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Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers

BMC Infectious Diseases volume 24, Article number: 1050 (2024) Cite this article

Abstract

Objective

To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).

Methods

The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.

Results

Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.

Conclusion

Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.

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Introduction

A solitary pulmonary nodule (SPN) is defined as a round or oval opacity, typically less than 3 cm in diameter, surrounded by solid or subsolid lesions within the air-containing lung tissue and exhibiting well-defined borders, without pulmonary atelectasis, hilar enlargement, and pleural effusion [2, 3]. The prevalence of lung nodules is increasing annually, attributed to factors such as severe haze from industrial pollution, as well as personal living habits and working environment. The current prevalence of lung nodules is estimated to range from approximately 8–52%. SPNs are frequently detected incidentally during routine chest computed tomography (CT) scans or X-ray examinations, with an occurrence rate of 0.09–0.2% on chest radiographs. Each year, approximately 150,000 such nodules are identified [15]. The underlying causes of these nodules are varied, including benign etiologies such as tuberculosis, granulomas, and cysts, in contrast to malignant ones, which are primarily primary lung cancer and metastatic disease [24]. Therefore, in the diagnostic evaluation and management of SPNs, timely detection of malignant nodules is essential for optimizing patient outcomes and survival rates.

The emergence of Coronavirus Disease 2019 (COVID-19) has compounded the complexity of diagnosing SPNs. Ground-glass opacities and consolidations, which are hallmarks of COVID-19 on CT scans, can mimic or overlap with characteristics of malignant nodules, complicating their interpretation [33, 34]. Approximately 90% of COVID-19 patients develop ground-glass opacities within two weeks of infection, while 5% may present with solid nodules or pulmonary thickening, potentially impacting clinicians’ assessment of nodule malignancy [19, 21]. The COVID-19 pandemic has resulted in many newly diagnosed lung cancer cases being identified at an advanced stage. Research indicates that in the second year of the pandemic, the diagnosis rate for new lung cancer cases increased by 75%, with over 50% of cases presenting as advanced or metastatic disease [10]. This not only increases the complexity of treatment for patients but also negatively impacts overall survival rates. Consequently, there is an urgent need for robust diagnostic tools capable of accurately differentiating between benign and malignant SPNs, particularly in the context of COVID-19.

Serum tumor markers have emerged as valuable tools in cancer diagnosis, offering a non-invasive approach to assess the malignant potential of SPNs [5]. The combination of imaging and serum tumor markers has been shown to improve diagnostic accuracy over either method used alone [23, 32]. However, the literature on the added value of this combined approach, especially in the context of COVID-19, remains limited.

In this study, we retrospectively analyzed data from 267 patients with SPNs to evaluate the diagnostic efficacy of a combined approach using CT imaging and serum tumor markers. Our aim was to establish a nomogram predictive model that could accurately predict the malignant potential of SPNs, particularly in patients with COVID-19 infection. By integrating established imaging criteria with emerging serological data, our study provides a comprehensive analysis that could potentially enhance clinical decision-making and improve patient outcomes.

Materials and methods

Participants

The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. Among these patients, 146 were males and 121 were females. Patients and their families were informed about the study, and they signed the informed consent form. This study was reviewed and approved by the ethics committee of the First Affiliated Hospital of Zhengzhou University.

Inclusion criteria: (1) All patients who had undergone CT, serum tumor marker testing and pathological examination, and had a diagnosis of isolated pulmonary nodules; (2) complete and unaltered patient case data; (3) no prior history of chemotherapy.

Exclusion criteria: (1) patients with other serious comorbid conditions, such as heart, liver, brain and kidney diseases; (2) patients who had undergone second surgeries; (3) patients with allergies to iodinated contrast agents; (4) patients lacking the capacity for informed consent or unable to cooperate with the study; (5) the presence of other abdominal tumors; (6) women who were pregnant.

Respiratory specimen viral nucleic acid testing

All individuals diagnosed with COVID-19 underwent confirmation via respiratory specimen viral nucleic acid testing. To prepare the specimens, which include sputum, throat, or nasal swabs, a preliminary treatment was conducted to lyse the virus and extract the nucleic acids. Utilizing real-time fluorescent quantitative PCR technology, three specific genes of the SARS-CoV-2 virus were targeted for amplification: the open reading frame 1a/b (ORF1a/b), the nucleocapsid protein (N), and the envelope protein (E) genes. The diagnostic outcome was ascertained by measuring the post-amplification fluorescence intensity. A positive nucleic acid result is indicated by the presence of the ORF1a/b gene and/or either the N or E gene.

