A field of view-based assessment of soft tissue calcifications in cone beam computed tomography of the maxillofacial region

Abstract

Soft tissue calcifications are frequently encountered in radiographic examinations. Since some of these calcifications are associated with systemic diseases and potentially serious conditions, accurate radiographic diagnosis can assist in managing the underlying pathology. This study aimed to determine the frequency and size of soft tissue calcifications in cone beam computed tomography (CBCT) scans covering either the maxilla or mandible. 420 CBCT scans with a field of view (FOV) of either the maxilla or mandible were evaluated in patients aged 6 to 82 years. Depending on the FOV type, the scans were evaluated for the presence and size of various calcifications. Furthermore, associations between calcification frequency and size with age and sex were statistically analyzed. A significance level of 0.05 was applied. Tonsilloliths were the most frequently observed calcifications in both FOV types, followed by calcified stylohyoid ligaments (CSLs), with most calcifications being of medium size. Overall, soft tissue calcifications were more frequently detected in the mandibular FOV (p = 0.01). Independent of calcification type and FOV, a significant correlation was observed between increased patient age and both higher frequency (p < 0.001) and larger size (p = 0.003) of calcifications. Tonsilloliths and CSLs are the most common calcifications observed in CBCT scans of both the maxilla and mandible. In mandibular FOVs, especially in patients ≥ 50 years, particular attention should be paid to calcifications of the carotid arteries, stylohyoid ligaments, and tonsilloliths. An FOV‑specific checklist may improve reporting consistency and referrals for appropriate medical/dental follow‑up.

Soft tissue calcifications (STCs) are commonly observed in dental radiographs and are often incidental findings. While many of these calcifications are asymptomatic and benign, some may be related to systemic diseases or even potentially life-threatening conditions. Early radiographic detection can play a key role in prompt diagnosis and medical intervention^1–3.

There are various types of STC in the head and neck region with distinct etiologies, clinical significance, and radiographic features. These calcifications may occur as a result of dystrophic, idiopathic, or metastatic mechanisms^4,5. The prevalence of calcifications varies across populations and may be influenced by age, sex, anatomical location, and systemic health conditions^6,7. A wide range of head and neck structures can undergo pathological calcification, including carotid arteries, stylohyoid ligaments, salivary glands, lymph nodes, and venous structures. For example, sialoliths can cause pain and dysfunction in salivary glands, while carotid artery calcifications may indicate a heightened risk of stroke^8–10. In diagnosing STCs, it is essential to distinguish pathological calcifications from anatomical structures such as the hyoid bone, styloid process, and thyroid cartilage. Critical diagnostic criteria include the calcification’s location, distribution, number, size, and shape^11–13.

Imaging techniques such as panoramic radiographs, although commonly used, have limitations in precise localization and differentiation due to their two-dimensional nature. Cone-beam computed tomography (CBCT) provides three-dimensional imaging with high spatial resolution and lower radiation exposure compared to conventional CT, making it particularly useful for evaluating the maxillofacial structures. Since calcifications are often incidental findings, careful evaluation of the entire scanned volume regardless of the imaging target is crucial^1,5,14,15.

While many CBCT scans are acquired in a way that they only focus on the maxilla or mandible according to the diagnostic purpose, few studies have examined STC prevalence in CBCT with different fields of view^3,6,7. Since some STCs may arise from systemic diseases or alarming conditions, their detection has diagnostic and clinical value. Prior work has described overall prevalence; however, three gaps remain: (i) FOV‑specific yield is rarely quantified; (ii) reliability (κ/ICC) is not consistently reported; and (iii) clinical guidance is often generic, limiting actionability.

Recent studies propose staging for external carotid artery calcifications (ECACs) on CBCT and advocate opportunistic vascular assessment during routine imaging, underscoring the potential for patient‑level impact^16,17. We therefore evaluated FOV‑specific detection and reliability, with age‑specific analyses to align with cardiovascular risk enrichment.

