# Predicting acute coronary events after breast cancer radiotherapy: Integrating baseline cardiovascular risk, systemic therapy, and cardiac radiation dose in the MEDIRAD BRACE study

D.S. Spoor, N.B. Boekel, F.E.V. Leeuwen, N. Russell, Sophie Jacob, S.E. Combs, M. Mayinger, K. Borm, R. Vliegenthart, P. van der Harst, et al.

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## ► To cite this version:

D.S. Spoor, N.B. Boekel, F.E.V. Leeuwen, N. Russell, Sophie Jacob, et al., Predicting acute coronary events after breast cancer radiotherapy: Integrating baseline cardiovascular risk, systemic therapy, and cardiac radiation dose in the MEDIRAD BRACE study. European Journal of Cancer, 2025, 231, pp.116055. 10.1016/j.ejca.2025.116055. hal-05462295

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Original research

# Predicting acute coronary events after breast cancer radiotherapy: Integrating baseline cardiovascular risk, systemic therapy, and cardiac radiation dose in the MEDIRAD BRACE study

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D.S. Spoor<sup>a,1</sup>, N.B. Boekel<sup>b</sup>, F.E.V. Leeuwen<sup>b,1</sup>, N. Russell<sup>b,1</sup>, S. Jacob<sup>c</sup>, S.E. Combs<sup>d</sup>, M. Mayinger<sup>d,1</sup>, K. Borm<sup>d</sup>, R. Vliegenthart<sup>e</sup>, P. van der Harst<sup>f</sup>, G. Sikkema<sup>a</sup>, V.A.B. van den Bogaard<sup>a,1</sup>, N.M. Sijtsema<sup>a</sup>, J.H. Maduro<sup>a</sup>, E. Schuit<sup>g,2</sup>, J.A. Langendijk<sup>a</sup>, A.P.G. Crijns<sup>a,z</sup>

## ARTICLE INFO

### Keywords:

- Breast neoplasms

- Coronary disease

- Radiation therapy

- Radiation injuries

- Risk assessment

## ABSTRACT

**Purpose:** This study aimed to develop prediction models for acute coronary events (ACE) following breast cancer (BC) radiation therapy (RT).

**Patients and Methods:** The cohort included a development set (n = 4305) and an external set (n = 1503) of BC patients treated with postoperative RT (2005–2015). Cardiac dose-volume histogram (DVH) parameters were obtained from three-dimensional CT-based planning. Multivariable cause-specific Cox regression models predicted ACE, accounting for overall mortality risk. Model 1 included seven clinical parameters, whole heart (WH)-Dmean, and left ventricle (LV)-V5. Model 2 replaced WH-Dmean and LV-V5 with cardiac DVH parameters offering higher prognostic value. Model 3 was built by testing whether the better-performing model was enhanced by adding left anterior descending artery DVH parameters, coronary artery calcium (CAC) volumes, or CAC DVH parameters. The incremental value of adding a parameter (or to compare models) was assessed using the likelihood ratio (LR) test. Internal validation was done via bootstrapping. An exploratory assessment of the models' performance in an external population was performed.

**Results:** At ten years, the cumulative incidence of ACE was 4.8 % and 1.4 % in the development and external sets, respectively. Model 2, which included WH-D1, LV-V6, and right ventricle (RV)-V6, did not outperform Model 1 (LR p = 0.422). Consequently, further model improvement was pursued by expanding Model 1 rather than Model 2. The resulting Model 3 incorporated the parameters from Model 1 along with the total CAC volume, significantly enhancing predictive performance (LR p < 0.001). At 10 years post-RT, each model had c-indexes above 0.73 at internal validation, with Model 3 performing best (c-index 0.78; 95 % CI, 0.72–0.83)

**Conclusion:** A model including seven clinical parameters, WH-Dmean, LV-V5, and total CAC volume best predicted ACE risk and may guide RT dose optimisation strategies.

<footnote><sup>a</sup> University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, Netherlands</footnote>

<footnote><sup>b</sup> The Netherlands Cancer Institute, Amsterdam, Netherlands</footnote>

<footnote><sup>c</sup> Institute for Radiation Protection and Nuclear Safety, Fontenay Aux Roses, France</footnote>

<footnote><sup>d</sup> Department of Radiation Oncology, TUM School of Medicine and Health, TUM University Hospital, Klinikum rechts der Isar, Munich, Germany</footnote>

<footnote><sup>e</sup> University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, Netherlands</footnote>

<footnote><sup>f</sup> University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, Netherlands</footnote>

<footnote><sup>g</sup> Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands</footnote>

<footnote>* Correspondence to: Department of Radiation Oncology, University Medical Center Groningen, P.O. Box 30001, Groningen 9700 RB, the Netherlands.
E-mail address: a.p.g.crijns@umcg.nl (A.P.G. Crijns).</footnote>

<footnote n="1">¹ During the period of study execution</footnote>

<footnote n="2">² Responsible for statistical analyses</footnote>
# 1. Introduction

