Development and validation of a nomogram for the non-invasive prediction of human epidermal growth factor receptor 2 expression in gastric adenocarcinoma: an ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography-based study

Introduction

Gastric cancer (GC) ranks as the fifth most common malignancy and the fifth leading cause of cancer-related mortality worldwide, representing a significant public health challenge (1). It is often diagnosed at an advanced stage, wherein patients are often ineligible for curative surgery and face a poor prognosis, making them reliant on systemic therapies (2). Human epidermal growth factor receptor 2 (HER2) is a critical therapeutic target in the treatment of advanced GC. Previous studies have shown that HER2 is not only a key driver of tumorigenesis and progression in GC but also an independent prognostic factor (3). The recent advent of antibody-drug conjugates (ADCs) has marked a substantial advance in personalized medicine, offering new therapeutic avenues for patients with advanced HER2-positive GC (4,5). This progress underscores the imperative for the accurate assessment of HER2 status, which is fundamental for guiding treatment selection and optimizing outcomes in advanced disease.

In clinical practice, HER2 expression status in GC is primarily assessed through immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) on biopsy or surgical specimens (6). However, HER2 expression may undergo dynamic changes during the course of treatment (7). As invasive procedures carry inherent risks such as bleeding and infection, they are unsuitable for repeated, real-time monitoring (8). Beyond these risks, endoscopic biopsy also faces challenges related to intratumoral heterogeneity and sampling error. In such challenging scenarios, a non-invasive imaging tool that provides a global assessment of the entire tumor might theoretically offer complementary information (for instance, by guiding biopsy targeting, providing supportive evidence in equivocal cases, or serving as a reference when re-biopsy is not feasible). ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) is a molecular imaging technique that integrates anatomical and metabolic function information. By quantifying glucose metabolic activity in lesions, it reveals the pathophysiological characteristics of diseases non-invasively. It is now widely used for diagnosing, staging, assessing treatment response, and predicting prognosis in various malignancies (9,10). Moreover, PET/CT has shown considerable potential in predicting molecular phenotypes in oncology, such as epidermal growth factor receptor (EGFR) mutation status in lung cancer and Kirsten rat sarcoma viral oncogene homolog (KRAS) status in colorectal cancer (11,12).

Whether PET/CT metabolic parameters can predict HER2 expression in GC remains controversial. Several studies have suggested a potential correlation between HER2 status and metabolic parameters like the maximum standardized uptake value (SUV_max) (13-15), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) (16). In contrast, CELLI et al. reported no significant difference in glucose metabolism between HER2-positive and HER2-negative GCs (17). These conflicting findings may stem from regional and institutional variations in patient demographics, detection methodologies, and sample sizes. To address this, our study strictly adhered to 2016 Guidelines for HER2 Detection in Gastric Cancer to determine HER2 status and rigorously screen eligible participants. This study primarily aimed to investigate the association between PET metabolic parameters and HER2 expression in gastric adenocarcinoma. As a secondary objective, we explored the feasibility of constructing a nomogram as an exploratory predictive tool. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0345/rc).

Methods

Research object

This retrospective observational study enrolled patients with GC who underwent preoperative ¹⁸F-FDG PET/CT at The Third Affiliated Hospital of Soochow University between October 2011 and June 2024. The inclusion criteria were: (I) histopathological confirmation of gastric adenocarcinoma; (II) an ¹⁸F-FDG PET/CT scan performed within one month before surgery; (III) HER2 expression status determined by IHC on surgical specimens, supplemented by FISH when indicated; and (IV) complete clinical data. The exclusion criteria included: (I) prior neoadjuvant therapy; (II) poor visualized lesions on PET/CT; and (III) presence of other primary malignancies.

A total of 231 GC patients were ultimately enrolled based on the inclusion and exclusion criteria. Preoperative baseline clinical data, including sex, age, body mass index (BMI) and serum levels of tumor markers [carbohydrate antigen 199 (CA199), carbohydrate antigen 724 (CA724), carcinoembryonic antigen (CEA)] were retrospectively collected. Tumor-related characteristics, such as tumor-node-metastasis (TNM) stage, maximum tumor diameter, lymph node metastasis status, tumor location, and histological grade of differentiation, were obtained from postoperative pathological reports. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of The Third Affiliated Hospital of Soochow University [Approval No. 2025(education)CL026-01] and individual consent for this retrospective analysis was waived. The study flowchart is summarized in Figure 1.

