Coronary artery calcification burden in cancer vs. non-cancer populations: a systematic review and meta-analysis

Abstract

Background

Coronary artery disease (CAD) events contribute to morbidity in cancer survivors, yet it is unclear whether this reflects increased atherosclerotic burden. Coronary artery calcium (CAC) scoring is a sensitive, non-invasive marker of atherosclerotic burden. This review aims to establish differences in the prevalence and severity of CAC between cancer survivors and non-cancer controls.

Methods

MEDLINE, EMBASE, and Cochrane databases were searched from inception to January 2025 for studies comparing CAC between adult cancer patients and matched non-cancer controls. The primary outcome was CAC presence (Agatston score > 0) and the secondary outcome was CAC score. We recorded differences in demographic and cardiovascular risk profiles between cancer patients and controls. Random-effects meta-analysis was used to estimate pooled risk differences and Cohen’s d.

Results

Twelve studies (2,182 cancer patients [mean age 59.8 years, 49% female]; 16,524 controls [mean age 58.7 years, 54% female]) were included. Cancer patients and controls had overall comparable cardiovascular risk profiles. Cancer patients were older, more often male, and had more dyslipidaemia but less hypertension and diabetes. CAC prevalence did not differ between groups (risk difference 0.05; 95% CI: − 0.03 to 0.12; p = 0.22). A secondary analysis found modestly higher CAC severity in cancer patients (Cohen’s d = 0.29; 95% CI: 0.01 to 0.57; p = 0.04), though between-study heterogeneity was high. Female sex was a potential contributor to this increase.

Conclusions

There was no significant difference in CAC prevalence between cancer survivors and non-cancer controls. Further research could clarify whether the CAD mortality in this population reflects plaque vulnerability, thrombotic risk or other causes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40959-025-00422-6.

Background

Cancer and cardiovascular disease (CVD) represent the two most common causes of death globally [1–3] and their intersection is an emerging clinical frontier. Advances in cancer treatments have significantly improved long-term survival – survivors are projected to increase from 18 million in 2022 to 22 million by 2023 in the United States alone [4, 5]. However, this success has introduced a new clinical challenge: cancer survivors now face markedly increased rates of cardiovascular morbidity and mortality [5–7], with coronary artery disease (CAD) being one of the leading non-cancer causes of death across many malignancies. Cancer survivors face double the CAD risk of non-cancer patients [8]. Potential reasons include greater atherosclerotic burden driven by shared cardiovascular risk factors and malignancy-driven inflammation[5, 8, 9], increased vulnerability to plaque rupture [10] and thrombosis [11] – possibly related to cardiotoxicity of cancer therapies. Understanding the principal driver of this process seems to be the first step in preventing it.

Coronary artery calcium (CAC) scoring is a reliable, non-invasive marker of the extent of subclinical atherosclerosis [12]. CAC has been extensively validated in the general population [13, 14]. It independently predicts myocardial infarction and cardiovascular mortality beyond traditional risk factors, improving upon risk classification when added to conventional scores [13]. However, the differences in CAC prevalence amongst cancer patients and non-cancer patients remains unclear. To address this knowledge gap, we conducted a systematic review and meta-analysis comparing differences in CAC prevalence between adult cancer patients and non-cancer controls. This analysis seeks to clarify whether the CAD risk of survivors reflects a greater prevalence of atherosclerosis.

Methods

Search strategy

This review was conducted according to PRISMA guidelines and registered in (10.17605/OSF.IO/MQNBX). Electronic searches were conducted in MEDLINE, EMBASE, and EBMR (OvidSP) from inception to January 21, 2025. A comprehensive search strategy was used to improve sensitivity in articles due to variability in the terminology for cancer and coronary atherosclerosis. The search strategy can be summarised as “cancer” AND (“coronary artery disease” OR “atherosclerotic plaque”) whereby key terms were represented alongside all synonyms. All papers were screened for inclusion by 2 authors (J.W and J.C). The full search strategy is detailed in Supplementary Table 1.

Study selection

Inclusion criteria for eligible articles were: (i) included both adult cancer patients and non-cancer controls, (ii) CAC defined by CT, (iii) sample size > 10 per group. Exclusion criteria were (i) systematic reviews, (ii) case reports, (iii) non-English language.

