Oral Presentation NSW State Cancer Conference 2023

Understanding cardiac risk in thoracic cancer radiotherapy (#62)

Vicky Chin 1 2 3 , Robert N Finnegan 2 4 5 , Phillip Chlap 1 2 3 , Daniel Al Mouiee 1 2 3 , Lois Holloway 1 2 3 5 , Eric Hau 6 7 8 9 , Anselm Ong 6 , James Otton 1 10 , Geoff P Delaney 1 2 3 11 , Shalini K Vinod 1 2 3 11
  1. University of New South Wales, Sydney, NSW, Australia
  2. Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
  3. Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, NSW, Australia
  4. Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia
  5. Institute of Medical Physics, University of Sydney, Sydney, NSW, Australia
  6. Department of Radiation Oncology, Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead, NSW, Australia
  7. Department of Radiation Oncology, Blacktown Haematology and Cancer Centre, Blacktown Hospital, Blacktown, NSW, Australia
  8. Westmead Clinical School, University of Sydney, Sydney, NSW, Australia
  9. Westmead Institute of Medical Research, Centre for Cancer Research, Westmead, NSW, Australia
  10. Department of Cardiology, Liverpool Hospital, Liverpool, NSW, Australia
  11. Joint senior authors, .

Introduction

Cardiac toxicity is a potential side-effect of thoracic radiotherapy, due to proximity of the heart to radiation tumour targets. However, there is currently no accurate way to quantify an individual’s post-treatment cardiac risk. Existing studies have focused on whole heart radiation dose, though there is increasing evidence that cardiac substructure doses may be more predictive of different detrimental effects1-4. Studying cardiac substructures is challenging, as manually delineating these small structures is labour-intensive, often resulting in small cohort numbers. There is thus a significant gap in our understanding of cardiac radiation dose limits, with no known “safe dose” to the heart.

 

Methods

Our multidisciplinary team set out to develop an automatic segmentation tool that can accurately and consistently delineate the heart and substructures. This tool can be utilised on thoracic radiotherapy planning CTs – including stereotactic ablative lung radiotherapy(SABR), non-SABR lung radiotherapy and breast radiotherapy - to examine radiation doses delivered to heart and substructures.

 

Results

We successfully developed and validated an automatic segmentation tool for 18 heart structures (whole heart, 4 chambers, 3 great vessels, 4 cardiac valves, 4 coronary arteries and 2 conduction nodes)5,6. This was achieved through combining the strengths of 3 different automatic segmentation techniques – deep learning, multi-atlas mapping, and geometric definitions for small hard-to-see structures. When compared with manual “gold standard” heart substructure delineations, the tool had a median accuracy of 2.1-8.6mm (mean-distance-to-agreement) and median variation in radiation dose of 0.9-6.8%. We successfully extended this tool to model uncertainties that occur during radiation planning, such as cardio-respiratory motion.

 

We have deployed this tool on a cohort of 117 SABR patients from an Australian phase 2 multi-centre trial (SSBROC-002). Analysis showed that for central tumours, the maximum dose can be as high as 51.7Gy to the heart. When stratified by the mean heart dose (MHD), Kaplan-Meier analysis suggest detrimental survival for the 50% who received higher than the median MHD (p=0.00004).

 

Conclusion

This novel and time-efficient automatic segmentation tool makes large-scale studies feasible, overcoming prior challenges associated with manual delineation. We demonstrated that it can be deployed on radiotherapy CT imaging, and current work involves analysing larger breast and lung cancer patient datasets. Further work also involves linking heart substructure doses with baseline cardiovascular risks, effects of systemic therapy and cardiac outcomes. This will enable development of an individualised cardiac risk-prediction model, with potential to reduce cardiac morbidity and mortality, leading to improved patient outcomes.

  1. Atkins, K.M., T.L. Chaunzwa, N. Lamba, D.S. Bitterman, B. Rawal, J. Bredfeldt, et al., Association of Left Anterior Descending Coronary Artery Radiation Dose With Major Adverse Cardiac Events and Mortality in Patients With Non-Small Cell Lung Cancer. JAMA Oncol 2021. 7(2):206-19. https://doi.org/10.1001/jamaoncol.2020.6332.
  2. McWilliam, A., J. Khalifa, E. Vasquez Osorio, K. Banfill, A. Abravan, C. Faivre-Finn, et al., Novel Methodology to Investigate the Effect of Radiation Dose to Heart Substructures on Overall Survival. Int J Radiat Oncol Biol Phys 2020. 108(4):1073-81. https://doi.org/ 10.1016/j.ijrobp.2020.06.031.
  3. McWilliam, A., J. Kennedy, C. Hodgson, E. Vasquez Osorio, C. Faivre-Finn, and M. van Herk, Radiation dose to heart base linked with poorer survival in lung cancer patients. Eur J Cancer 2017. 85:106-13. https://doi.org/10.1016/j.ejca.2017.07.053.
  4. van den Bogaard, V.A., B.D. Ta, A. van der Schaaf, A.B. Bouma, A.M. Middag, E.J. Bantema-Joppe, et al., Validation and Modification of a Prediction Model for Acute Cardiac Events in Patients With Breast Cancer Treated With Radiotherapy Based on Three-Dimensional Dose Distributions to Cardiac Substructures. J Clin Oncol 2017. 35(11):1171-8. https://doi.org/ 10.1200/JCO.2016.69.8480.
  5. Finnegan, R.N., V. Chin, P. Chlap, A. Haidar, J. Otton, J. Dowling, et al., Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation. Phys Eng Sci Med 2023. 46(1):377-93. https://doi.org/10.1007/s13246-023-01231-w.
  6. Chin, V., R.N. Finnegan, P. Chlap, J. Otton, A. Haidar, L. Holloway, et al., Validation of a Fully Automated Hybrid Deep Learning Cardiac Substructure Segmentation Tool for Contouring and Dose Evaluation in Lung Cancer Radiotherapy. Clin Oncol (R Coll Radiol) 2023. 35(6):370-81. https://doi.org/10.1016/j.clon.2023.03.005