The value of complete blood count parameters in the preoperative diagnosis of parathyroid tumor

Introduction

Primary hyperparathyroidism (PHPT) is the third most common endocrine disease and is caused by parathyroid tumors, including parathyroid carcinoma (PC) and parathyroid adenoma (PA) (1). PA accounts for 80–85% of cases of PHPT. In contrast, PC is a rare disease characterized by hypersecretion of parathyroid hormone (PTH), which may result in refractory hypercalcemia and mortality (2). Currently, radical resection remains the most effective treatment for parathyroid tumors (3). En bloc resection of the parathyroid and ipsilateral thyroid lobe is the first-line treatment for patients with PC, whereas tumor resection is adequate for patients with PA. Therefore, the differential diagnosis of PC before surgery is critical for selecting the proper surgical approach.

However, preoperative differentiation of PC remains challenging without definite evidence of metastasis before surgery due to the overlap in clinical presentation and biochemical results between PA and PC in many cases. Furthermore, fine needle aspiration biopsy of parathyroid neoplasms is not recommended because of the risk of tumor dissemination (4). Therefore, despite the identification of specific pathological markers, such as loss of parafibromin staining and elevated expression of galectin-3, a large portion of PC cases are definitively diagnosed only upon recurrence and metastasis, leading to a poor prognosis (5). In many cases, tumors are initially presumed benign, and radical surgery is not performed until recurrence or distant metastasis is observed. Moreover, it is challenging to distinguish PCs from PAs intraoperatively, as no specific gross features are specific to PC (6,7). Studies have indicated that a palpable neck mass greater than 3 cm, plasma calcium >3.5 mmol/L, and PTH levels 10 times above the upper limit of normal indicate malignancy (8,9). However, the diagnostic power of a model incorporating these factors for clinical practice is extremely weak, as many patients with large-volume PA present with severe illness resulting from patient delay. Therefore, a practical diagnostic model with an improved ability to identify malignancies is urgently needed to guide surgical decision-making and reduce the risk of misdiagnosis and overtreatment.

In recent years, extensive research has highlighted the significant role of inflammation in driving the malignant transformation of normal cells (10,11). Inflammatory processes contribute to the development of various cancers. For example, chronic inflammation may influence immune responses and modulate the tumor microenvironment, impacting all stages of tumor progression in prostate and liver cancers (12-14). Inflammatory markers that reflect the inflammatory status of patients have demonstrated diagnostic value in various cancers (15). Complete blood count (CBC) analysis is a cost-effective routine method, and the results can reflect the systemic inflammatory response. Notably, CBC analysis involves a wide range of inflammatory parameters and has been investigated for diagnostic purposes in many diseases, such as sepsis, acute respiratory distress syndrome (ARDS), and various cancers, including bladder and colorectal cancer (16-20). Previous studies on PC have focused on the diagnostic significance of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR). The LMR has been identified as a predictor of malignancy in patients with PHPT (21,22). However, the diagnostic value of other CBC parameters in PC has not been extensively studied, and the number of patients with PC assessed has been limited.

In this study, a retrospective analysis of CBC parameters was performed in a relatively large cohort of patients with PA and PC. Logistic regression (LR) and machine learning models were constructed, and receiver operating characteristic (ROC) curves were generated to assess the diagnostic significance of the CBC parameters. Additionally, we analyzed risk factors for recurrence and mortality of PC with respect to CBC parameters. We present this article in accordance with the STARD reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-2025-110/rc).

Methods

Patients

In this retrospective analysis, 68 patients were consecutively selected from 127 individuals diagnosed with PC at first visit in Peking Union Medical College Hospital from November 2002 to June 2024. The collected data included sex, age, CBC parameters, serum PTH level, serum calcium (Ca) level, and tumor size. The inclusion criteria were as follows: (I) diagnosis of PC confirmed histopathologically after surgical treatment; (II) preoperative data, including complete CBC parameters before initial surgery; and (III) no history of preoperative chemotherapy, radiotherapy, or immunotherapy. The exclusion criteria included (I) concurrent tumors in other locations and (II) cardiac, pulmonary, or hepatic insufficiency. Meanwhile, 340 patients with PA (a 5:1 ratio) were randomly selected as a control group from a prospective database of patients with PHPT at our institute. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, and was approved by the Ethics Committee of Peking Union Medical College Hospital (approval No. K2494). Informed consent was obtained from all participants.