Study outcomes

The primary outcome was the prevalence of CAC (defined as CAC > 0) in cancer patients compared to non-cancer controls. The secondary outcome was CAC severity (reported as a continuous score). Between-group differences in baseline demographics and cardiovascular risk profiles were also gathered.

Data extraction

Data extraction were performed by one author (JHW), followed by an independent evaluation of extractions for correctness by a second author (JC). For each study, details regarding study design, year of publication, patient age, cancer type, CAC score, cardiovascular risk profile (diabetes, hypertension, dyslipidaemia, body mass index, family history, ethnicity, smoking history), lipid and inflammatory profile [(cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides, C-reactive protein (CRP)] and medication history (lipid control, cancer therapy) were extracted.

Statistical analysis

Participant characteristics were pooled across studies and compared using chi-square tests (categorical variables) and unpaired t-tests (continuous variables). Meta-analyses were performed using a random-effects, restricted maximum likelihood (REML) model. For studies reporting CAC as a binary outcome, risk differences (RD) with 95% confidence intervals (CI) were calculated. For continuous outcomes, CAC medians and interquartile ranges were converted to means and standard deviations using established methods and pooled using Cohen’s d with 95% CI[15, 16]. To assess the influence of individual studies on the pooled estimate, we conducted a leave-one-out (LOO) sensitivity analysis on CAC prevalence and severity. For each iteration, we recorded the changes in pooled risk difference (prevalence), Cohen’s d (severity), the 95% CI and p-value. Univariable meta-regression was conducted to explore sources of heterogeneity in CAC severity using study-level covariates. Funnel plot inspection and Egger’s regression test were used to assess for risk of publication bias. All analyses were performed in Stata v18.0, with statistical significance defined at p-value of < 0.05.

Results

Study selection

A total of 10,197 articles were identified through database searching. After removal of 1,435 duplicates, 8,763 articles remained. Title and abstract screening excluded 8,763 articles based on predefined eligibility criteria. Full-text assessment was conducted on 281 articles, of which 175 were excluded due to lack of cancer group, absence of plaque data, non-coronary plaque, conference abstracts, full text unavailability, duplicates, or non-English language. Of the remaining 106 articles, 96 were excluded for lacking a non-cancer control group. Ultimately, 12 studies met inclusion criteria and were incorporated into the review and meta-analysis (Fig. 1).

Fig. 1.

PRISMA Flow Diagram of Study Selection Process

Study characteristics

The final cohort included 2,182 cancer patients and 16,524 non-cancer controls (Table 1). Of the included studies, nine focused on general, asymptomatic cancer populations undergoing routine or screening coronary imaging. Three studies examined patients who underwent imaging due to suspected or established coronary artery disease. The most common cancer types were lung (n = 550, 24.4%), breast (n = 374, 16.6%), skin (n = 295, 13.1%), and haematological malignancies (n = 295, 13.1%). Other cancer types included genitourinary (n = 232, 10.3%), gastrointestinal (n = 122, 5.4%), prostate (n = 69, 3.1%), thyroid (n = 39, 1.7%), and head and neck cancers (n = 7, 0.3%), with an additional 268 cases (11.9%) classified as other or unspecified (Table 2).

Table 1.

