An open source auto-segmentation algorithm for delineating heart and substructures - Development and validation within a multicenter lung cancer cohort

Investigation of cardiac substructure automatic segmentation methods on synthetically generated 4D cone-beam CT images

BACKGROUND

STereotactic Arrhythmia Radioablation (STAR) is a novel noninvasive method for treating arrythmias in which external beam radiation is directed towards subregions of the heart. Challenges for accurate STAR targeting include small target volumes and relatively large patient motion, which can lead to radiation related patient toxicities. 4D Cone-beam CT (CBCT) images are used for stereotactic lung treatments to account for respiration-related patient motion. 4D-CBCT imaging could similarly be used to account for respiration-related patient motion in STAR; however, the poor contrast of heart tissue in CBCT makes identifying cardiac substructures in 4D-CBCT images challenging. If cardiac structures can be identified in pre-treatment 4D-CBCT images, then the location of the target volume can be more accurately identified for different phases of the respiration cycle, leading to more accurate targeting and a reduction in patient toxicities.

PURPOSE

The aim of this simulation study is to investigate the accuracy of different cardiac substructure segmentation methods for 4D-CBCT images.

METHODS

Repeat 4D-CT scans from 13 lung cancer patients were obtained from The Cancer Imaging Archive. Synthetic 4D-CBCT images for each patient were simulated by forward projecting and reconstructing each respiration phase of a chosen "testing" 4D-CT scan. Eighteen cardiac structures were segmented from each respiration phase image in the testing 4D-CT using the previously validated platipy toolkit. The platipy segmentations from the testing 4D-CT were defined as the ground truth segmentations for the synthetic 4D-CBCT images. Five different 4D-CBCT cardiac segmentation methods were investigated: 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods. For all methods except the Direct CBCT segmentation method, a separate 4D-CT (Planning CT) was used to assist in generating 4D-CBCT segmentations. Segmentation performance was measured using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and volume ratio (VR) metrics.

RESULTS

The mean ± standard deviation DSC for all cardiac substructures for the 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods were 0.48 ± 0.29, 0.52 ± 0.29, 0.37 ± 0.32, 0.53 ± 0.29, 0.57 ± 0.28, respectively. Similarly, the HD values were 10.9 ± 3.6 , 9.9 ± 2.6 , 17.3 ± 5.3 , 9.9 ± 2.8 , 9.3 ± 3.0 mm, the MSD values were 2.9 ± 0.6 , 2.9 ± 0.6 , 6.3 ± 2.5 , 2.5 ± 0.6 , 2.4 ± 0.8 mm, and the VR Values were 0.81 ± 0.12, 0.78 ± 0.14, 1.10 ± 0.47, 0.72 ± 0.15, 0.98 ± 0.44, respectively. Of the five methods investigated the Synthetic CT segmentation method generated the most accurate segmentations for all calculated segmentation metrics.

CONCLUSION

This simulation study investigates the accuracy of different cardiac substructure segmentation methods for 4D-CBCT images. Accurate 4D-CBCT cardiac segmentation will provide more accurate information on the location of cardiac anatomy during STAR treatments which can lead to safer and more effective STAR. As the data and segmentation methods used in this study are all open source, this study provides a useful benchmarking tool to evaluate other CBCT cardiac segmentation methods.

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.

Cancer therapy-related cardiac dysfunction after radiation therapy for breast cancer: results from the BACCARAT cohort study

BACKGROUND

Radiation therapy (RT) for breast cancer (BC) can result in subtle cardiac dysfunction that can occur early after treatment. In 2022, the European Society of Cardiology (ESC) published the first guidelines in cardio-oncology with a harmonized definition of cancer therapy-related cardiac dysfunction (CTRCD). The aim of this study was to evaluate CTRCD occurrence over 24 months of follow-up after RT in BC patients and to analyze the association with cardiac radiation exposure.

METHODS

The prospective monocentric BACCARAT study included BC patients treated with RT without chemotherapy, aged 40-75 years, with conventional and 2D Speckle tracking echocardiography performed before RT, 6 and 24 months after RT. Based on ESC cardio-oncology guidelines, CTRCD and corresponding severity were defined with left ventricle ejection fraction and global longitudinal strain decrease, occurring at 6 or 24 months after RT. Dosimetry for whole heart, left ventricle (LV) and left coronary artery (left anterior descending and circumflex arteries (CX)) was considered to evaluate the association with CTRCD, based on logistic regressions (Odds Ratio - OR and 95% confidence interval - 95%CI). Youden index based on receiver operating characteristic curve analysis was used to identify the optimal threshold of dose-volume parameters for predicting CTRCD.

RESULTS

The study included 72 BC patients with a mean age of 58 ± 8.2 years. A total of 32 (44%) patients developed CTRCD during follow-up: 20 (28%) mild CTRCD, 7 (9%) moderate CTRCD, and 5 (7%) severe CTRCD. Cardiac radiation doses were generally higher among patients with CTRCD rather than non-CTRCD. Dose-response relationships were significant for mean CX dose (OR = 2.48, 95%CI (1.12-5.51), p = 0.02) and marginally significant for V2 of LV (OR = 1.03 95%CI (1.00-1.06), p = 0.05). V2 of LV ≥ 36% and mean CX dose ≥ 1.40 Gy thresholds were determined to be optimal for predicting CTRCD.

CONCLUSION

For BC patients treated with RT without chemotherapy, CTRCD can be observed in an important proportion of the population over 24 months after treatment. Left ventricle and circumflex coronary artery exposure were found to be associated with CTRCD and could be used for the prediction of such cardiotoxicity. Further research remains needed to confirm these results.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier- NCT02605512.

Heart and Lung Dose as Predictors of Overall Survival in Patients With Locally Advanced Lung Cancer. A National Multicenter Study

Introduction

It is an ongoing debate how much lung and heart irradiation impact overall survival (OS) after definitive radiotherapy for lung cancer. This study uses a large national cohort of patients with locally advanced NSCLC to investigate the association between OS and irradiation of lung and heart.

Methods

Treatment plans were acquired from six Danish radiotherapy centers, and patient characteristics were obtained from national registries. A hybrid segmentation tool automatically delineated the heart and substructures. Dose-volume histograms for all structures were extracted and analyzed using principal component analyses (PCAs). Parameter selection for a multivariable Cox model for OS prediction was performed using cross-validation based on bootstrapping.

Results

The population consisted of 644 patients with a median survival of 26 months (95% confidence interval [CI]: 24-29). The cross-validation selected two PCA variables to be included in the multivariable model. PCA1 represented irradiation of the heart and affected OS negatively (hazard ratio, 1.14; 95% CI: 1.04-1.26). PCA2 characterized the left-right balance (right atrium and left ventricle) irradiation, showing better survival for tumors near the right side (hazard ratio, 0.92; 95% CI: 0.84-1.00). Besides the two PCA variables, the multivariable model included age, sex, body-mass index, performance status, tumor dose, and tumor volume.

Conclusions

Besides the classic noncardiac risk factors, lung and heart doses had a negative impact on survival, while it is suggested that the left side of the heart is a more radiation dose-sensitive region. The data indicate that overall heart irradiation should be reduced to improve the OS if possible.