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. 2025 Sep 9;18:44. doi: 10.1186/s13044-025-00261-8

Comparative analysis of three thyroglobulin immunoassays: analytical performance and clinical implications

Evelina La Civita 1, Mariano Fiorenza 1, Giuseppe Jannuzzi 1, Carmela Polito 1, Rosa Sirica 1, Gianluigi Carbone 1, Domenica Sorvillo 1, Aniello Saviano 1, Daniela Terracciano 1,✉
PMCID: PMC12418655  PMID: 40922020

Abstract

Objectives

Serum thyroglobulin (Tg) is a key biomarker in the post-surgical monitoring of differentiated thyroid cancer (DTC). However, inter-assay variability among different immunoassay platforms can impact clinical interpretation, particularly at low Tg concentrations. This study aimed to compare the analytical performance and concordance of three widely used Tg immunoassays, Access (Beckman Coulter, Tg-B), Atellica (Siemens, Tg-A), and Liaison (Diasorin, Tg-L), with a focus on their agreement across clinically relevant Tg ranges.

Methods

A total of 103 residual serum samples from subjects with or without thyroid pathology were analyzed using Tg-B, Tg-A, and Tg-L. Correlation analysis, Bland-Altman plots, and concordance rates were evaluated across three Tg concentration intervals: <2 ng/mL, 2–50 ng/mL, and > 50 ng/mL. Tg-B was used as the reference method for comparison.

Results

All three assays demonstrated strong overall correlations. Tg-L showed a very strong correlation with Tg-B (ρ = 0.89), with moderate agreement at Tg < 2 ng/mL. Tg-A also correlated well with Tg-B (ρ = 0.92), though agreement declined slightly at higher concentrations (> 50 ng/mL). The concordance rate for detecting undetectable Tg (< 0.2 ng/mL) was 96% for Tg-L and 98% for Tg-A when compared to Tg-B. Bland-Altman analysis revealed a significant negative bias for Tg-L versus Tg-B, while Tg-A and Tg-B showed no significant difference. A significant discrepancy was also observed between Tg-L and Tg-A.

Conclusions

Although the three Tg immunoassays demonstrated high correlation, notable differences emerged at lower and higher Tg levels, likely due to assay-specific antibody characteristics and calibrator variability. Our findings underscore the need for re-baselining when switching methods in longitudinal follow-up. However, the use of residual serum samples from a heterogeneous population, including individuals with and without thyroid pathology limits the direct clinical generalizability of the results and underscores the need for further validation in well-characterized post-thyroidectomy DTC cohorts.

Keywords: Thyroid cancer, Differentiated thyroid carcinoma, Thyroglobulin, Immunoassay comparison, Method validation, Clinical decision-making

Introduction

Differentiated thyroid cancer (DTC) accounts for over 90% of all thyroid malignancies, with their incidence rising in recent decades, primarily due to advancements in diagnostic techniques [1]. DTC is generally associated with a favorable prognosis, and the standard treatment includes surgical resection (either lobectomy or total thyroidectomy), often followed by radioactive iodine (RAI) ablation when indicated. To assess treatment effectiveness and recurrence risk, patient follow-up typically involves imaging studies and serum thyroglobulin (Tg) measurement as key surveillance tools [2–5].

Tg is a high-molecular-weight glycoprotein found in the thyroid colloid, serving as the precursor for thyroid hormone synthesis. In patients with DTC, Tg is the primary tumor marker for disease monitoring [6]. According to the American Thyroid Association (ATA) guidelines, patient responses to treatment are classified into three categories based on serum Tg levels during follow-up: excellent, indeterminate, and incomplete response [2]. Therefore, Tg measurement must be standardized and highly reliable. Only TgAb-negative specimens were included in this study to avoid the well-documented interference of anti-thyroglobulin autoantibodies, which can lead to falsely low Tg measurements and unreliable interpretation in sandwich immunoassays [2, 7].