Clinical data collection

Clinical data were meticulously gathered, encompassing a spectrum of patient-specific information: age, gender, smoking and cessation history, exposure to passive smoking, personal and familial tumor histories, pulmonary disease history, and exposure to cooking fumes. Additionally, the use of solid fuels such as firewood and coal briquettes, occupational exposures, clinical symptoms, and the status of COVID-19 infection were recorded. The criteria for defining cooking fume exposure stipulate that the patient must have cooked at least once daily over a period exceeding six months. A standardized inquiry was conducted to assess the degree of ocular and pharyngeal irritation provoked by cooking fumes, with the responses categorized as “never,” “occasionally,” “sometimes,” and “often.” An affirmative response of “sometimes” or “often” is quantified with a score of 1, while all other responses are assigned a score of 0.

CT examination

The US GE256 row Revolution CT machine was used to perform chest plain scans on subjects in the deep inspiration and breath-hold state.The scanning range extended from the apex of the lungs to the diaphragmatic surface of the lung bases. All images of lung window and mediastinal window were reconstructed using a bone segmentation algorithm and a standard algorithm, respectively. The scanning parameters were as follows: tube voltage and tube current were 120 kVp and 10–500 mA respectively, the tube rotation speed was 0.8s/r, the pitch was 0.992:1, and the image reconstruction slice thickness and layer spacing were 5 mm. All images were reconstructed by the AW4.7 workstation (GE HealthCare, USA), with mediastinal window settings of 350 HU and 40 HU, and lung window settings of 1500 HU and − 700 HU. Two experienced radiologistsat at our hospital interpreted the images, employing a visual comparison method for the test results. CT imaging data were collected, including the location, maximum diameter, type of nodule, vascular convergence sign, pleural retraction sign, bubble sign, air bronchogram sign, spiculated sign, lobulation sign, and calcification.

Detection of serum tumor markers

Five mL of morning fasting venous blood was collected from patients within 10 days prior to the CT scan, and the supernatant was collected after centrifugation. The electrochemiluminescence immunoassay, using a specific instrument kit, was used to determine the concentrations of carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), and neuron-specific enolase (NSE). A concentration of CEA > 5.0 µg/L was considered positive, CA125 > 35.0U/mL was considered positive, and NSE > 17.0 µg/L was considered positive. A positive tumor marker status was indicated if any one of the above indicators was positive.

Pathological diagnosis

Lesion tissues were obtained by percutaneous lung puncture or fiberoptic bronchoscopy. The tissues were successively fixed in 10% formalin, dehydrated through a graded ethanol series, cleared with xylene, embedded in paraffin, and sectioned for HE staining. All pathological evaluations were conducted independently by two pathologists. The pathological diagnostic results were used as the final diagnostic criteria.

Evaluation criteria

(1) Diagnostic criteria combining CT scan with tumor markers: a positive result on CT examination or a positive result for any of the three tumor markers was considered indicative of a positive SPN diagnosis.(2) The sensitivity, specificity and accuracy of the two examination modalities, both individually and in combination, were analyzed for the differential diagnosis of benign and malignant isolated pulmonary nodules using pathological findings as the gold standard. Sensitivity = true positive/(true positive + false negative) × 100%; specificity = true negative/(true negative + false positive) × 100%; accuracy = (true positive + true sexual)/(true positive + false positive + true negative + false negative) × 100%.

Nomogram construction

Single-factor and multi-factor logistic regression analyses were employed to identify independent predictive factors for malignant SPNs from clinical and radiological characteristics. Subsequently, a nomogram model based on these independent predictive factors was constructed using the “rms” package in the R programming language.

Statistical analysis

Data analysis was performed using SPSS 22.0 software. The normality of data distribution was evaluated employing Q-Q plots. Data that were normally distributed were described using mean ± standard deviation (x ± s), and group comparisons were conducted using the independent samples t-test. In cases where data were not normally distributed, they were depicted as median with interquartile ranges (M(P25, P75)), and comparisons were made utilizing the Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages, and group differences were assessed using the chi-square (χ [2]) test. Statistical significance was set at a p-value of less than 0.05. Model construction and statistical data analysis were performed using R software (version 4.2.1).