The present study is designed to estimate STC prevalence by FOV (mandibular vs. maxillary), age, and sex; quantify inter‑observer reliability; and propose practical reporting guidance.

Materials and methods

This cross-sectional analytical study was conducted after obtaining ethical approval from the Research Ethics Committee of Guilan University of Medical Sciences (Approval ID: IR.GUMS.REC.1400.116). All methods were carried out in accordance with relevant guidelines and regulations of the Declaration of Helsinki. Informed consent was obtained from all subjects and/or their legal guardian(s). A total of 420 CBCT scans were systematically randomly sampled from the archives of the oral and maxillofacial radiology department of Guilan University of Medical Sciences using interval *3*, and stratified by FOV (either maxillary or mandibular). All eligible CBCT scans were compiled into a sampling frame and ordered chronologically by acquisition date. Considering interval *3*, every third CBCT scan was selected. To determine the starting point, a random integer between 1 and 3 was generated using a random number generator, and the corresponding scan was chosen as the first sample. Subsequently, scans numbered r, r + 3, r + 6, … were selected until the required sample size was achieved. The study was performed retrospectively on CBCT scans that were acquired between 2020 and 2024 for various dental and maxillofacial purposes, including evaluation of impacted teeth and implant sites. Thus, patients were not exposed to any additional radiation regarding this study. Furthermore, patients’ personal data remained confidential during all stages of the study. The scans belonged to patients aged 6 to 82 years. CBCT scans of poor quality or containing foreign bodies were excluded. Furthermore, scans showing evidence of prior surgery distorting regional anatomy and syndromic calcification disorders were excluded.

The sample size was determined based on a previous prevalence estimate of 25.9% for soft tissue calcifications. Using a confidence level of 95%, power of 95% and margin of error of 5%, the minimum required sample size was calculated to be 400 cases.

All scans had been acquired using a Pax-i 3D CBCT machine (Vatech, Korea) under standard conditions (95 kVp, 5.5 mA, voxel size 0.25 mm). The FOV was 120 × 90 mm targeted either at the maxilla or the mandible. Scans were reconstructed using Ez3D-i software (Vatech, Korea), and images were reviewed in axial, coronal, and sagittal sections for calcification detection.

Evaluations were performed independently by two experienced oral and maxillofacial radiologists using a high-resolution monitor (RadiForce MX241W, EIZO, Japan) under low ambient lighting. Both radiologists were calibrated before the study using a set of training images not included in the final sample. Image contrast and brightness were adjusted as needed. A stratified 25% subset was double‑rated to compute Cohen’s κ for dichotomous features and ICC for continuous measures; 95% CIs were obtained by bias‑corrected bootstrap. Disagreements were adjudicated by consensus for final calls.

Each CBCT scan was categorized into either the maxillary or mandibular FOV, and the following list was used for each group to identify potential calcifications (Figs. 1, 2 and 3):

Fig. 1.

Bilateral tonsilloliths in a CBCT with mandibular FOV. Measurements show medium size in the right side and large size in the left side. Tonsillolith presence and size may be linked to periodontal bone loss and dental pathologies as the microbial biofilm of these conditions are quite similar.

Fig. 2.

Calcinosis cutis of medium size in the right side related to a CBCT with maxillary FOV. These calcifications are usually related to tissue damage caused by inflammatory, neoplastic, or traumatic processes.

Fig. 3.

Bilateral calcified stylohyoid ligaments viewed in the axial and coronal sections of a CBCT with mandibular FOV. Measuring the length of these calcifications should be considered when symptomatic context suggests Eagle syndrome.

• -Maxillary FOV: tonsillolith, calcified stylohyoid ligament (CSL), sialolith, calcinosis cutis, phlebolith, antrolith, calcified lymph node.

• -Mandibular FOV: tonsillolith, calcified stylohyoid ligament (CSL), sialolith, calcified carotid atheroma (CCA), calcinosis cutis, phlebolith, calcified lymph node.