Approximately 85 % of breast cancer (BC) patients have an indication for radiation therapy (RT) to improve locoregional control and survival, making it a cornerstone of treatment [1–5]. Despite advances in RT techniques, regional nodal irradiation – particularly involving the internal mammary nodes - or unfavourable anatomy can still lead to considerable cardiac exposure in individual patients [6,7]. For acute coronary events (ACE), no safe radiation dose threshold has been established, below which there is no excess risk of late radiation-induced cardiac diseases [8,9]. Moreover, larger irradiated heart volumes may worsen ACE prognosis [10,11]. With rising BC survival due to improved diagnostics and treatments, more survivors face late radiation toxicity risks [4], underscoring the need for preventive measures, including heart dose optimisation. Normal Tissue Complication Probability (NTCP) models can predict ACE risk, guiding dose reduction strategies by providing targets for dose optimisation and identifying patients who benefit from proton therapy [12].

The link between dose distributions in cardiac substructures and complications remains poorly understood [8,9,13–15]. Darby et al. reported a 7.4 % ACE risk increase per Gy of mean whole-heart dose (WH-Dmean) [8]. Van den Bogaard et al. [9], confirmed WH-Dmean's role in ACE risk using three-dimensional dose distributions and suggested LV-V5 (left ventricle volume receiving ≥5 Gy) as a stronger predictor. A subsequent study linked radiation to pre-existing calcified plaques in the left anterior descending artery (LAD) with ACE risk after BC RT [16], but further validation in larger, independent datasets remain essential. Previous analyses also lacked key ACE risk factors, such as atherosclerosis severity and systemic treatments such as chemotherapy and hormonal therapy, to prevent overfitting [17–20].

This study aimed to develop NTCP models for ACE after breast cancer radiation therapy by integrating baseline cardiovascular risk, systemic therapies, and radiation dose to cardiac structures. To assess generalisability, we conducted an exploratory assessment of the model's performance in a multicentre external dataset.

# 2. Materials and methods

This research is part of the MEDIRAD-BRACE project, supported by the MEDIRAD consortium (H2020-Euratom-1.4 / 755523, NCT03211442), which aims to enhance radiation protection in the medical field. This study was conducted following the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines [21]. A brief overview of the materials and methods is provided here, with further details available in the Supplementary Materials.

## 2.1. Study population

We included females with ductal carcinoma in situ (DCIS) or stage I-III breast cancer (BC) who received RT between 2005 and 2015 (see Suppl. Figure 1). The development set consisted of 4305 patients from the University Medical Center Groningen (UMCG), Netherlands. Using bootstrapping, 200 samples of the same size as this development set were drawn with replacement to create an internal validation dataset. An independent, multicentre external dataset of 1503 patients was sourced from the Netherlands Cancer Institute - Antoni van Leeuwenhoek (NKI), Institute de Radioprotection et de Sûreté Nucléaire (IRSN), and Klinikum rechts der Isar der Technischen Universität München (TUM).

## 2.2. Data collection

Patient and treatment data, including cardiovascular (CV) risk factors and diseases, were collected from hospital records and general practitioners (GPs). Ethical approval was obtained for each participating

centre ((UMCG: METc 2017.231, NKI: IRBd18-056, IRSN: CNIL 2103119.v0, TUM: 388/17 S). The primary endpoint was acute coronary events (ACE), including myocardial infarction, coronary revascularisation, and ischaemic heart disease mortality [22]. The follow-up period lasted until the most recent reliable follow-up date, with subjects censored upon event occurrence.

For RT planning, three-dimensional conformal RT or intensity-modulated RT was used. Dose-volume histogram (DVH) parameters for the WH, left atrium (LA), right atrium (RA), LV, RV and LAD were derived from planned 3D dose distributions [13,23]. Coronary artery calcification (CAC) was assessed via a semi-automated segmentation method on planning CT scans.

## 2.3. Statistical analysis

A previous study at UMCG, involving 910 breast cancer (BC) patients, indicated an ACE rate of 3.3 % over the first 9 years post-RT. The initial sample size calculation suggested that 5000 subjects were needed for model development and 2000 subjects for external validation (α = 0.05; power = 0.80). Patient selection was based on this initial estimate. However, during the execution phase, updated methodological guidance became available, leading to a revised sample size requirement of 3237 for model development and approximately 10,000 for a robust external validation. Although it was no longer feasible to include additional subjects retrospectively, we used this revised calculation to properly interpret and evaluate the study results [24,25]. Missing data were handled using multiple imputation by chained equations (10 times). Results were combined following Rubin's rules to account for uncertainty in the imputations [26].

The cumulative incidence of ACE was analysed using the Kaplan-Meier method, with overall mortality considered a competing risk. Baseline characteristics were compared between the development and validation sets using independent t-tests for continuous variables and Chi-squared tests for categorical variables. The relationship between continuous variables and ACE was assessed using restricted cubic splines, and a log transformation was applied to total coronary artery calcium (CAC) volume.

