher than that for O-ring LINAC plans.ConclusionWe verified the feasibility of fast 4DCT-based O-ring LINAC treatment for patients with average respiratory amplitude&amp;lt; 0.5 cm and who cannot endure long treatment times due to poor performance status in lung 4D-SBRT.

Dosimetric comparison of four-dimensional computed tomography based internal target volume against variations in respiratory motion during treatment between volumetric modulated arc therapy and three-dimensional conformal radiotherapy in lung stereotactic body radiotherapy.

This study focused on the dosimetric impact of variations in respiratory motion during lung stereotactic body radiotherapy (SBRT). Dosimetric comparisons between volumetric modulated arc therapy (VMAT) and three-dimensional conformal radiotherapy (3DCRT) were performed using four-dimensional computed tomography (4DCT)-based internal target volumes (ITV). We created retrospective plans for ten patients with lung cancer who underwent SBRT using 3DCRT and VMAT techniques. A Delta4 Phantom + (ScandiDos, Uppsala, Sweden) was used to evaluate the dosimetric robustness of 4DCT-based ITV against variations in respiratory motion during treatment. We analyzed respiratory motion during treatment. Dose-volume histogram parameters were evaluated for the 95% dose (D95%) to the planning target volume (PTV) contoured on CT images obtained under free breathing. The correlations between patient respiratory parameters and dosimetric errors were also evaluated. In the phantom study, the average PTV D95% dose differences for all fractions were - 2.9 ± 4.4% (- 16.0 - 1.2%) and - 2.0 ± 2.8% (- 11.2 - 0.7%) for 3DCRT and VMAT, respectively. The average dose difference was < 3% for both 3DCRT and VMAT; however, in 5 out of 42 fractions in 3DCRT, the difference in PTV D95% was > 10%. Dosimetric errors were correlated with respiratory amplitude and velocity, and differences in respiratory amplitude between 4DCT and treatment days were the main factors causing dosimetric errors. The overall average dose error of the PTV D95% was small; however, both 3DCRT and VMAT cases exceeding 10% error were observed. Larger errors occurred with amplitude variation or baseline drift, indicating limited robustness of 4DCT-based ITV.

EP17.04-07 Lung Stereotactic Body Radiation Therapy in a Developing Country: Patterns of Safety and Treatment Toxicities

EP17.04-07 Lung Stereotactic Body Radiation Therapy in a Developing Country: Patterns of Safety and Treatment Toxicities

Stereotactic body radiation therapy in the treatment of early-stage non-small cell lung cancer.

Stereotactic body radiation therapy (SBRT) is now a standard treatment option for patients with early-stage non-small cell lung cancer (ES-NSCLC) who are unfit for surgery or refuse to undergo an operation. SBRT is a method of external beam radiotherapy that accurately delivers a high dose of irradiation in one or few treatment fractions. Intensive regimens of biologically effective dose ≥ 100 Gy are associated with good local control and overall survival, higher than in conventionally fractionated radiotherapy. There are still controversial areas in the SBRT indication where data are limited - indications for elderly and comorbid patients, indications for treatment without histological verification, treatment of central/ultracentral lesions, indications for tumors larger than 5 cm, indications for operable patients. The optimal follow-up practice of these patients also remains unclear, including the frequency of imaging, the use of PET-CT, and requirements for biopsy. CT changes after SBRT differ from those following conventional radiotherapy and it is difficult to distinguish them from tumor recurrence. Due to the high local control achieved with lung SBRT, data on the treatment of local failure are insufficient. The aim of the publication is to demonstrate the current information and the importance of SBRT for patients with ES-NSCLC.