Results

Pathological findings

In this study, we enrolled 267 cases of SPNs that fulfilled the eligibility criteria, including 135 male and 132 female patients, with an age range of 38 to 75 years and a median age of 53 years. Pathological evaluations classified these cases into two distinct groups: Benign SPNs (169 cases) and malignant SPNs (98 cases). The benign SPNs group was further divided into inflammatory granuloma (77 cases), inflammatory nail tumor (67 cases), and tuberculoma (25 cases). The malignant SPNs group comprised adenocarcinoma (66 cases), squamous carcinoma (17 cases), small cell lung cancer (9 cases), and large cell neuroendocrine tumor (6 cases). No statistically significant differences were observed in the basic demographic data between the malignant and benign SPNs groups (P > 0.05), as detailed in Table 1. Furthermore, in both the non-COVID-19 and COVID-19 cohorts, statistically significant differences were observed between the benign and malignant SPNs groups across several CT parameters. These parameters included the mean CT value, maximum nodule diameter, spiculation, vascular convergence sign, vacuole sign, air bronchogram sign, and lobulation sign, as outlined in Table 2.

In non-COVID-19 patients with malignant nodules, CT imaging revealed a nodule in the right upper lobe exhibiting bronchial cutoff, with persistent enhancement noted on arterial and venous phase images following contrast administration. A biopsy confirmed the diagnosis of squamous cell carcinoma in the right middle lobe. COVID-19 patients with malignant nodules displayed a small nodule with internal vacuolization in the left lower lobe, accompanied by evident feeding vessels and sustained enhancement on enhanced CT images. Conversely, non-COVID-19 patients with benign nodules, having a history of hepatitis B for more than three years and diabetes for one year, were diagnosed with hepatocellular carcinoma and concurrently identified with a nodule in the right lower lobe six months prior. These patients were asymptomatic for respiratory distress, cough, expectoration, hemoptysis, chest pain, or dyspnea and exhibited elevated levels of alpha-fetoprotein and normal inflammatory indices on laboratory examination; a CT-guided lung biopsy disclosed chronic inflammation alongside interstitial fibrous tissue proliferation. COVID-19 patients with benign nodules, identified during routine check-ups, presented with a well-demarcated nodule in the left upper lobe on CT scans, characterized by smooth contours and minimal enhancement with predominantly low-density areas upon contrast administration (Fig. 1A and D). HE staining showed the pathological changes of different types of SPNs (Fig. 1E).

Fig. 1
figure 1

The representative SPN cases. A, malignant SPN cases (non-COVID-19 ); B, malignant SPN case (COVID-19); C, benign SPN case (non-COVID-19 ); D, benign SPN case (COVID-19 );①&②, plain scan lung window and mediastinal window images; ③&④enhanced axial images of arteriovenous phase. E, HE staining (400×)

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Table 1 Demographic characteristics of study participants
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Table 2 Comparison of imaging features of non-COVID-19 and COVID-19 patients
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Tumor marker levels

Whether patients have been infected with COVID-19 or not, malignant SPNs were associated with significantly higher levels of CEA, NSE than benign SPNs (P < 0.05), while no significant difference was found in CA125 levels (P > 0.05). In addition, tumor marker levels were increased in patients infected with COVID-19 compared with the non-COVID-19 group (Table 3).

Table 3 Tumor marker levels and SUVmax
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Diagnostic efficiency of CT with or without tumor marker levels

The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the combined diagnosis using serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05) (Table 4).

Table 4 Diagnostic efficiency of CT with or without tumor marker levels
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Construction of visual prediction model

A visual nomogram predictive model for malignant pulmonary nodules was developed based on factors related to the vascular convergence sign, vacuole sign, air bronchogram sign, lobulation sign, CEA, and NSE (Fig. 2). In the nomogram, several line segments with scales are visible. The first line segment is used to calculate the individual score corresponding to different values of each predictor. Each predictor has a marked line segment with a scale that represents its value, and the length of the line segment indicates the predictor’s contribution to a malignant SPN. The score for an index is found at the intersection of the vertical line with the score axis. The sum of the scores for each of the six predictive variables yields the total score, and the probability of a malignant SPN can be determined using the total score’s line segment in the nomogram.