Study design and patient characteristics of the included studies

Author (Year)	Study Design and Entry Criteria	Cohort Size			Age		Sex (Female)
		Total	Cancer	Control	Cancer	Control	Cancer	Control
Whitlock (2015) [22]	Prospective MESA-population based, investigated progression of CAC in those who later developed cancer vs. remained cancer-free (asymptomatic)	3,122	135	2987	59.7 (± 9.3)	63.04 (± 8.9)	16	54
Vrtovec (2017) [24]	Cross-sectional comparison of CAC, carotid stiffness in ET patients to in matched controls (asymptomatic)	82	40	42	57.1 (± 14.1)	58.2 (± 13.1)	26	26
Van Rosendael (2017) [23]	Cross-sectional study of HL/NHL survivors’ post-radiotherapy vs. controls via CTCA (asymptomatic)	316	79	237	45 (± 11)	45 (± 11)	48	144
Azencot (2021) [20]	Long-term prospective follow-up of asymptomatic T2DM patients with baseline CAC/CTCA, investigating later cancer and CV events (asymptomatic)	735	155	580	64.1 (± 5.3)	63.3 (± 5.3)	36	307
Patel (2024) [19]	Retrospective cohort analysis of cancer vs. non-cancer, using CAC scoring to correlate to cardiovascular outcomes (asymptomatic)	10,742	703	703	61.4 (± 8.6)	61.3 (± 8.5)	-	-
Mast (2011) [27]	Retrospective comparison of baseline CAC in early pre-radiotherapy breast cancer, matched to MESA control population (asymptomatic)	6,894	80	6814	< 45 (13); 45–54 (13); 55–64 (33); 65–75 (19); 75–84 (2)	< 45 (0); 45–54 (2051); 55–64 (1887); 65–74 (1949); 75–84 (927)	-	-
Drofenik (2020) [26]	Retrospective comparison of ET patients vs. controls, assessing CAC, carotid artery stiffness (asymptomatic)	72	36	36	55.11	59.07	24	24
Nazari (2022) [21]	Retrospective imaging study evaluating coronary artery luminal area in phaeochromocytoma patients undergoing CTCA (asymptomatic)	32	16	16	37.9 (± 10)	38.4 (± 9)	9	9
Polomski (2023) [17]	Retrospective cross-sectional study of asymptomatic HL survivors versus matched non-cancer controls via CCTA for baseline CAC (asymptomatic)	194	97	97	45.5 (± 9.9)	45.5 (± 10.3)	60	60
Shabestari (2018) [25]	Prospective comparative study of breast cancer patients’ post-radiotherapy vs. controls, evaluating CAC for screening benefit (possible CAD)	100	50	50	50.4	50.5	-	-
Rodriguez-Granillo (2017) [18]	Retrospective review of patients with or without malignancy undergoing general thoracic CT, assessing coronary atherosclerosis and mortality risk (possible CAD)	1,901	500	1401	57.2 (± 9.9)	58.8 (± 9.4)	271	771
Xie (2024) [28]	Retrospective study of lung cancer patients on chemotherapy and chemoradiotherapy undergoing CTCA to evaluate coronary burden (possible CAD)	516	391	125	CHT 64 (51–77); CRT 64 (51–78)	63 (52–76)	125	46

Table 2.

Distribution of cancer types among participants within the included studies

Author (Year)		Cancer Type
	Head & Neck		Thyroid	Breast	Lung	Gastrointestinal	Genitourinary	Prostate	Haem	Skin	Other
Whitlock (2015) [22]	-		-	13	8	15	8	55	6	-	30
Vrtovec (2017) [24]	-		-	-	-	-	-	-	40	-	-
Van Rosendael (2017) [23]	-		-	-	-	-	-	-	79	-	-
Azencot (2021) [20]	7		-	18	14	17	21	14		31	4
Patel (2024) [19]	-		39	113	-	37	148	0	37	264	63
Mast (2012) [27]	-		-	80	-	-	-	-	-	-	-
Drofenik (2020) [26]	-		-	-	-	-	-	-	36	-	-
Nazari (2022) [21]	-		-	-	-	-	-	-	-	-	16
Polomski (2023) [17]	-		-	-	-	-	-	-	97	-	-
Shabestari (2018) [25]	-		-	50	-	-	-	-	-	-	-
Rodriguez-Granillo (2017) [18]	-		-	100	137	53	55	-	-	-	155
Xie (2024) [28]	-		-	-	391	-	-	-	-	-	-
Total, n (%)	7 (0.3%)		39 (1.7%)	374 (16.6%)	550 (24.4%)	122 (5.4%)	232 (10.3%)	69 (3.1%)	295 (13.1%)	295 (13.1%)	268 (11.9%)

Baseline demographics and cardiovascular risk factors

Table 3 summarises pooled demographic and clinical characteristics reported across the included studies. Cancer patients were older than controls (60 ± 12 vs. 59 ± 10 years, p < 0.001) and less frequently female (49% vs. 54%, p < 0.001). Ethnicity was reported in two studies, with a higher proportion of white participants in the cancer group (52% vs. 39%, p < 0.001). Body mass index was lower among cancer patients (24.1 ± 3.67 vs. 25.7 ± 4.67 kg/m², p < 0.001), as was waist circumference in males (95.0 ± 10.8 vs. 101.6 ± 10.3 cm, p < 0.05); no significant difference was observed in females.

Table 3.