Figure 1 Study flowchart for predicting HER2 status in gastric adenocarcinoma. GC, gastric cancer; HER2, human epidermal growth factor receptor 2; PET/CT, positron emission tomography/computed tomography.

Assessment of HER2 expression status

HER2 expression status was evaluated for all patients using surgically resected specimens. Testing was conducted in compliance with the Guidelines for HER2 Detection in Gastric Cancer [2016] (6). The testing process was as follows: IHC analysis was performed initially. Based on IHC scoring criteria, results were stratified as negative (scores 0 or 1+), equivocal (score 2+), or positive (score 3+). Specimens with equivocal (score 2+) results subsequently underwent FISH for verification. Cases demonstrating gene amplification on FISH were designated as HER2-positive, while those without amplification were confirmed as HER2-negative.

PET/CT imaging acquisition

All patients underwent ¹⁸F-FDG PET/CT imaging within one month before surgery using Siemens Biograph mCT 64 PET/CT scanner (Siemens Medical Solutions, Ann Arbor, MI, USA). The radiopharmaceutical (¹⁸F-FDG, radiochemical purity >95%) was supplied by Nanjing Jiangyuan Andico Positron Research and Development Company. Following a minimum 6-hour fast and confirmation of blood glucose levels ≤11.1 mmol/L, patients received an intravenous injection of 3.70–5.55 MBq/kg of ¹⁸F-FDG. Patients consumed ≥500 mL of water to distend the gastric lumen prior to PET/CT scanning. After a 60-minute uptake period in a quiet environment, low-dose CT for attenuation correction and anatomical localization was acquired with CareDose 4D technology (tube current was automatically adjusted during CT scan according to the body shape, anatomical structure, and tissue density), with tube voltage 100 kV, screw pitch 0.8, bulb tube single layer rotation time 0.5 s, and layer thickness 5 mm. PET acquisition was performed from the skull vertex to the upper thighs (2 minutes/bed). Image reconstruction was performed using Ultra HD algorithm [point spread function (PSF) + time-of-flight (TOF), 2 iterations, 21 subsets]. Finally, PET and CT images were co-registered and fused on a Siemens TrueD workstation to generate reconstructed transverse, sagittal, coronal tomographic images (18).

PET image analysis

PET image analysis was conducted by two nuclear medicine physicians who were blinded to the clinical and HER2 status of the patients, using LIFEx software (version 7.7.0). Any discrepancies in assessment were resolved by a subsequent consensus reading. The volume of interest (VOI) for the primary gastric tumor was semi-automatically delineated using a fixed threshold method (40% SUV_max), followed by manual refinement in multiple planes. Subsequently, the following metabolic parameters were automatically calculated: SUV_max, mean standardized uptake value (SUV_mean), MTV, and TLG.

Statistical analysis

All statistical analyses were conducted using IBM SPSS Statistics (version 27.0.1) and R software (version 4.4.3). The overall proportion of missing data was very low. Therefore, for the continuous variables with missing values, imputation was performed using the median. The normality of continuous variables was evaluated using the Kolmogorov-Smirnov test. Normally distributed data are reported as mean ± standard deviation, and non-normally distributed data as median (interquartile range). Group comparisons were conducted using Student’s t-test or the Mann-Whitney U-test. Categorical variables are presented as frequencies (percentages), compared using the χ² test or Fisher’s exact test appropriate. Univariate logistic regression analysis was first performed to screen for potential predictors of HER2-positive status. Variables with P<0.10 were subsequently entered into a multivariate logistic regression model to identify independent predictors. A predictive model was subsequently constructed based on the multivariate results and visualized as a nomogram. Model discrimination was evaluated by generating receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Internal validation was carried out via bootstrapping to assess model robustness. Calibration curves were used to examine the agreement between predicted and observed outcomes, and decision curve analysis (DCA) was applied to evaluate the clinical net benefit of the nomogram.

Results

Baseline characteristics of GC patients

As presented in Table 1, among the 231 patients with gastric adenocarcinoma, 30 (13.0%) were HER2-positive and 201 (87.0%) were HER2-negative. Compared with the HER2-negative group, the HER2-positive group had significantly higher proportions of cardia cancer (63.3% vs. 37.3%; P=0.007) and well or moderate-differentiated tumors (36.7% vs. 23.4%; P=0.047).