We used cause-specific Cox regression, assuming stable relationships between covariates and the endpoint over time, to assess the impact of various factors on ACE occurrence and overall mortality, with a focus on developing ACE prediction models. To address multicollinearity, sub-models were created for highly correlated DVH parameters, averaging their regression coefficients. Although the models were developed using the complete follow-up period, model performance was evaluated at 10 years post-RT. Three prediction models were developed:

* Model 1: Based on well-established baseline CV risk factors and cardiac DVH parameters (i.e. WH-Dmean and LV-V5).

* Model 2: Improved upon Model 1 by replacing WH-Dmean and LV-V5 with most predictive cardiac DVH parameters.

* Model 3: Enhanced the better performing model after comparison of Model 1 and Model 2 by incorporating LAD DVH parameters, CAC volumes, and CAC DVH parameters for patients without coronary revascularisation history.

Parameters with potential prognostic value for ACE were preselected by evaluating absolute and relative differences between patients with and without an ACE during follow-up. The incremental value of adding a parameter (or to compare models) was assessed using the likelihood ratio (LR) test, prediction variance, and integrated discrimination index (IDI). Model performance was evaluated through internal validation for optimism-adjusted measures. Due to limited statistical power, robust validation of the model in the independent multicentre external dataset was not feasible. Nevertheless, we cautiously explored its performance in this dataset by applying the models to estimate ACE and mortality risk in the validation cohort to gain preliminary insights. Discrimination and
calibration were assessed using AUC (c-index), observed-expected ratio, and calibration slope/intercept. Calibration plots demonstrated agreement between predicted and observed event risks in external validation, though internal validation plots were not presented due to variations in bootstrap samples and imputation sets.

R software (version 4.2.2) was used for all analyses [27].

# 3. Results

## 3.1. Data description

Baseline characteristics are summarised in Table 1, with institute-specific details in Suppl. Table 1. All noted differences were significant (p < 0.01). Median age was higher in the development set (59 years, IQR: 50–67) than in the validation set (54 years, IQR: 48–60).

Development set patients had more advanced BC (more stages T2–4 and regional lymph node involvement). Except for hypercholesterolaemia (p = 0.414), all CV risk factors were worse in the development set. Chemotherapy and hormonal therapy were more common in the development set. Mean doses to all cardiac structures, including LAD, were higher in the development set. Specifically, median WH-Dmean was 1.78 Gy (IQR: 1.17–3.59 Gy) in the development set vs. 1.11 Gy (IQR: 0.74–1.70 Gy) in the validation set. CAC was more prevalent in the development set (22.9 %) than in the validation set (17.2 %). Not only were CAC volumes higher across all coronary arteries, but radiation doses were also higher. Overall, these findings demonstrate substantial differences in baseline characteristics between the development and multicentre external cohorts.

Median follow-up was 6.2 years (IQR: 4.4–9.1) in the development set and 7.6 years (IQR: 6.7–9.0) in the validation set. During follow-up,