Pooled baseline characteristics of cancer and control populations from all included studies

Baseline Characteristics	Studies (n)	Cancer (n)	Non-Cancer (n)	Cancer	Non-Cancer	P-value
Demographics
Age (mean ± SD)	10	2152	6226	59.83 ± 12.36	58.74 ± 10.36	< 0.001
Female (n, %)	10	1499	5573	739 (49%)	3020 (54%)	< 0.001
White (n, %)	2	151	3003	78 (52%)	1166 (39%)	< 0.001
BMI (kg/m²)	5	638	801	24.1 ± 3.67	25.7 ± 4.67	< 0.001
Waist Circumference (cm)
└ Male	2	26	32	95.0 ± 10.8	101.6 ± 10.3	< 0.05
└ Female	2	50	50	89.6 ± 8.95	89.4 ± 13.9	0.932
Cancer Type (n, %)
└ Breast Cancer	6	374	-	374 (17%)	-	-
└ Lung Cancer	4	550	-	550 (24%)	-	-
└ Haematological Cancer	6	295	-	295 (13%)	-	-
Cardiovascular Risk Factors
Hypertension (n, %)	8	1551	11,029	480 (31%)	4417 (40%)	< 0.001
Dyslipidaemia (n, %)	7	1471	4215	747 (51%)	1086 (26%)	< 0.001
Diabetes Mellitus (n, %)	9	1605	4778	272 (17%)	969 (20%)	< 0.01
Family History of CAD (n, %)	3	165	325	7 (4%)	18 (6%)	0.690
Smoking History (n, %)	11	1782	11,689	741 (42%)	4824 (41%)	0.823
Pathology
Cholesterol (mmol/L)	4	227	3083	4.96 ± 1.07	5.02 ± 0.98	0.377
HDL (mmol/L)	4	227	3083	1.33 ± 0.42	1.32 ± 0.39	0.711
LDL (mmol/L)	3	92	96	2.7 ± 0.74	2.79 ± 0.73	0.402
Triglycerides (mmol/L)	3	92	96	1.67 ± 0.83	1.58 ± 0.81	0.453
CRP (mg/L)	2	191	618	3.51 ± 3.17	3.31 ± 3.16	0.445
Treatment
Lipid Control Medications (n, %)	4	327	3337	36 (11%)	498 (15%)	0.067
Cancer Treatment (n, %)
└ Chemotherapy	4	617	-	585 (95%)	-	-
└ Surgery	1	50	-	50 (100%)	-	-
└ Radiotherapy	4	617	-	367 (59%)	-	-

Among studies that reported each variable, the prevalence of hypertension (31% vs. 40%, p < 0.001), dyslipidaemia (51% vs. 26%, p < 0.001), and diabetes mellitus (17% vs. 20%, p < 0.01) was lower in cancer patients compared to controls. Smoking history (42% vs. 41%, p = 0.823) and family history of coronary artery disease (4% vs. 6%, p = 0.690) did not differ significantly between groups. No significant differences were observed in total cholesterol, HDL, LDL, triglycerides, or CRP levels.

Cancer treatment data were reported in only a subset of studies − 585 patients received chemotherapy, 367 patients received radiotherapy, 50 received surgery. Doses for chemotherapy and radiotherapy were generally not reported.

CAC prevalence in cancer patients

Five studies reported categorical CAC data (Table 4, presence vs. absence of calcification) [17–21]. Pooled analysis demonstrated a non-significant difference in CAC prevalence between cancer patients and controls (risk difference = 0.05; 95% CI: −0.03 to 0.12; p = 0.22; Fig. 2). Statistical heterogeneity was high (I²=81.6%).

Table 4.

Summary of coronary artery calcium findings in cancer vs. Control populations

Author (Year)	Continuous CAC		Categorical CAC
	Cancer	Control	CAC=0 (n)		CAC > 0 (n)
			Cancer	Control	Cancer	Control
Whitlock (2015)[22]	♀: 111.2 (30.8-409.1)♂: 87.6 (27.8-310.2)	♀: 46.9 (12.3-152.3) ♂: 87.0 (20.8-295.1)	-	-	-	-
Vrtovec (2017)[24]	0.1 (0-16.85)	0 (0-8.55)	-	-	-	-
Van Rosendael (2017)">[23]	0 (0-26) 65 ±166	0 (0-3) 36 ±159	-	-	-	-
Azencot (2021)[20]	61 (1-326)	59 (1-328)	37	136	118	444
Patel (2024)[19] *	-	-	318	275	385	427
Mast (2012)[27] °	°	°	-	-	-	-
Drofenik (2020)[26]	0 (0-8.6)	0 (0-8.6)	-	-	-	-
Nazari (2022)[21]	0 (0-0)	0 (0-0)	-	-	-	-
Polomski (2023)[17]	-	-	63	77	34	20
Shabestari (2018)[25]	0 (0-1.5)	0 (0-0)	-	-	-	-
Rodriguez-Granillo (2017)[18]	-	-	236	844	264	647
Xie (2024)[28]#	-	-	122	52	269	74