Correlation between HER2 expression and PET metabolic parameters with subsequent subgroup analysis

As shown in Table 1, HER2-positive GCs demonstrated elevated values of SUV_max, SUV_mean and TLG compared to HER2-negative tumors. ROC curve analysis established an optimal SUV_max cutoff of 19.22 (AUC =0.584, sensitivity =33.3%, specificity =87.1%). Using this threshold, patients were stratified into four subgroups based on HER2 status and dichotomized SUV_max. A significantly higher proportion of patients with SUV_max ≥19.22 was found in the HER2-positive group (10/30, Group 4) than in the HER2-negative group (26/201, Group 2) (χ²=8.26, P=0.004) (Table 2). Analysis of clinical characteristics across these subgroups revealed significant differences in tumor longitudinal diameter, histological differentiation, and primary tumor location (all P<0.10, Table 3). Notably, patients with SUV_max ≥19.22 had larger tumors irrespective of HER2 status. Furthermore, the HER2-positive/low-SUV_max subgroup (Group 3) exhibited a distinctive clinicopathological feature: a higher prevalence of well-differentiated tumors.

Univariate and multivariate analyses of factors influencing HER2 expression status in gastric adenocarcinoma patients

Univariate logistic regression analysis was performed with HER2 positivity as the dependent variable and the PET metabolic parameters of the primary tumor, along with clinical characteristics, as independent variables. The results indicated that tumor location, tumor differentiation, lymph node metastasis status, SUV_mean and SUV_max show a trend toward association with HER2 status (P<0.10). Specifically, tumors located in the gastric cardia were associated with a significantly higher likelihood of HER2 positivity compared to non-cardia tumors [odds ratio (OR) =2.90, 95% confidence interval (CI): 1.31–6.43, P=0.009]. Moderate or Well-differentiated adenocarcinoma was associated with increased odds of HER2 positivity relative to poorly differentiated tumors (OR =2.26, 95% CI: 1.00–5.12, P=0.051). Conversely, HER2-positive GC patients tended to have a lower likelihood of lymph node metastasis (OR =0.47, 95% CI: 0.22–1.05, P=0.06). Furthermore, a higher SUV_max (OR =1.04, 95% CI: 1.00–1.08, P=0.07) and SUV_mean (OR =1.07, 95% CI: 1.00–1.15, P=0.08) value of primary tumors were positively correlated with HER2-positive status (Table 4).

Variables with P<0.10 in univariate analysis were subsequently considered for inclusion in the multivariate logistic regression model. Due to multicollinearity between SUV_max and SUV_mean [variance inflation factor (VIF) >10], a backward stepwise regression approach was employed to construct the final model. The multivariate analysis confirmed SUV_max, tumor differentiation, and tumor location as independent predictors of HER2-positive expression, as detailed in Table 5.

Exploratory nomogram development and preliminary performance evaluation

The individual predictive performance for HER2 expression, assessed by the AUC, was 0.584 for SUV_max, 0.581 for degree of differentiation, and 0.630 for tumor location. As a secondary exploratory analysis, a combined predictive model integrating these three variables was developed and presented as a nomogram (Figure 2). The logistic regression formula is as follows:

logit(p)=1.14×location(cardia=1)+0.90×differentiation(wellormoderate=1)+0.046×SUVmax−3.325[1]

Figure 2 The nomogram constructed based on the coefficients of multiple logistic regression. HER2, human epidermal growth factor receptor 2; SUV_max, maximum standardized uptake value.

The integrated model achieves an AUC of 0.710 (95% CI: 0.605–0.816) and outperforms any single predictor, suggesting some potential for discrimination (Figure 3). Bootstrap internal validation confirmed the model’s robustness, yielding a corrected concordance index (C-index) of 0.686, which was slightly lower than the apparent performance. The calibration curve indicated excellent agreement between predictions and observations, supported by a Brier score of 0.106 (Figure 4A). DCA further revealed that within the fixed threshold range, the model provided greater clinical net benefit than both the “treat-all” and “treat-none” strategies (Figure 4B). A representative case illustrating the clinical application of the nomogram for predicting HER2 status is shown in Figure 5.

Figure 3 ROC curves of various models for predicting HER2 positive. HER2, human epidermal growth factor receptor 2; ROC, receiver operating characteristic; SUV_max, maximum standardized uptake value.

Figure 4 Performance of the nomogram for HER2 prediction in gastric adenocarcinoma patients. (A) Calibration curve of the nomogram. (B) Decision curve of the nomogram. HER2, human epidermal growth factor receptor 2.

