UNASSIGNED: Radiotherapy may cause grade ≥3 cardiac events, necessitating a better understanding of risk factors. The potential predictive role of imaging biomarkers with radiotherapy doses for cardiac event occurrence has not been studied.
UNASSIGNED: The aim of this study was to establish the associations between cardiac substructure dose and coronary artery calcium (CAC) scores and cardiac event occurrence.
UNASSIGNED: A retrospective cohort analysis included patients with locally advanced non-small cell lung cancer treated with radiotherapy (2006-2018). Cardiac substructures, including the left anterior descending coronary artery, left main coronary artery, left circumflex coronary artery, right coronary artery, and TotalLeft (left anterior descending, left main, and left circumflex coronary arteries), were contoured. Doses were measured in 2-Gy equivalent units, and visual CAC scoring was compared with automated scoring. Grade ≥3 adverse cardiac events were recorded. Time-dependent receiver-operating characteristic modeling, the log-rank statistic, and competing-risk models were used to measure prediction performance, threshold modeling, and the cumulative incidence of cardiac events, respectively.
UNASSIGNED: Of the 233 eligible patients, 61.4% were men, with a median age of 68.1 years (range: 34.9-90.7 years). The median follow-up period was 73.7 months (range: 1.6-153.9 months). Following radiotherapy, 22.3% experienced cardiac events, within a median time of 21.5 months (range: 1.7-118.9 months). Visual CAC scoring showed significant correlation with automated scoring (r = 0.72; P < 0.001). In a competing-risk multivariable model, TotalLeft volume receiving 15 Gy (per 1 cc; HR: 1.38; 95% CI: 1.11-1.72; P = 0.004) and CAC score >5 (HR: 2.51; 95% CI: 1.08-5.86; P = 0.033) were independently associated with cardiac events. A model incorporating age, TotalLeft CAC (score >5), and volume receiving 15 Gy demonstrated a higher incidence of cardiac events for a high-risk group (28.9%) compared with a low-risk group (6.9%) (P < 0.001).
UNASSIGNED: Adverse cardiac events associated with radiation occur in more than 20% of patients undergoing thoracic radiotherapy within a median time of <2 years. The present findings provide further evidence to support significant associations between TotalLeft radiotherapy dose and cardiac events and define CAC as a predictive risk factor.

UNASSIGNED: There is no consensus on the best photon radiation technique for non-small cell lung cancer (NSCLC). This study quantified the differences between commonly used treatment techniques in NSCLC to find the optimal technique.
UNASSIGNED: Treatment plans were retrospectively generated according to clinical guidelines for 26 stage III NSCLC patients using intensity modulated radiation therapy (IMRT), hybrid, and volumetric modulated arc therapy (VMATC, and VMATV5 optimized for lower lung and heart dose). Plans were evaluated for target coverage, organs at risk dose (including heart substructures) and normal tissue complication probabilities (NTCP).
UNASSIGNED: The comparison showed significant and largest median differences (>1 Gy or >5%) in favor of IMRT for the mediastinal envelope and heart (maximum dose), in favor of the hybrid technique for the lungs (V5Gy of the total lungs and V5Gy of the contralateral lung) and in favor of VMATC for the heart (Dmean), most of the substructures of the heart, and the spinal cord (maximum dose). The VMATV5 technique had significantly lower heart dose compared to the hybrid technique and significantly lower lung dose compared to the VMATC, combining both advantages in one technique. The mean ΔNTCP did not exceed the 2 percent point (pp) for grade 5 (mortality), and 10 pp for grade ≥2 toxicities (radiation pneumonitis and acute esophageal toxicity), but ΔNTCP was mostly in favor of VMATC/V5 for individual patients.
UNASSIGNED: This planning study showed that VMATV5 was preferred as it achieved low lung and heart doses, as well as low NTCPs, simultaneously.