* Patel (2024) [19]: Categorical CAC values for cancer vs. control: CAC 1-100 (n=178, n=229); CAC 101-400 (n=113, n=122); CAC >400 (n=94, n=76)

^#Xie (2024) [28]: Categorical CAC values for cancer vs. control: CAC 1-100 (n=127, n=46); CAC 101-400 (n=62, n=18); CAC >400 (n=80, n=10)

° Mast (2012): CAC percentiles (25th, 50th, 75th, 95th) in cancer vs. controls: 45–54 years (cancer=0, 0, 0, 8.1; controls=0, 0, 0, 31); 55–64 years (cancer=0, 0, 100, 665; controls=0, 0, 17, 200); 65–74 years (cancer=12.3, 50, 137, 410; controls=3, 17, 123, 641)[27].

Fig. 2.

Meta-analysis of the association between cancer status and presence of coronary artery calcium (CAC >0 vs CAC=0)

CAC severity in cancer patients

Eight studies (including sex-stratified data from Whitlock et al.)[22] reported continuous CAC values [20, 21, 23–27]. Meta-analysis demonstrated significantly greater CAC severity among cancer patients compared to controls (Cohen’s d = 0.29; 95% CI: 0.01 to 0.57; p = 0.04; Fig. 3). In the Whitlock study, higher CAC scores were observed in the cancer cohort, with statistical significance in females (Cohen’s d = 1.03) but not in males (Cohen’s d = 0.04). Inter-study heterogeneity was high (I²=84.5%). Three studies reported no significant difference in CAC severity between groups [20, 21, 26].

Fig. 3.

Meta-analysis of the association between cancer status and severity of coronary artery calcium (CAC score as continuous variable)

LOO analysis

Leave-one-out sensitivity analysis for CAC prevalence produced pooled risk differences ranging from 0.03 to 0.08 for each iteration (Table 5). The overall association remained non-significant in four out five iterations except Patel et al’s [19] study which found an increased the pooled RD to 0.077 (95% CI 0.017 to 0.137; p = 0.011). For CAC severity, the pooled Cohen’s d values ranged from 0.15 to 0.34 for each iteration. The effect estimates were similar across omissions (Table 6).

Table 5.

Leave-one-out sensitivity analysis for CAC prevalence

Omitted study	Risk diff.	95% CI (Lower)	95% CI (Upper)	p-value
Rodriguez-Granillo 2017 [18]	0.035	−0.058	0.128	0.459
Patel 2024 [19]	0.077	0.017	0.137	0.011
Azencot 2021 [20]	0.062	−0.031	0.155	0.189
Xie 2024 [28]	0.036	−0.053	0.124	0.429
Polomski 2023 [17]	0.030	−0.050	0.110	0.462
Overall (theta)	0.047	−0.028	0.123	0.218

Table 6.

Leave-one-out sensitivity analysis for CAC severity

Omitted study	Cohen’s d	95% CI (Lower)	95% CI (Upper)	p-value
Whitlock 2015 – F [22]	0.146	−0.015	0.308	0.076
Whitlock 2015 – M [22]	0.328	0.017	0.639	0.039
Vrtovec 2017 [24]	0.284	−0.030	0.599	0.077
Shabestari 2018 [25]	0.236	−0.061	0.532	0.119
van Rosendael 2017 [23]	0.303	−0.017	0.624	0.063
Azencot 2021 [20]	0.337	0.031	0.643	0.031
Drofenik 2020 [26]	0.323	0.020	0.626	0.037
Nazari 2022 [21]	0.312	0.014	0.610	0.040
Overall (theta)	0.287	0.010	0.563	0.042.

