AI-Based Radiation Dose Quantification for Estimation of Heart Disease Risk in Breast Cancer Survivors After Radiation Therapy

Internal Mammary Node Irradiation Debate: Case Closed? Not Yet, and Maybe Never

The IMN debate is still open and may never be closed for reasons outlined in this Comments and Controversies piece.

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.

Radiation dose to heart and cardiac substructures and risk of coronary artery disease in early breast cancer patients: A DBCG study based on modern radiation therapy techniques

BACKGROUND

Coronary artery disease (CAD) has been reported as a late effect following radiation therapy (RT) of early breast cancer (BC). This study aims to report individual RT doses to the heart and cardiac substructures in patients treated with CT-based RT and to investigate if a dose-response relationship between RT dose and CAD exists using modern radiation therapy techniques.

METHODS

Patients registered in the Danish Breast Cancer Group database from 2005 to 2016 with CT-based RT were eligible. Among 15,765 patients, the study included 204 with CAD after irradiation (cases) and 408 matched controls. Individual planning CTs were retrieved, the heart and cardiac substructures were delineated and dose-volume parameters were extracted.

RESULTS

The median follow-up time was 7.3 years (IQR: 4.6-10.0). Among cases, the median mean heart dose was 1.6 Gy (IQR 0.2-6.1) and 0.8 Gy (0.1-2.9) for left-sided and right-sided patients, respectively (p < 0.001). The highest RT doses were observed in the left ventricle and left anterior descending coronary artery for left-sided RT and in the right atrium and the right coronary artery after right-sided RT. The highest left-minus-right dose-difference was located in the distal part of the left anterior descending coronary artery where also the highest left-versus-right ratio of events was observed. However, no significant difference in the distribution of CAD was observed by laterality. Furthermore, no significant differences in the dose-volume parameters were observed for cases versus controls.

CONCLUSIONS

CAD tended to occur in the part of the heart with the highest left-minus- right dose difference, however, no significant risk of CAD was observed at 7 years' median follow-up.

Tissue Reactions and Mechanism in Cardiovascular Diseases Induced by Radiation

The long-term survival rate of cancer patients has been increasing as a result of advances in treatments and precise medical management. The evidence has accumulated that the incidence and mortality of non-cancer diseases have increased along with the increase in survival time and long-term survival rate of cancer patients after radiotherapy. The risk of cardiovascular disease as a radiation late effect of tissue damage reactions is becoming a critical challenge and attracts great concern. Epidemiological research and clinical trials have clearly shown the close association between the development of cardiovascular disease in long-term cancer survivors and radiation exposure. Experimental biological data also strongly supports the above statement. Cardiovascular diseases can occur decades post-irradiation, and from initiation and development to illness, there is a complicated process, including direct and indirect damage of endothelial cells by radiation, acute vasculitis with neutrophil invasion, endothelial dysfunction, altered permeability, tissue reactions, capillary-like network loss, and activation of coagulator mechanisms, fibrosis, and atherosclerosis. We summarize the most recent literature on the tissue reactions and mechanisms that contribute to the development of radiation-induced cardiovascular diseases (RICVD) and provide biological knowledge for building preventative strategies.

Automated coronary artery calcium scoring in patients with breast cancer to assess the risk of heart disease following adjuvant radiation therapy

Aim

Validation of coronary artery calcium (CAC) scores as prognostic factors of acute coronary events (ACE) development in breast cancer patients are demanded. We investigated prognostic impact of CAC on ACE development with cardiac exposure to radiation.

Methods

We evaluated breast cancer patients with (n = 511) or without (n = 600) adjuvant radiotherapy (RT) between 2005 and 2013. CAC Agatston scores were analyzed using a deep-learning-based algorithm. Individual mean heart dose (MHD) was calculated, and no RT was categorized as 0 Gy. The primary endpoint was the development of ACE following breast surgery.

Results

In the RT and no-RT cohorts, 11.2% and 3.7% exhibited CAC >0, respectively. Over a 9.3-year follow-up period, the 10-year ACE rate was 0.7%. In the multivariate analysis, the CAC score was a significant risk factor for ACE (CAC >0 vs CAC = 0, 10-year 6.2% vs 0.2%, P < 0.001). In the subgroup with CAC >0, the 10-year ACE rates were 0%, 3.7%, and 13.7% for patients receiving mean heart doses of 0 Gy, 0–3 Gy, and >3 Gy, respectively (P = 0.133). Although CAC score was not predictive for non-ACE heart disease risk (P > 0.05), the 10-year non-ACE heart disease rates were 1.7%, 5.7%, and 7.1% for patients with CAC = 0 receiving MHD of 0 Gy, 0–3 Gy, and >3 Gy, respectively (P < 0.001).

Conclusions

The CAC score was a significant predictor of ACE in patients with breast cancer. Although further studies are required, CAC score screening on simulation CT in patients undergoing breast RT can help identify those with high risk for ACE on a per-patient basis.

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CAC score was successfully validated as a strong predictive factor for ACEs.

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MHD was identified as a significant factor in development of ACE and NAHD.

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Best efforts should be made to keep the dose to cardiac structures as low as possible.

Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.