Most clinical laboratories measure serum thyroglobulin (Tg) concentrations using automated immunoassays, which should be standardized against the CRM-457 international reference material [8] to minimize analytical variability. However, despite the introduction of the International Reference Preparation (IRP) MRC 65/93 for anti-thyroglobulin antibody (TgAb) quantification, significant inter-method variability persists, limiting the interchangeability of results across different assays [9]. This variability is likely attributed to the structural heterogeneity of Tg and differences in assay design. Given this high analytical variability, it is essential that longitudinal monitoring of both Tg and TgAb levels in DTC patients is performed consistently using the same analytical method throughout follow-up. Any change in the assay platform requires a comparative validation study to assess potential discrepancies between methods, ensuring continuity and accuracy in patient management.

Previous studies have compared various Tg immunoassays [10, 11], primarily assessing correlations and analytical biases between methods. However, these comparisons should extend beyond analytical differences to evaluate their clinical impact on DTC patient management, particularly in relation to the American Thyroid Association (ATA) classification system [2].

Therefore, this study aims to compare Tg measurements between three immunoassay platforms: the well-established Access (Beckman Coulter) and newly introduced Liaison (Diasorin) and Atellica (Siemens) Tg immunoassay. The goal is to provide an updated perspective on the clinical implications of assay-related discrepancies in Tg values, particularly in the follow-up of differentiated thyroid cancer (DTC). Special focus is placed on Tg concentrations below 1 ng/mL, a critical range for patient classification and risk stratification [2]. Although the analytical comparison of Tg immunoassays has been extensively investigated, this study offers a timely validation of three widely used, current-generation platforms. Specifically, we compare the performance of the Beckman Coulter Access Tg assay, a well-established method, with two newer automated systems: the Siemens Atellica IM and the DiaSorin Liaison TG II Gen. Considering the growing clinical adoption of these platforms, especially for longitudinal DTC monitoring, this work provides updated evidence on their analytical concordance and underscores the real-world impact of assay-specific variability on clinical decision-making.

Materials and methods

Serum samples

We selected 103 residual serum samples between April and May 2024. These subjects included 37 men and 66 women, and their mean age was 49 years (interquartile range = 60 − 38). Samples with hemolysis, icterus, or lipemia or from subjects with positive anti-thyroglobulin antibodies were excluded. Serum samples were stored at -80 °C to ensure stability until analysis.

Assays

Tg was determined by three different chemiluminescence immunoassays (CLIA). Specifically, these included Beckman UniCeL DxI800 (Tg-B) (Beckman Coulter Inc, Brea, CA, USA), Liaison (Tg-L) (Liaison XL, Diasorin, Saluggia, Italy) and Atellica™ IM Thyroglobulin (Tg-A) (Siemens Healthcare Diagnostics Inc., Glyn Rhonwy Llanberis, Gwynedd, United Kingdom).

The characteristics of the analytical methods used are summarized in Table 1. Testing was performed according to the manufacturer’s instructions. The quality control from Bio-Rad system (Bio-Rad Laboratories Inc, Irvine, CA, USA) was used. Analytical measurement ranges (AMR) of Tg were from 0.1 to 500 ng/mL for Tg-B, from 0.050 to 150 ng/mL for Tg-A and from 0.1 to 500 ng/mL for Tg-L. All three assays are standardized against CRM-457.

Table 1.

Analytical method characteristics

Analyzer Beckman Coulter Siemens Diasorin
Kit Access Thyroglobulin Atellica IM Thyroglobulin LIAISON Tg II Gen
Measuring range 0.1–500 ng/mL 0.050–150 ng/mL 0.1–500 ng/mL
Absence of Hook effect until: (as noticed in the insert kit) 40.000 ng/mL 80.000 ng/mL 250.000 ng/mL
Test principle Sandwich immunoassay in one step Sandwich immunoassay in one step Sandwich immunoassay in one step
Normal range 1.4–78 ng/ml 0.2–55 ng/ml 0–55 ng/ml
Functional sensitivity 0.1 ng/mL 0.05 ng/mL 0.1 ng/mL
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Methods comparison and statistical analysis