<table><caption>**Table 1**

Patient and treatment characteristics at baseline and follow-up.</caption><thead><tr><th>Category</th><th>Variable</th><th>Development set (n = 4305)</th><th>External set (n = 1503)</th><th>p-value*</th></tr></thead><tbody><tr><td rowspan="2">Baseline</td><td>Overall, years</td><td>59 [50–67]</td><td>54 [48–60]</td><td rowspan="2">&lt; 0.001</td></tr><tr><td>18–35 years</td><td>87 (2.0 %)</td><td>35 (2.3 %)</td></tr><tr><td rowspan="7">Age</td><td>35–45 years</td><td>473 (11.0 %)</td><td>205 (13.6 %)</td><td rowspan="7">&lt; 0.001</td></tr><tr><td>46–55 years</td><td>1187 (27.6 %)</td><td>618 (41.1 %)</td></tr><tr><td>56–65 years</td><td>1277 (29.7 %)</td><td>437 (29.1 %)</td></tr><tr><td>66–75 years</td><td>1031 (23.9 %)</td><td>175 (11.7 %)</td></tr><tr><td>&gt; 75 years</td><td>250 (5.8 %)</td><td>32 (2.1 %)</td></tr><tr><td>Tis</td><td>440 (10.2 %)</td><td>296 (19.7 %)</td></tr><tr><td>T1</td><td>2564 (59.6 %)</td><td>957 (63.7 %)</td></tr><tr><td rowspan="4">Tumour characteristics</td><td>T-stage, n</td><td>T2</td><td>1060 (24.6 %)</td><td>230 (15.3 %)</td><td rowspan="4">&lt; 0.001</td></tr><tr><td></td><td>T3</td><td>149 (3.5 %)</td><td>18 (1.2 %)</td></tr><tr><td></td><td>T4</td><td>92 (2.1 %)</td><td>3 (0.2 %)</td></tr><tr><td></td><td>N0</td><td>2822 (65.6 %)</td><td>1148 (76.4 %)</td></tr><tr><td rowspan="4">Cardiovascular risk factors</td><td>N1</td><td>945 (22.0 %)</td><td>289 (19.2 %)</td><td rowspan="4">&lt; 0.001</td></tr><tr><td>N2</td><td>337 (7.8 %)</td><td>56 (3.7 %)</td></tr><tr><td>N3</td><td>201 (4.7 %)</td><td>10 (0.7 %)</td></tr><tr><td>Active or former smoker</td><td>2456 (57.0 %)</td><td>307 (20.4 %)</td><td>&lt; 0.001</td></tr><tr><td></td><td>History of ischaemic heart disease</td><td>191 (4.4 %)</td><td>19 (1.3 %)</td><td>&lt; 0.001</td></tr><tr><td></td><td>Diabetes Mellitus</td><td>408 (9.5 %)</td><td>76 (5.0 %)</td><td>&lt; 0.001</td></tr><tr><td></td><td>Hypercholesterolaemia</td><td>531 (12.3 %)</td><td>196 (13.1 %)</td><td>0.414</td></tr><tr><td></td><td>Hypertension</td><td>1280 (29.7 %)</td><td>303 (20.2 %)</td><td>&lt; 0.001</td></tr><tr><td></td><td>Body Mass Index, kg/m²</td><td>26.4 [23.5 – 30.1]</td><td>-</td><td>-</td></tr><tr><td></td><td>Chemotherapy</td><td>1877 (43.6 %)</td><td>564 (37.5 %)</td><td>&lt; 0.001</td></tr><tr><td rowspan="4">Adjuvant systemic treatment</td><td>Immunotherapy</td><td>418 (9.7 %)</td><td>-</td><td>-</td></tr><tr><td>Hormonal therapy</td><td>2252 (52.3 %)</td><td>694 (46.2 %)</td><td>&lt; 0.001</td></tr><tr><td>Whole Heart - Dmean, Gy</td><td>1.78 [1.17–3.59]</td><td>1.11 [0.74–1.70]</td><td>&lt; 0.001</td></tr><tr><td>Left Atrium - Dmean, Gy</td><td>0.97 [0.71–1.40]</td><td>0.61 [0.46–0.78]</td><td>&lt; 0.001</td></tr><tr><td rowspan="4">Radiation dose to cardiac structures</td><td>Right Atrium - Dmean, Gy</td><td>1.41 [0.93–2.09]</td><td>0.76 [0.5–1.28]</td><td>&lt; 0.001</td></tr><tr><td>Left Ventricle - Dmean, Gy</td><td>1.6 [0.58–5.20]</td><td>1.27 [0.34–2.20]</td><td>&lt; 0.001</td></tr><tr><td>Right Ventricle - Dmean, Gy</td><td>1.94 [1.42–2.91]</td><td>1.22 [0.87–1.62]</td><td>&lt; 0.001</td></tr><tr><td>LAD - Dmean, Gy**</td><td>3.60 [0.96–29.34]</td><td>3.04 [0.58–5.70]</td><td>&lt; 0.001</td></tr><tr><td rowspan="4">Coronary artery calcium**</td><td>Total</td><td>Frequency</td><td>962 (22.9 %)</td><td>257 (17.2 %)</td><td>&lt; 0.001</td></tr><tr><td></td><td>Volume, mm³***</td><td>71 [26–206]</td><td>62 [24–153]</td><td>0.105</td></tr><tr><td></td><td>Dmean, Gy***</td><td>1.64 [1.06–2.53]</td><td>1.00 [0.54–1.55]</td><td>&lt; 0.001</td></tr><tr><td></td><td>Frequency</td><td>167 (4.0 %)</td><td>28 (1.9 %)</td><td>&lt; 0.001</td></tr><tr><td rowspan="4">LCA</td><td></td><td>Volume, mm³***</td><td>38 [19–71]</td><td>45 [19–82]</td><td>0.555</td></tr><tr><td></td><td>Dmean, Gy***</td><td>1.37 [0.99–2.03]</td><td>0.85 [0.54–1.20]</td><td>0.002</td></tr><tr><td></td><td>Frequency</td><td>827 (19.6 %)</td><td>221 (14.8 %)</td><td>&lt; 0.001</td></tr><tr><td></td><td>Volume, mm³***</td><td>63 [23–146]</td><td>50 [23–120]</td><td>0.004</td></tr><tr><td rowspan="4">Cx</td><td>LAD</td><td>Frequency</td><td>371 (8.8 %)</td><td>65 (4.4 %)</td><td>&lt; 0.001</td></tr><tr><td></td><td>Volume, mm³***</td><td>53 [22–132]</td><td>62 [35–107]</td><td>0.593</td></tr><tr><td></td><td>Dmean, Gy***</td><td>1.34 [0.75–2.01]</td><td>0.85 [0.41–1.30]</td><td>&lt; 0.001</td></tr><tr><td></td><td>Frequency</td><td>359 (8.5 %)</td><td>97 (6.5 %)</td><td>0.015</td></tr><tr><td rowspan="2">RCA</td><td></td><td>Volume, mm³***</td><td>57 [20–152]</td><td>43 [16–110]</td><td>0.353</td></tr><tr><td></td><td>Dmean, Gy***</td><td>1.66 [1.27–2.19]</td><td>0.97 [0.71–1.46]</td><td>&lt; 0.001</td></tr><tr><td>Follow-up</td><td>Interval, years</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Acute coronary events</td><td>6.2 [4.4–9.1]</td><td>7.6 [6.7–9.0]</td><td>&lt; 0.001</td></tr><tr><td></td><td>Deaths</td><td>141 (3.3 %)</td><td>17 (1.1 %)</td><td>&lt; 0.001</td></tr><tr><td></td><td></td><td>809 (18.8 %)</td><td>97 (6.5 %)</td><td>&lt; 0.001</td></tr></tbody></table>