Figure 5 Representative case: an 85-year-old male with gastric cancer. (A,B) The CT and PET/CT images demonstrate focal wall thickening at the gastric cardia with abnormally elevated FDG uptake (SUV_max =20.68). (C,D) IHC analysis confirms HER2 positivity (score 3+). (E) According to the nomogram, the prediction probability of HER2-positive is greater than 40.0%, consistent with actual result. CT, computed tomography; FDG, fluorodeoxyglucose; HER2, human epidermal growth factor receptor 2; IHC, immunohistochemistry; PET, positron emission tomography; SUV_max, maximum standardized uptake value.

Discussion

We observed a notable correlation between ¹⁸F-FDG PET/CT metabolic parameters and HER2 expression in gastric adenocarcinoma, and further confirmed that tumor location and histological grade were independently associated with HER2 expression. As an exploratory extension, we developed and validated a nomogram integrating PET parameter with key clinical characteristics. However, its discriminative performance was moderate (AUC 0.710), positioning it as a hypothesis-generating tool rather than a clinically ready instrument.

To ensure the robustness of our findings, we intentionally adopted surgically resected specimens as the reference standard for HER2 assessment, as they provide comprehensive tumor sampling and effectively mitigate the heterogeneity-related inaccuracies inherent to endoscopic biopsy. Furthermore, all enrolled patients underwent upfront surgery without prior neoadjuvant therapy, ensuring that HER2 expression reflected the native tumor biology rather than therapy-induced alterations. These methodological safeguards collectively strengthen the reliability of the observed associations and provide a solid foundation for the exploratory predictive model presented herein.

In this study, the HER2 positivity rate in gastric adenocarcinoma was 13.0%, which differs from the findings reported in certain international studies (18). This discrepancy may be attributed to the considerable geographical variation in the epidemiological and pathological characteristics of GC. In Europe and America, although the overall incidence of GC is relatively low, the proportion of HER2-positive cases tends to be higher (19). Furthermore, HER2 expression is strongly associated with the primary site of GC. As early as 2005, Tanner et al. reported a higher HER2 positivity rate in gastroesophageal junction (GEJ) cancers, which aligns with our results (20). The cardia region has a distinct embryological origin and molecular microenvironment, contributing to differences in the etiology and pathogenesis of GEJ cancers compared to distal GCs. These tumors are more frequently of the intestinal type, which is more susceptible to regulation by the HER2 signaling pathway during cellular proliferation and differentiation, thereby influencing HER2 expression levels. Additionally, this study also found that well-differentiated gastric carcinomas exhibited a higher HER2 positivity rate than poorly differentiated tumors. This can be explained by the histologic predominance of the diffuse type (including signet ring cell carcinoma) in poorly differentiated cancers, versus the intestinal phenotype in well-differentiated ones. This pattern suggests that HER2 gene amplification and protein overexpression may be selectively associated with specific histological phenotypes.

Compared with HER2-negative patients, HER2-positive individuals exhibited generally higher metabolic parameters of the primary tumor on PET imaging. We hypothesize that HER2 signaling may drive metabolic reprogramming and enhance proliferative activity, leading to increased glucose uptake (reflected as higher SUV_max). To further explore heterogeneity in clinical characteristics across different populations, the study conducted a subgroup analysis of all patients based on SUV_max and HER2 expression levels. The results revealed that even within the HER2-positive cohort, there were differences in the distribution of clinical pathological features. Specifically, the HER2-positive/low-SUV_max subgroup was associated with a higher proportion of well or moderate-differentiated tumors, suggesting that patients in this subgroup may have a relatively favourable prognosis and biological behaviour. However, due to limited sample sizes in certain subgroups, the current statistical power remains inadequate. Therefore, future studies will aim to expand the cohort size and conduct in-depth assessments of prognostic differences among subgroups, with the goal of refining risk stratification and informing individualized treatment strategies for HER2-positive GC.

For the non-invasive assessment of HER2 expression, a variety of molecular probes specifically targeting this receptor have been developed, such as ⁶⁸Ga-ABY-025 and ^99mTc-ADAPT6 (21,22). However, most of these tracers remain in the preclinical or early-phase clinical development (phase I–II) stages and require long-term systematic validation before routine clinical implementation. Therefore, using existing, widely applied imaging techniques to predict HER2 expression status is more feasible. Against this backdrop, CT and magnetic resonance imaging (MRI) are often employed in exploratory research in this field due to their high operational safety and strong clinical accessibility. For example, Zhao et al. developed and validated a dual-energy CT (DECT)-based nomogram for non-invasive prediction of HER2 expression in GC, achieving AUCs of 0.807 and 0.815 in the training and validation cohorts, respectively (23). Nevertheless, CT and MRI have significant limitations, including limited detection ability for small or diffuse lesions, over-reliance on morphological features, and susceptibility of diagnostic results to the operator’s subjective experience.