Meta-regression analysis

Univariable meta-regression was conducted to explore sources of heterogeneity in CAC severity. Table 7 shows that sex (female) was the only variable significantly associated with greater CAC severity in both cancer (coefficient: 0.009; 95% CI: 0.002 to 0.015; p = 0.009; R²=54.7%) and control cohorts (coefficient: 0.009; 95% CI: 0.002 to 0.015; p = 0.01; R²=53.3%). No other demographic or clinical variables - including age, comorbidities, lipid markers, or cancer subtype - demonstrated significant associations or meaningful explanatory power. Total cholesterol in the cancer group showed moderate effect size (R²=47%) but was not statistically significant (p = 0.08).

Table 7.

Univariable meta-regression of demographic and clinical factors associated with CAC severity in cancer and control groups

Variable	Coefficient (95% CI)	P-value	N	R² (%)
Cancer group
Age (years)	0.005 (–0.030 to 0.041)	0.76	8	0
Female sex (%)	0.009 (0.002 to 0.015)	0.009	8	54.7
Hypertension (%)	–0.003 (–0.026 to 0.020)	0.81	5	0
Smoking history (%)	–0.004 (–0.017 to 0.010)	0.6	8	0
Diabetes mellitus (%)	–0.003 (–0.012 to 0.005)	0.46	8	0
Dyslipidaemia (%)	0.015 (–0.013 to 0.043)	0.31	5	0.1
Family history of CAD (%)	–0.003 (–0.169 to 0.162)	0.97	3	0
Total cholesterol (mmol/L)	1.43 (–0.15 to 3.00)	0.08	5	47
HDL (mmol/L)	0.31 (–2.64 to 3.25)	0.84	5	0
LDL (mmol/L)	0.43 (–2.78 to 3.64)	0.8	3	0
Triglycerides (mmol/L)	0.17 (–0.68 to 1.02)	0.69	3	0
Breast cancer (%)	0.006 (–0.027 to 0.037)	0.76	4	0
Haematological cancer (%)	–0.005 (–0.018 to 0.008)	0.47	6	0
Lung cancer (%)	–0.010 (–0.110 to 0.090)	0.85	3	0
Control group
Age (years)	0.003 (–0.036 to 0.042)	0.88	8	0
Female sex (%)	0.009 (0.002 to 0.015)	0.01	8	53.3
Hypertension (%)	0.010 (–0.036 to 0.056)	0.67	5	0
Smoking history (%)	–0.003 (–0.018 to 0.012)	0.69	8	0
Diabetes mellitus (%)	–0.003 (–0.012 to 0.005)	0.43	8	0
Dyslipidaemia (%)	0.022 (–0.028 to 0.073)	0.39	5	0
Family history of CAD (%)	–0.021 (–0.162 to 0.120)	0.77	3	0
Total cholesterol (mmol/L)	0.79 (–1.22 to 2.79)	0.44	5	0
HDL (mmol/L)	0.22 (–2.34 to 2.78)	0.87	5	0
LDL (mmol/L)	–1.27 (–4.87 to 2.33)	0.49	3	0
Triglycerides (mmol/L)	0.46 (–1.83 to 2.41)	0.64	3	99.2

Publication bias

Visual inspection of the funnel plot for CAC severity (Fig. 4) showed mild asymmetry with smaller studies tending toward larger effect sizes. However, Egger’s regression test was not statistically significant (p = 0.18), suggesting low likelihood of publication bias. The plot remained generally symmetric around the estimated pooled effect (Fig. 4). The risk of bias of every included study, assessed via the Newcastle Ottawa Scale, was low to moderate with an overall score of 62/72 (Table 8).

Fig. 4.

Funnel plot in included studies

Table 8.

Assessment of the quality of these studies using an adapted Newcastle Ottawa scale

Author (year)	Selection			Comparability			Outcome			Total
	Representative of exposed cohort (cancer)	Selection of external control	Ascertainment of exposure	Outcome of interest	Main Factor	Additional Factor	Assessment of Outcomes	Sufficient follow-up time	Adequacy of follow-up
Whitlock (2015)[22]	★	★	★	N/A	★	★	★	N/A	N/A	6/6
Vrtovec (2017)[24]	★	★	★	N/A	★	★	★	N/A	N/A	6/6
Shabestari (2018)[25]	★	★	★	N/A	★		★	N/A	N/A	5/6
Van Rosendael (2017)[23]	★	★	★	N/A	★	★	★	N/A	N/A	6/6
Xie (2024)[28]	★		★	N/A	★		★	N/A	N/A	4/6
Rodriguez-Granillo (2017) [18]	★	★	★	N/A			★	N/A	N/A	4/6
Mast (2012)[27]	★		★	N/A	★	★	★	N/A	N/A	5/6
Drofenika (2020)[26]	★		★	N/A	★	★	★	N/A	N/A	5/6
Nazari (2022)[21]	★	★	★	N/A	★		★	N/A	N/A	5/6
Azencot (2021)[20]	★	★	★	N/A	★		★	N/A	N/A	5/6
Polomski (2023)[17]	★	★	★	N/A	★	★	★	N/A	N/A	6/6
Patel (2024)[19]	★		★	N/A	★	★	★	N/A	N/A	5/6