Abbreviations: LCA: left coronary artery, LAD: left anterior descending artery, Cx: circumflex artery, RCA: right coronary artery.

* P-values between both groups were estimated by two independent samples t-tests for continuous outcomes and Chi-squared tests for categorical outcomes.

**LAD dose, and CAC volumes and doses only provided for dataset that was used for Model 3 (i.e., subjects with a history of coronary revascularisation were excluded).

*** CAC volume and dose only provided for patients with CAC in that particular coronary artery
141 development set patients (3.3%) developed an ACE, and 809 (18.8%) died, compared to 17 (1.1%) and 97 (6.5%) in the validation set. The 10-year post-RT cumulative incidence of ACE was 4.8% in the development set and 1.4% in the external dataset (Suppl. Figure 2). Suppl. Table 2 details differences between development set patients with and without ACE, while Suppl. Figure 3 shows their mean radiation dose distributions per cardiac structure. Suppl. Figure 4 shows ACE incidence increased with larger CAC volumes.

## 3.2. Model development

Model 1 included WH-Dmean, LV-V5, and seven clinical parameters (i.e. age, ischaemic heart disease, hypertension, hypercholesterolaemia, diabetes, smoking, and hormonal therapy). To address multicollinearity, submodels were fitted for WH-Dmean and LV-V5. All predictors had hazard ratios (HR) > 1, except hormonal therapy (HR: 0.78). ACE risk increased by 6.7% per Gy WH-Dmean and 0.9% per percent LV-V5.

Suppl. Figure 5 identified cardiac DVH parameters with prognostic value for ACE. In addition to WH-Dmean and LV-V5, four parameters for WH, LV, and RV were preselected (Suppl. Figure 6). No LA or RA parameters met selection criteria. Incremental value analysis (Table 2) identified WH-D1, LV-V6, and RV-D1 as superior predictors, leading to their inclusion in Model 2 (D1: dose to the most irradiated 1% of volume; V6: volume receiving ≥ 6 Gy). ACE risk increased by 1.1% per Gy WH-D1, 1.1% per percent LV-V6, and 1.5% per Gy RV-D1.

Suppl. Figures 7–8 explored ACE predictors among LAD DVH parameters, CAC volumes, and CAC DVH parameters, including total CAC volume and volumes by coronary artery (excluding the left coronary artery). Preselected parameters also included total CAC Dmean, LAD CAC Dmean/Dmax, and LAD-Dmean/D1 (Suppl. Figure 9). Since Model 2 did not outperform Model 1, we focused on improving Model 1 through an incremental value assessment. Only total CAC volume significantly enhanced Model 1 (Table 2), leading to its inclusion in Model 3.

Univariable and multivariable associations for ACE, along with 10-year risk estimation parameters, are shown in Table 3. Suppl. Figure 10 compares parameter distributions in ACE vs. non-ACE subjects. Suppl. Table 3 details associations and model parameters for overall mortality prediction.

### 3.2.1. Preliminary evaluation of generalisability

Fig. 1 presents performance metrics at interval validation for ACE prediction. At 10 years post-RT, each model had c-indexes above 0.73, with Model 3 performing best (c-index 0.78; 95% CI, 0.72–0.83). Suppl. Figure 11 shows how the models performed in the external dataset. The observed-expected ratio was ~0.5 for all models in the external dataset, indicating ACE risk overestimation. Calibration plots (Suppl. Figure 12) confirmed this overestimation with minimal differences between models, which was also reflected numerically by calibration intercepts < 0 and slopes < 1. Performance metrics for overall mortality and external dataset calibration plots are in Suppl. Figures 13–15.

## 3.3. Model comparison

Model 2 did not significantly improve Model 1 (LR, p = 0.422), and both produced similar ACE risk predictions (Fig. 2a). However, Model 3 significantly outperformed Model 1 (LR, p < 0.001). Adding total CAC volume increased the proportion of new information by 33.2% and improved event discrimination (IDI, p < 0.001). This was reflected in substantial variation in predicted ACE risks (Fig. 2b), enhancing differentiation between low- and high-risk patients.