Previous studies utilizing conventional ¹⁸F-FDG PET/CT for predicting HER2 expression have primarily focused on analyzing the correlation between metabolic parameters and HER2 status, yet no consistent or definitive conclusions have been established (13,15). In contrast, the predictive model developed in this study enables direct quantification of the probability of HER2 positivity in patients with gastric adenocarcinoma and demonstrates favorable discriminative performance (AUC =0.710) and robust stability (Brier score =0.106). With the development of artificial intelligence, radiomics-based approaches have been increasingly employed to construct more complex HER2 prediction models. For instance, Jiang et al. developed three machine learning models (logistic regression, support vector machine, and random forest) by using PET/CT-derived radiomics features. These models achieved AUC values of 0.809, 0.761, and 0.861 in the training cohort, and 0.628, 0.993, and 0.717 in the validation cohort, with corresponding Brier scores of 0.118, 0.214, and 0.143, respectively (24). Although some models exhibited slightly higher AUCs than our study, they demonstrated inferior stability and reproducibility, showing large fluctuations in performance. Furthermore, such models typically require specialized software platforms and intricate data preprocessing procedures, posing significant challenges for clinical implementation. In contrast, the variables incorporated in our model are routinely available in clinical practice and grounded in well-established pathological principles. This model, which is based on widely accessible clinical and imaging parameters, has strong interpretability and is easier to integrate into actual clinical diagnosis and treatment processes, thereby enhancing its potential for clinical translation and practical application.

Nevertheless, we acknowledge that the predictive performance of our nomogram is only moderate, and the limited number of HER2-positive cases inevitably restricts its robustness. Compared with the complex artificial intelligence (AI)-based models mentioned above, the variables we included, though practically accessible, are already known correlates of HER2 expression, which reduces the novelty of the model to some extent. However, we believe our study still adds value by systematically evaluating conventional PET metabolic parameters in a well-characterized cohort and providing a simple, interpretable nomogram that can serve as a foundation for future hypothesis-driven research. We emphasize that the clinical applicability of this nomogram is not yet established, and it should not replace pathological HER2 testing as the current gold standard. Rather, it may offer complementary information in selected scenarios—such as when biopsy is challenging, when sampling error is a concern, or for the dynamic assessment of intratumoral heterogeneity during treatment—although these potential indications require rigorous prospective validation in larger, multicenter cohorts.

This study has the following limitations. First, as a retrospective analysis, the findings may be subject to selection bias. Second, this study did not fully explore and utilize the parameter information in CT images. Third, being a single-center investigation, the study lacks an independent external validation cohort. Fourth, the imbalanced sample size may lead to overfitting of the predictive model, limiting its generalizability. Although internal model stability was confirmed through Bootstrap validation, the generalizability of the results requires confirmation in prospective, multi-center studies with diverse populations. Lastly, we continue to enroll and collect follow-up data. The current analysis was restricted to examining the association between conventional metabolic parameters derived from ¹⁸F-FDG PET/CT and HER2 expression in GC, along with the predictive value of these parameters for HER2 status, without incorporating prognostic or survival outcomes.

Conclusions

This study demonstrates a significant association between ¹⁸F-FDG PET/CT metabolic parameters and HER2 expression in gastric adenocarcinoma. The exploratory nomogram integrating SUV_max, tumor location, and differentiation shows modest predictive performance and may serve as a preliminary reference for hypothesis generation. However, its current performance and limited sample size preclude clinical implementation; further prospective, multicenter validation is warranted before this approach can be considered for routine practice.

Acknowledgments

An abstract of this study was presented as a poster at the 38th Annual Meeting of the European Association of Nuclear Medicine (EANM), 2025.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0345/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0345/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0345/prf

Funding: This work was supported by the Changzhou Science and Technology Bureau (No. CJ20244011, to X.L.S.; No. CJ20252026, to Q.Z.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0345/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of The Third Affiliated Hospital of Soochow University (Approval No. 2025(education)CL026-01) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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