We compared serum Tg-B and serum Tg-A, serum Tg-B and serum Tg-L and serum Tg-L and serum Tg-A using 103 samples. SerumTg-B was selected as reference methods for statistical analysis purposes. Tg-B was selected as the reference method due to its widespread clinical adoption, long-standing analytical performance, and historical use as the standard assay in our laboratory’s routine monitoring of differentiated thyroid cancer patients. For comparison of different methods, we calculated the Spearman coefficient r (ρ), using three different assays. To assess the statistical significance between the differences among each correlation we used Fischer test. In addition, we used the Bland Altman methods to evaluate the agreement between the measurements.

Results

Concordance between Tg-B with Tg-L and Tg-A

The overall concordance rate between Tg-B and Tg-L in discriminating negative values (< 0.2 ng/mL) was 96%, (47/49 samples were negative in both assays) and the overall concordance rate in discriminating negative value between Tg-B and TG-A was 98% (48/49 samples were negative in both assays). All patients with discrepant Tg results displayed negative TgAb values.

Comparison of Tg-B and Tg-L

As shown in Fig. 1A a significant very strong correlation between Tg-B and Tg-L assays was found for samples in the overall population (ρ = 0.89, p value < 0.0001). To further understand the accordance of Tg-L with the reference method, we analyzed the correlation between Tg-B and Tg-L stratifying values according to different range of clinical interest. As showed in Fig. 1B, Tg-L displayed a moderate correlation with reference method for values between 0 ng/mL and 2 ng/mL (ρ = 0.42, p value = 0.0010), whereas very strong correlations for values between 2 ng/mL and 50 ng/mL (ρ = 0.97, p value < 0.0001) (Fig. 1C) and for values higher that 50 ng/mL (ρ = 0.90, p value < 0.0001) (Fig. 1D) were found.

Fig. 1.

Fig. 1

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Correlation between Tg-B (Beckman Coulter) and Tg-L (Diasorin) assays across clinically relevant Tg intervals. (A) Correlation across all samples (n = 103); (B) Correlation for Tg values between 0–2 ng/mL; (C) Correlation for Tg values between 2–50 ng/mL; (D) Correlation for Tg values > 50 ng/mL. Spearman’s correlation coefficients (ρ) and p-values are indicated. The analysis highlights assay agreement at different Tg levels

Comparison of Tg-B and Tg-A

As shown in Fig. 2A, a very strong correlation between Tg-B and Tg-A assays was found for samples in the overall population (ρ = 0.92, p value < 0.0001). To further understand the accordance of Tg-A with the reference method, we analyzed the correlation between Tg-B and Tg-A stratifying values according to different range of clinical interest. Tg-A displayed a moderate correlation with reference method for values between 0 ng/mL and 2 ng/mL (ρ = 0.53, p value < 0.0001) (Fig. 2B), a very strong correlation for values between 2 ng/mL and 50 ng/mL (ρ = 0.96, p value < 0.0001) (Fig. 2C) and a strong correlation for values higher than 50 ng/mL (ρ = 0.74, p value = 0.0003) (Fig. 2D). Noteworthy, the correlation between Tg-B and Tg-L was significantly higher compared to the correlation between Tg-B and Tg-A for values higher than 50 ng/mL (ρ 0.89 vs. 0.74, p value = 0.004).

Fig. 2.

Fig. 2

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Correlation between Tg-B (Beckman Coulter) and Tg-A (Siemens Atellica) assays across clinically relevant Tg intervals. (A) Correlation across all samples (n = 103); (B) Correlation for Tg values between 0–2 ng/mL; (C) Correlation for Tg values between 2–50 ng/mL; (D) Correlation for Tg values > 50 ng/mL. This figure illustrates the strength of correlation between the two assays, with stratified analysis by Tg concentration ranges

Comparison of Tg-L and Tg-A

As shown in Fig. 3A a very strong correlation between Tg-L and Tg-A assays was found for samples in the overall population (ρ = 0.92, p value < 0.0001). In addition, we analyzed the correlation between Tg-L and Tg-A stratifying values according to different range of clinical interests. As showed in Fig. 3B, C and D, Tg-A displayed a strong correlation with Tg-L for values between 0 ng/mL and 2 ng/mL (ρ = 0.60, p value < 0.0001), a very strong correlation for values between 2 ng/mL and 50 ng/mL (ρ = 0.98, p value < 0.0001) and a moderate correlation for values higher that 50 ng/mL (ρ = 0.53, p value = 0.0266).