Selection item 4 was deemed not applicable because our study did not require a minimum interval between cancer diagnosis and CAC measurement; it only required confirmation that cancer status was established before CAC assessment. Outcome items 7 and 8 were considered not applicable, as the outcomes of interest (CAC presence and severity) were generally assessed at a single time point, with no longitudinal follow-up needed to evaluate prevalence or severity

Discussion

In this systematic review and meta-analysis, we found no significant difference in CAC prevalence between cancer patients and non-cancer controls, with a substantial heterogeneity between studies. Both groups had broadly similar traditional cardiovascular risk profiles. A secondary analysis revealed a minor increase in CAC severity among cancer patients; however, this should be interpreted cautiously due to high inter-study variability and heterogeneity.

Pathophysiological of CAD in cancer survivors

The problem of CAD among survivors is evidenced by epidemiological data pertaining to CAD events. This is not necessarily contradicted by the absence of increased CAC prevalence. Several shared mechanisms may contribute to accelerated vascular aging in cancer survivors. Chronic inflammation is a fundamental process in both cancer and atherosclerosis [20, 29–31] – not only resulting in the development of both distinct diseases, but also because of the disease itself. Activated oncogenes and inactivated tumour-suppressor genes can produce pro-inflammatory molecules, fostering a microenvironment conducive to CVD development [11]. Malignant tissue can directly promote release of cytokines leading to a cascade of endothelial damage and atherosclerotic plaque formation [9, 32]. Beyond the cancer itself, oncological therapies, including anthracyclines [33, 34], radiotherapy [19, 35] and immunotherapy [29, 36], can provoke endothelial dysfunction, oxidative stress, and prothrombotic states, accelerating plaque progression independently of baseline cholesterol or glycaemic status. Finally, an often-overlooked aspect of the unique oncological stressors is the indirect impacts of cancer and cancer treatment on CVD. Exercise intolerance and sedentarism are common sequalae of cancer, secondary to weakness, muscle wasting, and fatigue [9, 36]. Collectively, these factors may produce atherosclerosis that is morphologically distinct from classic lipid-rich, calcified plaques - often dominated by non-calcified or fibrous lesions that escape CAC detection [37]. This may explain why CAC prevalence is not elevated in cancer survivors despite higher cardiovascular risk.

CAC in cancer survivors and controls

Our analysis suggests that the elevated atherosclerotic cardiovascular disease (ASCVD) risk seen in cancer survivors cannot be fully attributed to a higher burden of calcified plaque. One possible explanation is that the absence of a significant difference in CAC prevalence between cancer patients and non-cancer controls may suggest that cancer-related vascular injury may not consistently lead to detectable coronary calcification. It is possible that treatment-induced vascular damage, systemic inflammation, and metabolic disruption instead promote the development of non-calcified or fibrotic plaques, or accelerate early atherosclerosis to a level below the detection threshold of standard CAC scoring [12, 38]. In other words, CAC may underestimate cardiovascular risk in cancer survivors.

While the overall pooled analysis did not show a significant difference in CAC prevalence, three individual studies reported modestly elevated rates amongst cancer patients. One explanation may be that these studies disproportionally included individuals with thoracic malignancies – commonly treated with mediastinal or chest wall radiotherapy, which is associated with increased coronary calcification in a dose- and proximity-dependant manner[9, 33, 35]. In contrast, the two studies with null findings enrolled more heterogenous cancer cohorts, with thoracic cancers comprising less than a quarter of participants. These patterns may suggest that treatment exposures, particularly radiotherapy, may influence CAC in selected subgroups, though this does not appear to generalise across cancer populations more broadly. The minor increase in CAC severity observed in our secondary analysis may reflect plaque healing or stabilisation rather than progressive disease, especially among patients treated with statins or exposed to radiation. However, this observation should be interpreted cautiously, given the high heterogeneity across studies and the lack of detailed treatment data.