BACKGROUND: Dose to heart substructures is a better predictor for major adverse cardiac events (MACE) than mean heart dose (MHD). We propose an avoidance planning strategy for important cardiac substructures.
METHODS: Two plans, clinical and cardiac substructure-avoidance plan, were generated for twenty patients. Five dose-sensitive substructures, including left ventricle, pulmonary artery, left anterior descending branch, left circumflex branch and the coronary artery were chosen. The avoidance plan aims to meet the target criteria and organ-at-risk (OARs) constraints while minimizing the dose parameters of the above five substructures. The dosimetric assessments included the mean dose and the maximum dose of cardiac substructures and several volume parameters. In addition, we also evaluated the relative risk of coronary artery disease (CAD), chronic heart failure (CHF), and radiation pneumonia (RP).
RESULTS: Pearson correlation coefficient and R2 value of linear regression fitting demonstrated that MHD had poor prediction ability for the mean dose of the cardiac substructures. Compared to clinical plans, an avoidance plan is able to statistically significantly decrease the dose to key substructures. Meanwhile, the dose to OARs and the coverage of the target are comparable in the two plans. In addition, it can be observed that the avoidance plan statistically decreases the relative risks of CAD, CHF, and RP.
CONCLUSIONS: The substructure-avoidance planning strategy that incorporates the cardiac substructures into optimization process, can protect the important heart substructures, such as left ventricle, left anterior descending branch and pulmonary artery, achieving the substantive sparing of dose-sensitive cardiac structures, and have the potential to decrease the relative risks of CAD, CHF, and RP.

Left-sided breast cancer radiotherapy can lead to late cardiovascular complications, including ischemic events. To mitigate these risks, cardiac-sparing techniques such as deep-inspiration breath-hold (DIBH) and intensity-modulated radiotherapy (IMRT) have been developed. However, recent studies have shown that mean heart dose is not a sufficient dosimetric parameter for assessing cardiac exposure. In this study, we aimed to compare the radiation exposure to cardiac substructures for ten patients who underwent hypofractionated radiotherapy using DIBH three-dimensional conformal radiation therapy (3DCRT), free-breathing (FB)-3DCRT, and FB helical tomotherapy (HT). Dosimetric parameters of cardiac substructures were analyzed, and the results were statistically compared using the Wilcoxon signed-rank test. This study found a significant reduction in the dose to the heart, left anterior descending coronary artery, and ventricles with DIBH-3DCRT and FB-HT compared to FB-3DCRT. While DIBH-3DCRT was very effective in sparing the heart, in some cases, it provided little or no cardiac sparing. FB-HT can be an interesting treatment modality to reduce the dose to major coronary vessels and ventricles and may be of interest for patients with cardiovascular risks who do not benefit from or cannot perform DIBH. These findings highlight the importance of cardiac-sparing techniques for precise delivery of radiation therapy.

Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD), and volume ratio. Performance was reliable, with no errors observed and acceptable variation in accuracy between cases, including in challenging cases with imaging artefacts and atypical patient anatomy. The median DSC range was 0.81-0.93 for whole heart and cardiac chambers, 0.43-0.76 for great vessels and conduction nodes, and 0.22-0.53 for heart valves. For all structures the median MDA was below 6 mm, median HD ranged 7.7-19.7 mm, and median volume ratio was close to one (0.95-1.49) for all structures except the left main coronary artery (2.07). The fully automatic algorithm takes between 9 and 23 min per case. The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans. Robust and anatomically consistent segmentations, particularly for smaller structures, represents a major advantage of the proposed segmentation approach. The open-source software will facilitate more precise evaluation of cardiac doses and risks from available clinical datasets.