For potentially bilateral calcifications, their laterality (unilateral or bilateral) was also recorded. The maximum dimension of each calcification was measured using the software’s ruler tool, and calcifications were categorized by size as follows:

• -Small: ≤ 1 mm.

• -Medium: 1–3 mm.

• -Large: ≥ 3 mm.

For each patient, age, sex, type and location of calcifications, and calcification size were recorded. Data were analyzed using descriptive statistics (mean, standard deviation, frequency, and percentage). Inferential analyses were conducted using generalized estimating equations with binary and ordinal responses as well as binary and ordinal logistic models. The level of statistical significance was set at 0.05 for all tests.

Results

A total of 420 CBCTs belonging to subjects with the age range of 6–82 were included. The mean age of individuals in the mandibular FOV group was 47.77 ± 14.31 years, and in the maxillary FOV group, it was 41.14 ± 14.23 years.

Inter-observer reliability was almost perfect as Cohen’s κ was 0.904 for detection of the calcifications and ICC was 1 for the dimensions of the STCs.

Tonsilloliths were significantly more frequent in the mandibular FOV (OR = 2.07; p < 0.001), while CSL was more common in the maxillary FOV (OR = 0.57; p = 0.005).

Table 1 presents the frequency and size of soft tissue calcifications observed in CBCT images of both the maxilla and mandible. The most frequently detected calcification in both regions was tonsillolith, with 139 case (33.1%) in the mandibular FOV and 81 case (19.3%) in the maxillary FOV. The second most frequent calcification was the calcified stylohyoid ligament (CSL), with a frequency of 11% and 17.9% in the mandibular and maxillary FOV, respectively.

Table 1.

Frequency and size of soft tissue calcifications in CBCT images of the mandibular and maxillary FOV.

FOV	Calcification	Frequency N (%)	Size N (%)
Mandible			Small	Medium	Large
	Tonsillolith	139 (33.1)	5 (3.6)	102 (73.4)	32 (23)
	Calcified stylohyoid ligament	46 (11)	0 (0)	32 (69.6)	14 (30.4)
	Sialolith	5 (1.2)	0 (0)	4 (80)	1 (20)
	Calcified carotid atheroma	14 (3.3)	0 (0)	12 (85.7)	2 (14.3)
	Calcinosis cutis	21 (5)	2 (9.5)	19 (90.5)	0 (0)
	Phlebolith	1 (0.2)	0 (0)	0 (0)	1 (100)
	Calcified lymph node	8 (1.9)	0 (0)	7 (87.5)	1 (12.5)
Maxilla	Tonsillolith	81 (19.3)	4 (4.9)	70 (86.4)	7 (8.6)
	Calcified stylohyoid ligament	75 (17.9)	2 (2.7)	48 (64)	25 (33.3)
	Sialolith	2 (0.5)	0 (0)	2 (100)	0 (0)
	Antrolith	0 (0)	0 (0)	0 (0)	0 (0)
	Calcinosis cutis	25 (6)	1 (4)	24 (96)	0 (0)
	Phlebolith	0 (0)	0 (0)	0 (0)	0 (0)
	Calcified lymph node	0 (0)	0 (0)	0 (0)	0 (0)

For tonsilloliths, medium-sized calcifications (1–3 mm) were most common. 102 cases (73.4%) were found in the mandibular FOV and 70 cases (86.4%) were noticed in the maxillary FOV. Likewise, medium-sized CSLs were most prevalent, with 32 case (69.6%) in the mandibular FOV and 48 case (64%) in the maxillary FOV.

Table 2 summarizes the odds ratio (OR) of various calcifications based on gender and age for each FOV group. In the mandibular FOV, individuals over 50 had significantly higher odds of presenting tonsilloliths (OR = 2.24; p = 0.002) and carotid artery calcifications (OR = 16.78; p = 0.009). CSL was significantly more prevalent in females in the maxillary FOV group (p = 0.002). Calcinosis cutis in the maxillary FOV was significantly more frequent in males (p = 0.026). In other types of calcifications, due to low occurrence, statistical tests did not yield significant results (p > 0.05).