Blood test data

Fasting blood samples were collected from all participants before surgery. The 49 PC patients accepted first surgery in Pekeing Union Medical College Hospital. Approximately 3 mL of antecubital venous blood sample was anticoagulated using vacuum blood collection tubes containing EDTA-K2 (for CBC), gel and clot activator (for PTH), and lithium heparin (for Ca). Whole blood samples were stored at 4 ℃ and sent for laboratory analysis. The CBC data of rest PC patients were collected from their records from other institutes. We recorded a comprehensive panel of 19 CBC parameters, including white blood cell (WBC) count, red blood cell (RBC) count, hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), hemoglobin (HGB), platelet (PLT) count, monocyte percentage (MON%), monocyte (MON) count, lymphocyte percentage (LYM%), lymphocyte (LYM) count, mean platelet volume (MPV), basophil percentage (BAS%), basophil (BAS) count, eosinophil percentage (EOS%), eosinophil (EOS) count, neutrophil percentage (NEU%), and neutrophil (NEU) count. Serum PTH levels were measured with a second-generation chemiluminescence immunoassay. Serum calcium levels were measured with a biochemical autoanalyzer. Based on the World Health Organization (WHO) classification of parathyroid tumors, histopathological diagnoses of PC and PA were confirmed by two pathologists. Clinical information, but not index test results, was available to the pathologists. The histological definition of PC includes the fulfillment of one of the following criteria: (I) angioinvasion, lymphatic metastasis, or intraneural invasion; (II) local invasion into adjacent anatomic structures; or (III) distant metastasis (23). Using pathological diagnosis as the reference standard, we assessed which CBC parameters could be identified as novel diagnostic markers.

Statistical analysis

Data analyses were conducted with SPSS 26.0 software (IBM Corp., Armonk, NY, USA). Patients with indefinite pathological diagnoses or missing data were excluded. The normality of the continuous variables in each group was assessed via the Kolmogorov-Smirnov (K-S) test. Normally distributed data are presented as the mean ± standard deviation (SD), and a t-test was used for statistical analysis. Nonnormally distributed data are presented as medians with quartiles and were compared via the Mann-Whitney test. The Chi-squared test was used to compare categorical variables. LR models were used for binary outcomes to distinguish benign and malignant parathyroid tumors. Machine learning regression models were also constructed with caret, “randomForest”, “gbm”, “e1071”, “glmnet”, “xgboost”, “klaR”, “class”, and “MLmetrics” R packages. Algorithms included the random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), extreme gradient boosting (XGB), multilayer perceptron (MLP), naïve Bayes (NB), k-nearest neighbors (KNN), and least absolute shrinkage and selection operator (LASSO) regression algorithms. The evaluation metrics used were accuracy, AUC, recall, precision, F1 score, and kappa value. Cox regression models were used to identify prognostic factors for PC. The diagnostic value of preoperative CBC parameters for parathyroid tumors was evaluated via ROC curves. ROC curves were plotted with the “pROC” package (24). Pearson correlation analysis was applied to assess the correlations between CBC parameters and serum PTH level, Ca level, and tumor size. A P value <0.05 was considered to indicate statistical significance.

Results

Clinical characteristics between patients with PC and PA

A total of 408 patients were included in this study. Among the 68 patients with PC, 33 were male and 35 were female. The PA group included 340 patients, including 89 males and 251 females) (P<0.001; Table 1). The median age was 49.5 years for the PC group and 55 years for the PA group (P=0.001; Table 1). Serum PTH, Ca levels, and tumor size were significantly greater in patients with PC than in patients with PA (P<0.001; Table 1). We calculated 19 CBC parameters and 3 inflammation markers, including the NLR, PLR, and LMR. Thirteen of these parameters significantly differed between the two groups (P<0.05; Table 1). Compared with the PA group, the PC group had a significantly higher WBC count, BAS%, MON count, NEU%, NEU count, and NLR (P<0.05; Table 1). In contrast, the RBC, HCT, BAS count, LYM%, MPV, EOS%, and LMR were significantly lower in the PC group (P<0.05; Table 1).

Pearson correlation analysis revealed that the PLT count, WBC count, LYM%, NEU%, BAS%, NEU count, BAS count, RBC count, HCT, and PLR were strongly correlated with serum PTH and Ca levels and tumor size. Among these parameters, LYM%, BAS count, RBC count, and HCT were all negatively correlated (Figure 1).

Figure 1 Relationships between complete blood counts and clinical parameters in parathyroid neoplasms with correlation analysis. Top left box: positive and negative correlations are marked in red and blue, respectively. Bottom right box: the darker the orange color is, the less the P value (≤0.05). BAS, basophil; BAS%, basophil percentage; Ca, calcium; EOS, eosinophil; EOS%, eosinophil percentage; HCT, hematocrit; HGB, hemoglobin; LMR, lymphocyte-to-monocyte ratio; LYM, lymphocyte; LYM%, lymphocyte percentage; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MON, monocyte; MON%, monocyte percentage; MPV, mean platelet volume; NEU, neutrophil; NEU%, neutrophil percentage; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; PLT, platelet; PTH, parathyroid hormone; RBC, red blood cell; WBC, white blood cell.