Fig. 3.

Fig. 3

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Correlation between Tg-L (Diasorin) and Tg-A (Siemens Atellica) assays across clinically relevant Tg intervals. (A) Correlation across all samples (n = 103); (B) Correlation for Tg values between 0–2 ng/mL; (C) Correlation for Tg values between 2–50 ng/mL; (D) Correlation for Tg values > 50 ng/mL. Data show strong to moderate agreement depending on Tg levels, particularly at the extremes of the measurement range

Comparability of Tg-L and Tg-A with the well-established Tg-B

Bland-Altman plot showed a significant difference between Tg-L measurement compared to the reference method, Tg-B (Fig. 4A). Tg-L and Tg-B displayed a mean bias of -36.110 ng/mL (95% CI -58.56 to -13.66 p value = 0.0019), the regression equation was Y = 0.6821x – 4.7449 and 95% CIs for slope and Y-intercept were 1.858 to 2.051 and − 14.86 to 7.198, respectively. Bland-Altman plot showed no significant difference between Tg-A and Tg-B. In particular, the mean bias was 2.074 ng/mL (95% CI -5.983 to 10.13 p value = 0.6108), the regression equation was Y = -0.1191x + 2.7859 and 95% CIs for slope and Y-intercept were 0.7560 to 0.8910 and − 2.418 to 13.04 respectively.

Fig. 4.

Fig. 4

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(A) Bland-Altman plot for Tg-L versus Tg-B shows a significant negative bias; (B) Bland-Altman plot for Tg-A versus Tg-B reveals no significant bias. Each point represents an individual sample. The mean bias and 95% limits of agreement are indicated

Comparability of Tg-L and Tg-A

Fig. 5.

Fig. 5

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Bland-Altman plot comparing Tg-L (Diasorin) and Tg-A (Siemens Atellica) assays. This plot shows a significant negative bias for Tg-L compared to Tg-A. The mean difference and 95% confidence intervals are displayed. The results highlight potential discrepancies when switching between these two methods

Bland-Altman plots showed a significant difference between Tg-L and Tg-A (Fig. 5). The comparison between these two assays displayed a mean bias of -38.183 ng/mL (95% CI -65.53 to -10.84 p value = 0.0067). The regression equation was Y= -0.8158x + 9.8291 and the 95% CIs for slope and Y-intercept were 1.765 to 2.195 and − 22.92 to 21.38 respectively.

Discussion

The decision to change an analytical method in clinical laboratories is particularly critical when dealing with tumor markers like thyroglobulin (Tg) working as indicators of surgical radicality [12]. Immunoassays face technical challenges, including variability in antibody quality and interference from endogenous antibodies. For instance, monitoring elevated thyroglobulin (Tg) levels via immunometric assays is essential in the follow-up of patients with differentiated thyroid cancer (DTC) to detect residual or recurrent disease [13, 14] .

In recent years, the development of second-generation immunometric assays (Tg-IMAs) and mass spectrometry-based methods has significantly improved the sensitivity and specificity of Tg detection [15, 16].

While LC-MS/MS offers enhanced specificity [17, 18], its application is still limited by cost, technical complexity, and lack of standardization across laboratories [19].

Notably, newer Tg-IMAs demonstrate functional sensitivity below 0.1 ng/mL, enabling the detection of low levels of Tg in patients under TSH suppression therapy [20].