Table 2.

Summary of the results comparing calcifications of the maxillary and mandibular FOV by age and sex.

FOV	calcification			Odds ratio	p value
Mandible	Tonsillolith	Sex	F	1	0.063
			M	1.62
		Age	50 ≥	1	0.002
			50 <	2.24
	Calcified stylohyoid ligament	Sex	F	1	0.423
			M	1.43
		Age	50 ≥	1	0.376
			50 <	1.47
	Sialolith	Sex	F	1	0.532
			M	0.56
		Age	50 ≥	1	0.811
			50 <	0.8
	Calcified carotid atheroma	Sex	F	1	0.419
			M	0.53
		Age	50 ≥	1	0.009
			50 <	16.78
	Calcinosis cutis	Sex	F	1	0.180
			M	2.48
		Age	50 ≥	1	0.059
			50 <	3.59
	Calcified lymph node	Sex	F	1	0.318
			M	0.3
		Age	50 ≥	1	0.285
			50 <	3.57
Maxilla	Tonsillolith	Sex	F	1	0.651
			M	1.15
		Age	50 ≥	1	0.058
			50 <	1.84
	Calcified stylohyoid ligament	Sex	F	1	0.002
			M	0.26
		Age	50 ≥	1	0.179
			50 <	1.64
	Calcinosis cutis	Sex	F	1	0.026
			M	3.95
		Age	50 ≥	1	0.871
			50 <	0.91

The odds of observing large tonsilloliths in patients over 50 were 6.67 times greater than in those aged 50 or younger (p < 0.001). In the maxillary FOV, patients over 42 had 5.5 times greater odds of having large CSLs compared to younger individuals (p = 0.023). Age significantly affected both the occurrence and the size of calcifications in the maxillary and mandibular FOVs. In the maxillary FOV, the likelihood of calcification occurrence and large size increased in individuals over 42 (p = 0.017 and p = 0.007, respectively). In the mandibular FOV, these relations were even more pronounced (p < 0.001 and p = 0.006, respectively). Table 3 demonstrates age cut-off points for the maxillary and mandibular FOVs.

Table 3.

Comparison of the frequency of STCs in the maxillary and mandibular FOVs with regard to age*.

FOV	Age	Odds ratio (95% CI)	p value
Maxilla	≤ 42	1.59 (1.09–2.33)	0.017
	> 42
Mandible	≤ 50	1.99 (1.47–2.7)	< 0.001
	> 50

* Generalized estimating equations (GEE) with binary responses.

Overall, the odds of detecting soft tissue calcification were 1.3 times higher in the mandibular FOV (p = 0.01). Moreover, the probability of detecting large tonsilloliths was 2.54 times higher in the mandibular FOV. Patients over 45 had significantly higher chances of both having calcifications (OR = 1.51) and large calcifications (OR = 2.1).

Discussion

FOV‑specific analysis indicates that mandibular scans enhance detection of oropharyngeal and vascular‑adjacent STCs. Reliability was high, supporting routine incorporation into structured reporting. Our findings align with recent CBCT‑based staging of ECACs and opportunistic vascular assessment frameworks¹⁸, and complement contemporary data on stylohyoid complex variants and elongation prevalence^16,17,19,20.

STCs are mineral deposits often detected incidentally during routine radiographic examinations^21–23. While most STCs are asymptomatic and clinically insignificant, a subset can be associated with serious systemic conditions, emphasizing the importance of accurate diagnosis^24,25. Management decisions depend on the calcification’s location and size; some may require no intervention, while others necessitate further clinical evaluation or treatment^26–29.

Two-dimensional imaging modalities, such as panoramic radiography, present limitations in detecting STCs due to overlapping anatomical structures, distortion, and magnification artifacts. These limitations underscore the diagnostic advantage of cone-beam CT, which offers high-resolution, three-dimensional imaging^3,30.