## 4. Discussion

We developed three NTCP models to predict ACE following BC RT by integrating baseline cardiovascular risk factors, systemic therapies, and radiation dose to cardiac structures. Model 1 included seven clinical parameters, WH-Dmean, and LV-V5. Model 2 incorporated WH-D1, LV-

<caption>**Table 2**</caption>

Incremental value of adding candidate predictors to a base model. The base model for Model 2 excludes cardiac DVH parameters (WH-Dmean and LV-V5) from Model 1 and for Model 3 it included all Model 1 parameters (since it outperformed Model 2) but excludes patients with prior coronary revascularisation.

<table><thead><tr><th>Parameters</th><th></th><th>LR test (p-value)</th><th>Fraction of new information (variance)*</th><th>IDI (p-value)*</th></tr></thead><tbody><tr><td rowspan="6">Whole Heart</td><td>Dmean</td><td>0.086</td><td>-</td><td>-</td></tr><tr><td>D1</td><td>0.016</td><td>0.005</td><td>0.247</td></tr><tr><td>D2</td><td>0.027</td><td>0.005</td><td>0.219</td></tr><tr><td>V4</td><td>0.174</td><td>-</td><td>-</td></tr><tr><td>V7</td><td>0.092</td><td>-</td><td>-</td></tr><tr><td>D1</td><td>0.030</td><td>-0.001</td><td>0.347</td></tr><tr><td rowspan="6">Left Ventricle</td><td>V2</td><td>0.148</td><td>-</td><td>-</td></tr><tr><td>V3</td><td>0.076</td><td>-</td><td>-</td></tr><tr><td>V5</td><td>0.022</td><td>-0.003</td><td>0.262</td></tr><tr><td>V6</td><td>0.020</td><td>0.000</td><td>0.233</td></tr><tr><td>D1</td><td>0.034</td><td>0.004</td><td>0.186</td></tr><tr><td>V3</td><td>0.186</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Right Ventricle</td><td>V4</td><td>0.325</td><td>-</td><td>-</td></tr><tr><td>V6</td><td>0.433</td><td>-</td><td>-</td></tr><tr><td colspan="2">Total</td><td>&lt; 0.001</td><td>0.465</td><td>&lt; 0.001</td></tr><tr><td rowspan="5">CAC volume**</td><td>LCA</td><td>0.002</td><td>0.121</td><td>0.127</td></tr><tr><td>LAD</td><td>&lt; 0.001</td><td>0.396</td><td>&lt; 0.001</td></tr><tr><td>Cx</td><td>&lt; 0.001</td><td>0.250</td><td>0.002</td></tr><tr><td>RCA</td><td>&lt; 0.001</td><td>0.398</td><td>&lt; 0.001</td></tr><tr><td>Dmean</td><td>0.862</td><td>-</td><td>-</td></tr><tr><td rowspan="3">CAC dose</td><td>Total</td><td>Dmean</td><td>0.416</td><td>-</td><td>-</td></tr><tr><td>LAD</td><td>Dmean</td><td>0.052</td><td>-</td><td>-</td></tr><tr><td>LAD</td><td>Dmax</td><td>0.118</td><td>-</td><td>-</td></tr><tr><td>LAD</td><td>D1</td><td>0.156</td><td>-</td><td>-</td></tr></tbody></table>

Abbreviations: LR: likelihood ratio, IDI: integrated discrimination index, CAC: coronary artery calcium, LCA: left coronary artery, LAD: left anterior descending artery, Cx: circumflex artery, RCA: right coronary artery.

* Further assessment was conducted only if the likelihood ratio test yielded p < 0.05, i.e., statistically significant improvement in model fit after addition of the DVH-parameter to the base model.

** CAC volumes were log transformed in order to get a linear association with acute coronary events.
<caption>**Table 3**</caption>

Univariable and multivariable associations with acute coronary events after breast cancer radiotherapy, including model parameters for calculating individual patient risk.