While exploring alternative methods is essential, a thorough evaluation of its impact on patient management must be faced. Inter-assay variability among commercial immunoassays persists despite standardization efforts, such as the use of CRM-457 as a reference material [10]. Studies have demonstrated significant discrepancies in Tg values across different platforms, which may lead to misinterpretation of longitudinal trends in patient monitoring [21, 22].

Our comparative analysis of the Access (Beckman Coulter), Liaison (Diasorin), and Atellica (Siemens) immunoassays demonstrated strong overall concordance across methods, with some variability at different thyroglobulin (Tg) concentration ranges.

Tg-L demonstrated a very strong overall correlation with Tg-B, with moderate correlation at low Tg levels (0–2 ng/mL) and very strong correlations for both 2–50 ng/mL and > 50 ng/mL.

Tg-A showed a very strong overall correlation with Tg-B with moderate correlation at low Tg levels (0–2 ng/mL), very strong correlation for 2–50 ng/mL and strong correlation for > 50 ng/mL. Notably, Tg-B showed a high concordance in detecting negative Tg values (< 0.2 ng/mL) with Tg-L (96%) and Tg-A (98%), with all discrepant cases occurring in TgAb-negative patients.

Despite a slight analytical bias at low Tg concentrations (0–2 ng/mL), the three methods showed strong concordance.

Among the samples falling within the critical range of 0–2 ng/mL, a total of six samples exhibited discordant results across the three Tg assays, with values falling below the functional sensitivity threshold of 0.2 ng/mL in one assay and detectable in another. All these samples were from TgAb-negative patients, and none showed signs of heterophile antibody interference, based on standard laboratory flags and consistent analyte behavior. However, it is important to note that no heterophile-blocking agents were specifically used, which represents a limitation of the study.

Clinical follow-up data were available for four of the discordant cases. All patients had undergone total thyroidectomy for differentiated thyroid carcinoma and were under regular surveillance with no evidence of structural disease at the time of sampling. Importantly, none of these minor discrepancies in Tg levels led to changes in therapeutic management, as Tg values were still interpreted within the “excellent response” category according to ATA guidelines.

Additionally, we assessed whether changing the analytical cutoff from 0.2 ng/mL to 0.1 ng/mL would affect agreement rates. While this adjustment resulted in a slight increase in discordant classifications, the overall concordance between Tg-A and the reference method Tg-B remained high, and the impact on classification of patient response categories was minimal.

Moreover, the Bland-Altman analysis revealed a significant negative bias for Tg-L relative to Tg-B, indicating that Tg-L consistently reports lower values across the measured range. This systematic difference may be attributed to several analytical factors. First, although all three assays are standardized against CRM-457, differences in calibrator formulation, matrix composition, and traceability procedures can lead to divergence in quantification. Additionally, variability in capture and detection antibodies, particularly in their affinity for different Tg isoforms or glycoforms, may contribute to inter-assay discrepancies. Tg-L may be more selective for certain molecular forms of thyroglobulin or less reactive to circulating fragments, resulting in lower measured concentrations. Differences in signal detection technologies and epitope recognition could further exacerbate this underestimation. Understanding these assay-specific biases is critical when interpreting longitudinal Tg trends, especially if a change in assay platform occurs during patient follow-up.

The results highlight the need for re-baselining when switching between the three immunoassays, rather than supporting a general feasibility of transitioning across assays.

It is important to note that the serum samples analyzed in this study were not exclusively obtained from patients with confirmed thyroid cancer, but rather from a heterogeneous population with and without thyroid pathology. This constitutes a limitation of the present work and may partially influence the generalizability of the results in a strictly oncological setting. Furthermore, due to the inherent molecular heterogeneity of thyroglobulin and the variability in antibody design across different immunoassay platforms, we strongly recommend re-baselining Tg values when switching assay methods in longitudinal patient monitoring. This step is essential to ensure accurate trend interpretation and consistent clinical decision-making over time. Although the assays are highly correlated, differences between methods must be taken into account when transitioning between platforms, particularly for consistent longitudinal patient follow-up

Moreover, we suggest that Tg-A results between 0.06 and 0.2 µg/L should be accounted for ‘grey zone’ values and these subjects should undergo a stimulation test until long-term outcomes can be clarified through extended prospective studies to minimize clinical and therapeutic uncertainty and reduce potential anxiety among patients with DTC.