The present study identified tonsilloliths as the most frequent calcification in both the maxillary and mandibular FOVs, followed by calcified stylohyoid ligaments (CSLs). These findings align with prior studies using CBCT and panoramic images by Missias et al.⁶, Yesilova & Bayrakdar⁹, and Schreiner et al.¹⁰, who also reported tonsilloliths as the most prevalent STC. However, some studies reported CSL as the most frequent calcification. These variations likely stem from differences in study populations, imaging systems, diagnostic criteria, and sample selection.

In terms of size, most calcifications in both FOVs were medium (1–3 mm). This contrasts with the findings by Khojastepour et al.³, who found most STCs to be smaller than 1 mm. Differences in voxel size, resolution, measuring technique, and FOV can influence these results.

A significant association between age and both the frequency and size of calcifications was noted. Larger tonsilloliths were significantly more prevalent in patients over 50 years, and large CSLs were more common in individuals over 42. These observations are consistent with the study by Missias et al.⁶, who also noted a higher prevalence of STCs in patients over 40. Some studies, however, did not find a statistically significant relationship between STC prevalence and age⁹.

Gender differences were also observed. In the maxillary FOV, CSLs were more frequent in females while calcinosis cutis was more common in males. Similar findings were reported by Takahashi et al.¹¹, who found tonsilloliths more prevalent in males and CSLs more common in females. Conversely, other studies reported no significant gender differences or even found a higher prevalence of STCs in females^26,28. Such discrepancies highlight the influence of demographic factors and study designs.

Notably, our study statistically compared STC frequency and size between the maxillary and mandibular FOVs. Tonsilloliths were significantly more frequent and larger in the mandibular FOV, while CSLs were more prevalent in the maxillary FOV. These differences may be explained by the anatomical coverage of each FOV since mandibular FOV better visualizes the tonsillar region, while maxillary FOV includes more of the stylohyoid ligament path.

Overall, the results emphasize the importance of evaluating all regions captured in CBCT scans, even if they extend beyond the primary area of interest. Age and gender are significant variables influencing the occurrence and characteristics of soft tissue calcifications.

Clinical implications:

We propose the following “CBCT incidental STC reporting checklist” to standardize review and referral:

1. Tonsilloliths — presence, laterality, estimated size.

2. Carotid territory calcifications — side/level (C3–C4), morphology; recommend medical evaluation as appropriate.

3. Stylohyoid complex — elongation/calcification; measure length when fully visualized.

4. Sialoliths/lymph node calcifications, where included in FOV.

Limitations of the current investigation of STCs include single‑center, indication‑driven sampling, lack of systemic medical histories, and retrospective design of the study. A multi‑center, prospective study with health record linkage could evaluate systemic associations and outcomes, and facilitate validation of a structured CBCT reporting template for STCs. Emerging AI approaches (e.g., for tonsillolith or vascular calcification detection) may further standardize triage.

Conclusion

This study concluded that the most common soft tissue calcifications in both maxillary and mandibular CBCT images are tonsilloliths and calcified stylohyoid ligaments, with medium-sized calcifications being the most prevalent. Overall, the likelihood of detecting calcifications was higher in the mandibular FOV. Age plays a significant role in both the frequency and size of the calcifications. In patients aged ≥ 50 years, mandibular CBCT scans should be routinely scrutinized for carotid artery calcifications, stylohyoid ligament calcifications, and tonsilloliths. FOV‑specific review and a structured checklist can improve detection and appropriate referral.

Author contributions

Maryam Latifi douabsari: Methodology, Visualization, Reviewing and editingSina Rahmanpanah: Methodology, Visualization, Reviewing and editingNegar Khosravifard: Conceptualization, Methodology, Original draft preparationSaba Kheirkhah: Methodology, Visualization, Reviewing and editingZahra Dalili Kajan: Methodology, Visualization, Reviewing and editingMohammad Ebrahim Ghaffari: Statistical analysis, Reviewing and editing.

Data availability

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

Declarations

Competing interests

The authors declare no competing interests.