Diagnostic value of CBC parameters for PC

LR model

The abovementioned 13 parameters were included in a multifactorial LR analysis. The diagnostic factors for PC were found to be RBC count [odds ratio (OR) 0.443; 95% confidence interval (CI): 0.243–0.809], BAS count (OR 0.000; 95% CI: 0.000–0.001), BAS% (OR 12.314; 95% CI: 3.567–42.514), EOS% (OR 0.732; 95% CI: 0.571–0.937), and NEU count (OR 1.445; 95% CI: 1.139–1.833) (P<0.05, Table 2). The AUC of these CBC parameters in diagnosing PC was 0.870 (Figure 2A). Using clinical features such as tumor size, serum PTH, and Ca levels, the AUC for the diagnosis of PC was 0.876 (Figure 2B). Notably, in the combination of clinical features and CBC parameters, the independent factors for diagnosing PC were BAS count (OR 0.000; 95% CI: 0.000–0.001; P=0.001), BAS% (OR 7.833; 95% CI: 2.054–29.872; P=0.003), EOS% (OR 0.677; 95% CI: 0.497–0.923; P=0.013), serum PTH (OR 1.002; 95% CI: 1.001–1.002; P<0.001), and Ca level (OR 4.208; 95% CI: 1.486–11.916; P=0.007; Table 3). The AUC of this panel of combined parameters was 0.946 (Figure 2C).

Figure 2 ROC curves for PC diagnosis of clinical and CBC parameters. (A) The AUC for CBC parameters was 0.870; (B) the AUC for clinical parameters was 0.876; (C) the AUC of the combined CBC and clinical parameters was 0.946. AUC, area under the ROC curve; CBC, complete blood cell; CI, confidence interval; PC, parathyroid carcinoma; ROC, receiver operating characteristic.

Machine learning models

Several machine learning regression models were built to predict the malignancy of parathyroid neoplasms. When CBC parameters and clinical features were combined, the accuracy values of the RF, GBM, SVM, XGB, MLP, NB, KNN, and LASSO models were 1.000, 0.998, 0.913, 1.000, 0.945, 0.923, 0.888, and 0.913, respectively (Figure 3A), with the corresponding AUCs being 1.000, 1.000, 0.946, 1.000, 0.942, 0.970, 0.927, and 0.942, respectively (Figure 3B). Among the top variables that contributed most significantly to all models, BAS%, BAS count, and PTH and Ca levels made substantial contributions to the differential diagnosis (Figure 3C).

Figure 3 Diagnostic value of complete blood cell parameters for distinguishing parathyroid carcinoma via machine learning models. (A) Performance metrics of machine learning models using six evaluation criteria; (B) ROC curves of the machine learning models for parathyroid carcinoma diagnosis; (C) the critical variables contributing to decision-making across models. AUC, area under the ROC curve; BAS, basophil; BAS%, basophil percentage; Ca, calcium; EOS, eosinophil; EOS%, eosinophil percentage; GBM, gradient boosting machine; HCT, hematocrit; HGB, hemoglobin; KNN, k-nearest neighbors; LASSO, least absolute shrinkage and selection operator; LogReg, logistic regression; LMR, lymphocyte-to-monocyte ratio; LYM, lymphocyte; LYM%, lymphocyte percentage; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MLP, multilayer perceptron; MON, monocyte; MON%, monocyte percentage; MPV, mean platelet volume; NB, naïve Bayes; NEU, neutrophil; NEU%, neutrophil percentage; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; PLT, platelet; PTH, parathyroid hormone; RBC, red blood cell; RF, random forest; ROC, receiver operating characteristic; SVM, support vector machine; WBC, white blood cell; XGB, extreme gradient boosting.

Value of CBC parameters in predicting PC outcomes

Of the 68 patients with PC enrolled in this study, 5 were excluded from the disease-free survival (DFS) analysis due to the presence of distant metastasis at the time of consultation. Additionally, 21 patients experienced recurrence, and 11 died during follow-up. In the multifactorial Cox regression analysis, the independent risk factors for recurrence were MCHC (P=0.02), parafibromin staining loss (P=0.003), and PTH (P=0.02) (Table 4).