Sensitivity analysis

In the leave-one-out analyses, the pooled estimates for both CAC prevalence and severity were generally robust. For CAC prevalence, only removal of the Patel et al.’s [19] study shifted the pooled effect from null to a statistically significant positive association. Patel et al.’s [19] was the only study in which CAC prevalence was higher in the non-cancer cohort compared with the cancer group. This finding may be partly attributable to a higher burden of cardiovascular risk factors found in the non-cancer cohort (including hypertension, obesity, type 2 diabetes, and dyslipidaemia). Furthermore, the cancer cohort comprised a high proportion of skin and genitourinary malignancies which are unlikely to have been treated with thoracic radiotherapy. For CAC severity, all studies were weighted similarly, and the leave-one-out results indicated a balanced mix of positive and null findings across studies, with the overall signal emerging because the positive effects slightly outweighed the negatives.

Practical considerations - screening for CAD

There are currently no universally accepted guidelines for CAD monitoring in cancer survivors [39]. Traditional cardiovascular risk assessments often underperform in cancer populations [40]. Guidelines for risk assessment remain crucial, not only to ensure cost-effective use of available resources, but more importantly to enable early detection of CAD before major adverse cardiovascular events (MACE) occur, reducing cardiovascular-related mortality in cancer patients. Amongst oncology patients, incidental CAC is identified in 30–70% of routine computed tomography (CT)s, yet remains underreported and rarely acted upon, representing a potential missed opportunity for low-barrier risk stratification and early intervention [33]. Nonetheless, the results of this study suggest that plaque identification may not be the only – or even the main – step for reliable assessment of atherosclerotic risk in cancer patients. Overall, while CAC remains a valuable tool for population-level risk stratification, its limitations in oncology populations highlight the need for a multimodal approach that incorporates treatment history, systemic inflammation, and imaging modalities capable of detecting non-calcified or treatment-altered atherosclerotic plaque phenotypes.

Study limitations

Our systematic review is the first to compare CAC prevalence and severity between cancer survivors and non-cancer controls. However, several limitations should be acknowledged. First, this review is limited by the lack of detailed treatment data, particularly chemotherapy regimens or radiation dosage, key factors when evaluating CAC amongst survivors, but often unavailable in primary care and research settings. Second, the heterogeneity in cancer types likely contributed to variability in findings. While this reflects real-world diversity, the small sample sizes within subtypes limited our ability to perform stratified analyses. Third, the predominantly cross-sectional design of included studies limits casual interpretation, precluding conclusions about whether CAC reflects pre-existing cardiovascular risk or treatment-related effects.

Conclusions

This systematic review found no significant difference in CAC prevalence between cancer survivors and non-cancer controls, suggesting the elevated ASCVD risk observed in cancer survivors is unlikely to be explained by an increased burden of calcified plaque. This finding implies that CAC alone may not adequately capture the full extent of vascular changes associated with cancer and its treatments. Further research is needed to explore other, more sensitive and specific screening methodologies to identify cancer patients who are at an increased risk of CAD, enabling earlier intervention with protective therapies.

Abbreviations

ASCVD

Atherosclerotic Cardiovascular Disease

BMI

Body Mass Index

CAC

Coronary Artery Calcium

CAD

Coronary Artery Disease

CI

Confidence Interval

CRP

C–Reactive Protein

CT

Computed Tomography

CVD

Cardiovascular Disease

HDL

High–Density Lipoprotein

LDL

Low–Density Lipoprotein

LOO

Leave–One–Out

MACE

Major Adverse Cardiovascular Events

OvidSP

Ovid Search Platform

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta–Analyses

R²

Coefficient of Determination

RD

Risk Difference

REML

Restricted Maximum Likelihood

Authors’ contributions

James Hua Wang - searched and reviewed the articles and abstracts, wrote the paperJacky Chen - searched and reviewed the articles and abstracts, co-wrote the paperJoshua Wong - provided content knowledge, revised the paperThomas H Marwick - co-designed the study and analyses, obtained funding and revised the paperCheng Hwee Soh - supervised the primary authors, designed the study, completed the analyses and revised the paper.

Funding

T.H.M. is supported by an Investigator grant from the National Health and Medical Research Council, Canberra, Australia (2008129).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.