Radiotherapy (RT) doses to cardiac substructures from the definitive treatment of locally advanced non-small cell lung cancers (NSCLC) have been linked to post-RT cardiac toxicities. With modern treatment delivery techniques, it is possible to focus radiation doses to the planning target volume while reducing cardiac substructure doses. However, it is often challenging to design such treatment plans due to complex tradeoffs involving numerous cardiac substructures. Here, we built a cardiac-substructure-based knowledge-based planning (CS-KBP) model and retrospectively evaluated its performance against a cardiac-based KBP (C-KBP) model and manually optimized patient treatment plans. CS-KBP/C-KBP models were built with 27 previously-treated plans that preferentially spare the heart. While the C-KBP training plans were created with whole heart structures, the CS-KBP model training plans each have 15 cardiac substructures (coronary arteries, valves, great vessels, and chambers of the heart). CS-KBP training plans reflect cardiac-substructure sparing preferences. We evaluated both models on 28 additional patients. Three sets of treatment plans were compared: (1) manually optimized, (2) C-KBP model-generated, and (3) CS-KBP model-generated. Plans were normalized to receive the prescribed dose to at least 95% of the PTV. A two-tailed paired-sample t-test was performed for clinically relevant dose-volume metrics to evaluate the performance of the CS-KBP model against the C-KBP model and clinical plans, respectively. Overall results show significantly improved cardiac substructure sparing by CS-KBP in comparison to C-KBP and the clinical plans. For instance, the average left anterior descending artery volume receiving 15 Gy (V15 Gy) was significantly lower (p < 0.01) for CS-KBP (0.69 ± 1.57 cc) compared to the clinical plans (1.23 ± 1.76 cc) and C-KBP plans (1.05 ± 1.68 cc). In conclusion, the CS-KBP model significantly improved cardiac-substructure sparing without exceeding the tolerances of other OARs or compromising PTV coverage.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: Emerging evidence suggests cardiac substructures are highly radiosensitive during radiation therapy for cancer treatment. However, variability in substructure position after tumor localization has not been well characterized. This study quantifies inter-fraction displacement and planning organ at risk volumes (PRVs) of substructures by leveraging the excellent soft tissue contrast of magnetic resonance imaging (MRI).
UNASSIGNED: Eighteen retrospectively evaluated patients underwent radiotherapy for intrathoracic tumors with a 0.35 T MRI-guided linear accelerator. Imaging was acquired at a 17-25 s breath-hold (resolution 1.5 × 1.5 × 3 mm3). Three to four daily MRIs per patient (n = 71) were rigidly registered to the planning MRI-simulation based on tumor matching. Deep learning or atlas-based segmentation propagated 13 substructures (e.g., chambers, coronary arteries, great vessels) to daily MRIs and were verified by two radiation oncologists. Daily centroid displacements from MRI-simulation were quantified and PRVs were calculated.
UNASSIGNED: Across substructures, inter-fraction displacements for 14% in the left-right, 18% in the anterior-posterior, and 21% of fractions in the superior-inferior were > 5 mm. Due to lack of breath-hold compliance, ~4% of all structures shifted > 10 mm in any axis. For the chambers, median displacements were 1.8, 1.9, and 2.2 mm in the left-right, anterior-posterior, and superior-inferior axis, respectively. Great vessels demonstrated larger displacements (> 3 mm) in the superior-inferior axis (43% of shifts) and were only 25% (left-right) and 29% (anterior-posterior) elsewhere. PRVs from 3 to 5 mm were determined as anisotropic substructure-specific margins.
UNASSIGNED: This exploratory work derived substructure-specific safety margins to ensure highly effective cardiac sparing. Findings require validation in a larger cohort for robust margin derivation and for applications in prospective clinical trials.

Few studies have examined the cardiac volume and radiation dose differences among cardiac phases during radiation therapy (RT). Such information is crucial to dose reconstruction and understanding of RT related cardiac toxicity. In a cohort of nine patients, we studied the changes in the volume and doses of several cardiac substructures between the end-diastolic and end-systolic phases based on the clinical magnetic resonance-guided RT (MRgRT) treatment plans. Significant differences in the volume and dose between the two phases were observed. Onboard cardiac cine MRI holds promise for patient-specific cardiac sparing treatment designs.

OBJECTIVE: 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.