<table><thead><tr><th rowspan="3">Candidate predictors</th><th colspan="2">Univariable</th><th colspan="6">Multivariable</th></tr><tr><th rowspan="2">HR (95 % CI)</th><th colspan="2">Model 1</th><th colspan="2">Model 2</th><th colspan="2">Model 3</th></tr><tr><th>β</th><th>HR</th><th>β</th><th>HR</th><th>β</th><th>HR</th></tr></thead><tbody><tr><td colspan="8"><strong>Clinical parameters</strong></td></tr><tr><td>Ischaemic heart disease</td><td>10.1 (6.93–14.72)</td><td>1.59</td><td>4.900</td><td>1.59</td><td>4.917</td><td>0.96</td><td>2.607</td></tr><tr><td>Hypercholesterolaemia</td><td>4.06 (2.86–5.76)</td><td>0.54</td><td>1.724</td><td>0.54</td><td>1.721</td><td>0.34</td><td>1.398</td></tr><tr><td>Hypertension</td><td>3.85 (2.75–5.38)</td><td>0.69</td><td>1.992</td><td>0.69</td><td>1.990</td><td>0.51</td><td>1.670</td></tr><tr><td>Diabetes Mellitus</td><td>3.63 (2.47–5.35)</td><td>0.41</td><td>1.506</td><td>0.41</td><td>1.512</td><td>0.44</td><td>1.545</td></tr><tr><td>Active or Former Smoker</td><td>1.12 (0.78–1.62)</td><td>0.30</td><td>1.355</td><td>0.30</td><td>1.354</td><td>0.08</td><td>1.083</td></tr><tr><td>Age, years</td><td>1.06 (1.05–1.08)</td><td>0.04</td><td>1.041</td><td>0.04</td><td>1.042</td><td>0.02</td><td>1.017</td></tr><tr><td>Hormonal therapy</td><td>0.79 (0.57–1.11)</td><td>-0.25</td><td>0.778</td><td>-0.25</td><td>0.780</td><td>-0.21</td><td>0.813</td></tr><tr><td>Chemotherapy</td><td>0.60 (0.42–0.86)</td><td>0.27</td><td>1.310</td><td>0.27</td><td>1.310</td><td>0.28</td><td>1.321</td></tr><tr><td>Total CAC volume (mm³)*</td><td>1.55 (1.45–1.66)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.34</td><td>1.405</td></tr><tr><td colspan="8"><strong>DVH parameters**</strong></td></tr><tr><td>Whole Heart - Dmean, Gy</td><td>1.059 (0.99–1.133)</td><td>0.06</td><td>1.067</td><td>-</td><td>-</td><td>0.05</td><td>1.055</td></tr><tr><td>Left Ventricle - V5Gy, %</td><td>1.01 (1–1.02)</td><td>0.01</td><td>1.009</td><td>-</td><td>-</td><td>0.01</td><td>1.007</td></tr><tr><td>Whole Heart - D1, Gy</td><td>1.01 (1–1.02)</td><td>-</td><td>-</td><td>0.01</td><td>1.011</td><td>-</td><td>-</td></tr><tr><td>Left Ventricle - V6Gy, %</td><td>1.01 (1–1.02)</td><td>-</td><td>-</td><td>0.01</td><td>1.011</td><td>-</td><td>-</td></tr><tr><td>Right Ventricle - D1, Gy</td><td>1.01 (1–1.02)</td><td>-</td><td>-</td><td>0.02</td><td>1.015</td><td>-</td><td>-</td></tr><tr><th colspan="2">Model parameters</th><th>Model 1</th><th colspan="2">Model 2</th><th colspan="2">Model 3</th></tr><tr><td colspan="2">Average linear predictor</td><td>3.19</td><td colspan="2">3.38</td><td colspan="2">0.27</td></tr><tr><td colspan="2">Baseline survival at 10 years after RT</td><td>0.957</td><td colspan="2">0.957</td><td colspan="2">0.963</td></tr></tbody></table>

Individual risk of ACE x-years post-RT can be estimated by:

$$p_{x \text{ yr}} = 1 - \left( \text{baseline survival}_{x \text{ yr}} * \frac{\text{individual linear predictor} - \text{average linear predictor}}{\text{individual linear predictor}} \right)$$

$$\text{individual linear predictor} = \sum \text{parameter} * \beta$$

Abbreviations: HR: Hazard Ratio, DVH: dose-volume histogram.

* CAC volumes were log transformed to get a linear association with acute coronary events.

** To address multicollinearity, submodels were created for DVH parameters with correlation coefficients above 0.7 and then combined into a composite model by averaging their regression coefficients.

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Performance metrics of prediction models for acute coronary events 10 years post-radiotherapy, assessed via internal validation.</figcaption></figure>

V6, and RV-D6 but did not outperform Model 1. Model 3, which added total CAC volume to Model 1, performed best. At 10 years post-RT, all models demonstrated an AUC above 0.73 at internal validation, with Model 3 performing best (c-index 0.78; 95 % CI, 0.72–0.83). External

validation was attempted but unsuccessful due to limited sample size and low event count.

The models addressed the competing risk of overall mortality, leading to more accurate ACE estimates. While models predicting
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Scatterplots comparing predicted acute coronary event risks between models. Plot A shows how Model 2's predictions compare to those of Model 1, while Plot B compares Model 3 to Model 1. Each dot represents a patient's predicted risk. The diagonal line represents perfect agreement with Model 1, and the red lines indicate the 0.1 and 0.9 quantiles of Model 2 (A) and Model 3 (B) predictions, showing the spread of predicted risks for patients with similar risks in Model 1. Wider spreads suggest greater divergence in risk prediction between models.</figcaption></figure>

overall mortality were briefly described, the focus was on ACE predic-
tion models. All models included well-established coronary artery dis-
ease risk factors [28,29], except hormonal therapy, which showed a
protective effect possibly due to tamoxifen's favourable impact on lipid
metabolism [30].