Despite the use of certified reference materials, discrepancies among different immunoassays are well documented. These differences are most likely attributable to variations in the source and composition of kit calibrators, the intrinsic heterogeneity of the analyte, and the use of distinct antibody sets in each assay, which differ in their specificity toward various Tg isoforms [13, 23].

There are currently no universally accepted Tg cut-off values for assessing disease status in DTC. This is due to inter-assay variability, differences in assay sensitivity, and individual patient factors. As a result, monitoring longitudinal trends in Tg levels using the same assay, ideally under stable TSH conditions, provides more reliable clinical information than relying on single absolute values. Consistency in the assay platform is essential to accurately detect subtle changes that may indicate disease recurrence or progression [23, 24].

In our study anti-thyroglobulin antibody (TgAb) status was assessed using a single immunoassay method. We acknowledge this as a limitation, since confirming TgAb negativity with two independent methods would have provided stronger evidence for the absence of interference. Nonetheless, all patients included in the study tested negative for TgAb, and TgAb negativity was consistently observed across all samples, including those showing discrepant Tg values. Additional studies involving larger patient cohorts and subjects with positive anti-thyroglobulin antibodies are warranted to confirm these findings and to better define the utility of Tg-A in clinical decision-making, especially when used to monitor patients after thyroidectomy for biochemical recurrence.

In addition, the analytical measurement range (AMR) represents a crucial parameter for assay suitability in routine follow-up [25]. The Atellica assay (Tg-A) demonstrated the narrowest AMR among the three methods tested, spanning from 0.050 to 150 ng/mL, compared to the broader range of 0.1 to 500 ng/mL reported for both Tg-B and Tg-L. The relatively limited upper range of Tg-A may necessitate frequent manual or automated dilutions in patients with residual thyroid tissue or biochemical recurrence. This limitation could affect laboratory workflow and turnaround time, especially in settings managing large volumes of thyroid cancer patients. Therefore, both the risk of hook effect at high concentrations and the constraints of AMR must be taken into account when selecting an assay platform and interpreting longitudinal trends in Tg monitoring.

A major limitation of this study lies in the composition of the sample cohort, which includes residual serum samples from a heterogeneous population of individuals, both with and without thyroid pathology. While this reflects the variety of Tg levels encountered in routine clinical practice and supports analytical comparisons across a broad dynamic range, it limits the clinical specificity of the findings for DTC surveillance. Ideally, assay comparisons should be conducted in a cohort of post-thyroidectomy DTC patients under TSH-suppressed or stimulated conditions, where precise Tg quantification has direct implications for disease monitoring and therapeutic decision-making. Therefore, while the present results provide important analytical insights, further studies in targeted DTC populations are needed to confirm their applicability in a clinical follow-up context. Nevertheless, this comparative analysis highlights the critical need for standardization in Tg measurement and for awareness of assay-specific biases to support accurate risk stratification and effective long-term management of patients with DTC [26].

In conclusion, although limited follow-up data suggest a minimal clinical impact in a subset of cases, further studies are required to validate these findings in a larger DTC cohort.

Meanwhile, a personalized and closely monitored follow-up strategy, as proposed in this study, is recommended.

Acknowledgements

Not applicable.

Author contributions

ELC: Conceptualization, Methodology, Data Curation, Formal analysis. RS, M.F, CP, AS, DS, GJ, GC: Investigation, Data curation, Visualization. DT: Writing - Original Draft, Conceptualization, Funding acquisition, Supervision, Writing - Review and Editing.

Funding

This study did not receive any funding.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Serum samples from the study population were retrospectively selected from stored residual samples. These samples were obtained from subjects who had provided informed consent for the collection, storage and reuse of their samples.

Consent for publication

All authors approved the final version of the manuscript and consented to its publication in its actual form.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

No datasets were generated or analysed during the current study.

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