Fig. 2
figure 2

Model nomogram. The nomogram was constructed based on logistic regression modeling, where each characteristic is plotted along the horizontal axis. A vertical line is drawn from each feature to the score axis, intersecting at the point that represents the score for that variable. The scores for all variables are then summed to obtain a total score. By drawing a vertical line from this total score to the risk axis, the intersection point indicates the likelihood of a malignant SPN

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Discussion

Clinical studies have revealed that 40–50% of malignant SPNs are eventually diagnosed as malignant tumors. Therefore, differentialing benign from malignant SPNs is crucial for timely and effective treatment of patients [11]. Compared with ordinary chest X -ray examinations, low-dose CT (LDCT) can be used to scan and screen high-risk patients, reducing the relative risk of lung cancer death by 20%. Furthermore, high -resolution CT can increase the discovery rate of lung nodules to 60%. Chest thin layer CT scanning often uses a 1-mm thickness or even thinner to reconstruct the image, which can more clearly display the details of the size, position, form, and density of lung nodules. It is one of the indispensable methods for diagnosing lung nodules at an early stages. PET-CT, as a noninvasive test, can show the metabolic status and anatomical localization of SPNs and can identify small nodules in the proximal diaphragmatic plane that lack typical benign and malignant CT signs or are difficult to determine with PET [28, 31]. Studies have confirmed that the sensitivity and specificity of PET-CT for the diagnosis of SPNs are higher than PET or CT alone, reaching up to 96% and 88%, respectively, with a negative predictive value of 92% [14, 16]. However, some inflammatory lesions, such as infections, granulomas, and tuberculosis, often exhibit high glucose uptake rates, which can lead to elevated SUVmax and thus false positives. In addition, highly differentiated lung adenocarcinomas and some alveolar carcinomas can present false negative results due to a lack of glucose uptake. It is extremely challenging for clinicians to minimize false-positive and false-negative rates during PET-CT testing [17, 18].

Salihoğlu [22] indicated that CT has a higher diagnostic value than MRI for SPNs, with higher sensitivity, specificity, and improved diagnostic accuracy. During the COVID-19 pandemic, the accuracy of CT scans may be compromised. The range of CT findings characteristic of COVID-19 includes ground-glass opacities (GGO), areas of consolidation, signs of vascular engorgement, interstitial thickening, and pleural effusions [4, 37, 39]. Although these features are crucial for diagnosing COVID-19, they also risk being misattributed to other pulmonary conditions, potentially reducing the specificity of CT imaging [12, 20]. Studies have demonstrated that CT scans can identify the quintessential radiographic manifestations of COVID-19 in patients with negative RT-PCR test results [6, 8, 29]. This indicates that CT scans may offer greater sensitivity than RT-PCR, especially during the early phases of infection or when RT-PCR results are indeterminate. However, this heightened sensitivity also increases the chance of false-positive results due to pulmonary changes associated with COVID-19. Additionally, post-recovery from COVID-19, individuals may exhibit ongoing pulmonary abnormalities, such as fibrosis-like changes [13, 25]. These lasting pulmonary alterations might further complicate the accuracy of CT scans in evaluating the malignancy of pulmonary nodules.

CEA, an acidic glycoprotein produced by the fetal gastrointestinal tract, has properties of a human embryonic antigen and serves as a broad-spectrum tumor marker. It exhibits relatively low expression in normal populations, and elevated levels can indicate the presence of malignancy [7]. CA125 is a tumor-associated antigen derived from somatic epithelial tissue, recognized by antibodies specific to somatic epithelial cells, and expressed in both blood and tumor tissues of patients with tumors. It is one of the important markers for ovarian tumors [35]. NSE is a glycolytic enzyme present in neuronal, and peripheral neuroendocrine tissues and is expressed in tumors of neuroendocrine origin [36]. In this study, we evaluated the impact of the COVID-19 pandemic on the diagnostic accuracy and efficacy of methods predicated on CT scans and tumor marker levels in predicting the malignancy of pulmonary nodules. Malignant SPNs showed significantly elevated levels of CEA, NSE, and SUVmax compared to benign SPNs, irrespective of COVID-19 infection status (P < 0.05). Consistent with previous studies [26, 27], no significant difference in serum CA125 levels was observed between patients with benign and malignant SPNs, a finding also observed in our study. Interestingly, serum levels of tumor markers were found to be elevated in COVID-19 patients compared to non-COVID-19 patients, aligning with the results reported by Wei et al. [30], and potentially representing a significant factor affecting the diagnosis of SPNs.