Cardiac dosimetry was crucial in predicting ACE, with WH-Dmean and LV-V5 remaining reliable predictors. Model 2's detailed cardiac dose-volume parameters did not improve ACE prediction. Due to WH-Dmean's established status and ease of interpretation, we proceeded with Model 1 for developing Model 3. Adding CAC volume to Model 3 reduced the coefficients for other risk factors but improved prediction accuracy. CAC volume can be automatically quantified from planning CT scans, enabling fast and low-cost risk assessment without extra aging or radiation [31]. Adding CAC volume to Model 3 enhanced prediction accuracy, highlighting its value alongside traditional risk factors and dosimetric parameters [32]. Notably, another study found that one in three BC patients who had a high CAC score lacked other CVD risk factors [33]. While hazard ratios for WH-Dmean and LV-V5 slightly declined, both remained independently associated with ACE. Consistent with this, Refsgaard et al. [34] showed that CAC from planning CT scans predicted ACE risk and overall survival, with WH-Dmean being the strongest predictor in patients with low CAC, and CAC itself dominating risk in those with high scores.

All models overestimated ACE risk twofold in the external dataset. However, these results should be interpreted with caution due to insufficient statistical power. ACE is a relatively rare event, requiring large cohorts for meaningful validation—a need already acknowledged through a revised sample size calculation after patient selection. Additionally, the external cohort differed substantially from the development cohort, which had a more adverse cardiovascular risk profile (i.e. older age, worse CV risk factors, more advanced BC, higher cardiac doses, and more frequent adjuvant therapy). Many patients with electron boost or internal mammary node involvement were excluded from the external dataset due to technical limitations in dose reconstruction, despite their higher cardiac exposure and ACE risk. Unmeasured factors, such as environmental or socio-economic differences, may have further contributed. These imbalances reduced event rates and limited comparability. Nonetheless, the analysis was retained for transparency and to support future validation in larger, more representative datasets.

Despite the absence of robust external validation, the models' strong

internal validation performance indicates potential for clinical use. They
are relevant for providing targets for dose optimisation and identify
patients who may benefit from proton therapy (via model-based selec-
tion procedure [12]) or targeted cardiovascular risk management
post-treatment. With rising BC survival rates, maintaining long-term
cardiac health is crucial.

Our models build on prior research, refining predictive parameters [8]. The weaker association we found between cardiac dose and ACE likely reflects adjustment for multiple risk factors, yielding more reliable dose-effect estimates. Multiple studies have challenged the clinical significance of the association between incidental cardiac irradiation and subsequent cardiac events after BC RT. For example, the Early Breast Cancer Trialists' Collaborative Group demonstrated that, since 1980, regional RT has not increased non-breast cancer-specific mortality [35]. Poortmans et al. [36] reported a modest 1.7% increase in cardiac events 15 years after irradiation of the internal mammary and medial supra-clavicular lymph nodes. While no difference was reported during the first decade, their results may suggest a slight increase in risk over time. Nevertheless, as individual patients may still receive substantial cardiac exposure, the associations identified in our study remain clinically relevant. Furthermore, Struikmans et al. [37] questioned the linearity of the WH-Dmean-ACE relationship, suggesting LV and coronary artery doses may be more predictive. Notably, our study showed that the combination of WH-Dmean and LV-V5 achieved the highest predictive performance. In addition, both WH-Dmean and LV-V5 showed linear associations with ACE, even after adjustment for cardiovascular risk factors, including baseline coronary calcification. Previous studies also emphasised the significance of LAD CAC dose [38]. However, our findings revealed that, unlike total CAC volume, CAC DVH parameters did not improve ACE prediction models. The main limitation of our study is the lack of robust external validation, primarily due to the limited size and low number of events in the external cohort, as previously discussed. Consequently, the generalisability of the developed models could not be adequately determined. Another limitation was the reliance on retrospective data, which may have introduced biases related to RT techniques and dosimetry accuracy.

To confirm the generalisability of our prediction models, future
research should assemble larger, more representative datasets for robust
external validation. Site-specific validation sets can recalibrate our
models by updating the mean linear predictor and baseline ACE risk to
align with local populations, without changing the model's coefficients. Furthermore, investigating the impact of proton therapy compared to photon-based techniques on ACE risk is warranted. Additionally, integrating cardiac health biomarkers like genetic predispositions and baseline cardiac function, as studied in the MEDIRAD EARLY-HEART project [39], could refine prediction models. Although adding LAD and CAC dose did not improve the prediction model, further research should explore these connections.

## 5. Conclusion

We identified key predictors of ACE following BC RT by integrating cardiovascular risk factors, systemic therapies, and cardiac dose parameters. The combination of WH-Dmean, LV-V5, and total coronary calcification volume yielded the strongest predictive performance, with all models showing an AUC above 0.73 at ten-year internal validation. External validation was attempted but unsuccessful due to limited sample size and event count. These findings support dose optimisation and clinical decision-making to identify patients who may benefit from preventive strategies.

## Funding

The Horizon 2020 MEDIRAD project received funding from the Euratom Research and Training Programme 2014–2018 under grant agreement No 755523.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the author(s) used 'Microsoft Copilot' in order to improve the language and readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The Horizon 2020 MEDIRAD project received funding from the Euratom research and training programme 2014–2018 under grant agreement No 755523. In addition to this grant, authors HL, RV and SC reported personal relationships/activities/interests related to the content of this manuscript. All authors declare that these activities do not affect the integrity of the research presented.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ejca.2025.116055.

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