Discussion

The preoperative diagnosis of PC is critical for optimal management and surgical decision-making but is difficult to accomplish without evidence of metastasis. CBC parameters may be used as auxiliary tools to increase diagnostic power. Several previous studies have made groundbreaking explorations into its potential. In an earlier study, the preoperative NLR was significantly correlated with the Ca level, PTH level, and adenoma size (22). Our results were consistent with those of previous studies on the association between NLR and serum PTH and Ca levels. Ohkuwa et al. reported that a low LMR was related to malignancy in a cohort of 36 patients with PC (21). However, our results could not confirm the significance of LMR in diagnosing PC despite its recognized prognostic role in other malignancies (25). This inconsistency may be due to the limited sample size in the studies, given the low incidence of PC.

CBC parameters at the time of diagnosis could reflect the inflammatory status of tumors (26). In our cohort of 68 PC and 340 PA cases, RBC, BAS, BAS%, EOS%, and NEU were identified as independent correlates for PC diagnosis in LR. RBC count was negatively correlated with PC risk. The increased oxygen demand in tumors may initially trigger compensatory elevations in RBC counts to increase the oxygen-carrying capacity (27,28). However, in patients with terminal cancer, the osmotic resistance and deformability of RBCs are decreased. Tumor-derived anemia-inducing substances in the plasma may influence RBC energy metabolism, leading to cytotoxicity of RBCs (29). This might explain the low RBC counts in patients with PC. The other parameters we examined are associated with inflammatory responses. Our results revealed that patients with higher BAS%, NEU, and lower BAS and EOS% may be more susceptible to malignancy. These findings suggest that these cells may play a role in tumor-related immunology and tumor development in PC, consistent with their roles in other tumors (30). EOSs can influence tumor cell growth by releasing cytokines, chemokines, and cytotoxic substances. In patients with solid tumors and hematologic malignancies, a relatively high EOS level may be detected (31,32). This contrasts with our results, which may be due to different immune statuses and malignancies. However, increased BAS% was associated with PC. NEUs are typically associated with acute inflammatory responses. Cytokines and chemokines recruit NEUs to the tumor microenvironment. The increase in NEU count may reflect immune response activation within the tumor microenvironment, which could release tumor growth factors to promote tumor development (33,34). Studies have shown that NEU count may act as a potential prognostic factor in certain cancers through the NLR, indicating its potential value in cancer diagnosis (35-37).

Studies indicate that higher serum PTH and Ca levels and larger tumor size are correlated with PC risk (8,9). In our cohort, serum PTH and Ca levels and tumor size were significantly higher in patients with PC than in those with PA. For classic clinical parameters such as serum PTH, Ca level, and tumor size, the AUC for diagnosing PC was 0.876. The model that combined the CBC parameters had a higher AUC of 0.946. Consistent with the LR model results, the machine learning models further revealed the potential diagnostic value of BAS combined with PTH and Ca. BAS was related to chronic inflammatory processes, which may be a mechanism for PC development.

Our results also suggest that CBC parameters may be useful indicators for the prognosis of patients with PC. A lower MCHC in CBC analysis was found to be an independent risk factor for recurrence in patients with PC, and BAS count was associated with mortality. MCHC reflects the hemoglobin concentration in RBCs, with a low MCHC typically being associated with anemia. Patients with tumors generally present with anemia because tumor cells secrete soluble molecules that suppress erythropoiesis (38,39). Thus, it is reasonable to speculate that a high MCHC can reduce the recurrence rate in patients with PC. A previous study reported that a high MCHC could predict improved outcomes in patients with gastric cancer (40). BASs play a key role in immunoglobin E (IgE)-dependent and IgE-independent allergic inflammation. Increased BAS% is associated with poor outcomes in prostate cancer (41) and gastric cancer (42) but is associated with improved outcomes in colorectal cancer (43) and glioblastoma (44). In our cohort, increased BAS count indicated PC, as mentioned above, and thus it may alert clinicians to a relatively poor prognosis in patients with PC. BAS infiltration may increase vascular endothelial growth factor release, promoting tumor growth (45-47) and potentially leading to PC development.

Several shortcomings of this study should be noted. First, the sample size of patients with PC was limited despite being one of the largest cohorts of such patients assembled in recent years. The significance of some CBC indices in diagnosis may therefore be statistically underscored. Second, the use of a retrospective data might have introduced bias in this study. A multicenter prospective study is underway to further refine this diagnostic model.

Conclusions

CBC parameters may serve as important auxiliary indicators for diagnosing parathyroid malignancy.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-110/rc

Data Sharing Statement: Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-110/dss

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-110/prf

Funding: This work was supported by the National Central High-level Hospitals Clinical Research Funding (2022-PUMCH-B-003) and the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2021-I2M-1-002).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-2025-110/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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, and was approved by the Ethics Committee of Peking Union Medical College Hospital (approval No. K2494). Informed consent was obtained from all participants.

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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(English Language Editor: J. Gray)