In this study, patients with malignant SPNs exhibited significantly higher serum levels of CEA and NSE compared to those with benign SPNs. The specificity of the combined detection of these two serum tumor markers for diagnosing malignant SPNs was relatively high (73.71%), yet the sensitivity was comparatively low (56.52%), which significantly impacted the diagnostic accuracy to 67.79%. The most important finding of this study is that the combined diagnosis of serum tumor markers and CT had higher sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) than the sensitivity, specificity, and accuracy of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing (56.52%, 73.71%, 67.79%) (P < 0.05), which is consistent with Zhang’s research results [38]. It is suggested that a combined diagnostic method using serum tumor markers and CT can enhance the accuracy of diagnostic results in SPNs patients in the context of COVID-19, potentially avoiding unnecessary surgical operationsand positively contributing to subsequent treatment.

Despite CT’s high sensitivity for early lung cancer detection, its specificity is limited due to imaging overlaps among different lung cancer subtypes and benign conditions, risking misdiagnosis. While tumor markers are established in lung cancer diagnosis, more research is needed on their early cancer sensitivity and specificity. Combining CT with markers could improve early diagnosis and patient outcomes. Additionally, pursuing precise, minimally invasive diagnostics like Computer-Aided Diagnosis (CAD) is crucial [1]. CAD’s advancement helps expedite diagnostic decisions by analyzing image patterns, potentially avoiding unnecessary biopsies. Employing features like 3D Histogram of Oriented Gradients (HOG), Resolved Ambiguity Local Binary Patterns (RALBP) enhances nodule classification for early lung cancer detection. Innovative endoscopies like Optical Coherence Tomography (OCT) and Endobronchial Ultrasound (EUS) offer non-invasive anatomical insights, beneficial for early lung disease assessment [9]. Nuwandi M. Ariyasingha suggested that the use of hyperpolarized diethyl ether gas or hyperpolarized butane gas as contrast agents for lung ventilation imaging may address the various challenges faced by hyperpolarized 129Xe gas in clinical functional lung imaging for a range of pulmonary diseases [40, 41]. Collectively, integrating these technologies may boost diagnostic precision and efficiency, ultimately enhancing patient prognosis.

Conclusion

The identification diagnosis of SPNs must be comprehensive and involve a thorough analysis. It is limited to rely on a single test method. Clinically, it should be evaluated and diagnosed in many ways. Therefore, the use of combined diagnostic methods can effectively overcome the limitations of a single examination and further improve diagnostic accuracy. In summary, the accuracy of using CT and tumor marker diagnosis is high, and it is worth promoting and applying.

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

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Author notes

  1. Huijuan Xiao and Yihe Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China

    Huijuan Xiao, Pan Liang, Ping Hou, Yonggao Zhang & Jianbo Gao

  2. Department of Emergency, the First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zheng zhou, Zhengzhou, 450052, Henan, China

    Yihe Liu

Authors
  1. Huijuan Xiao
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Contributions

H.J.X., Y.H.L.,and J.B.G. designed and supervised the study. H.J.X., P.L., P.H. conducted the experiments and drafted the manuscript. H.J.X., Y.G.Z. collected and analyzed the data. H.J.X., and Y.H.L. operated the software and edited the manuscript.All authors reviewed the manuscript.

Corresponding author

Correspondence to Jianbo Gao.

Ethics declarations

Ethics approval and consent to participate

This study was approved bythe Research Ethics Committee (REC) of Zhengzhou University under the ID number (2022-KY-0961-002). Methods used complied with all relevant ethical guidelines and regulations. Informed consent to participate was obtained from all of the participants in the study.

Competing interests

The authors declare no competing interests.

Summary points

The combined diagnosis of CT imaging and tumor markers can effectively overcome the limitations of a single examination, which is conducive to more accurately identifying the differential diagnosis of SPNs. Nomograms can enable clinicians to make individualized, visual, and precise predictions of the malignancy probability of solitary pulmonary nodules. Well-structured data is crucial for assisting healthcare professionals in identifying cancer patients and improving pandemic outcomes.

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Not applicable.

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The authors declare no conflicts of interest.

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Xiao, H., Liu, Y., Liang, P. et al. Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers. BMC Infect Dis 24, 1050 (2024). https://doi.org/10.1186/s12879-024-09952-3

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  • DOI: https://doi.org/10.1186/s12879-024-09952-3

Keywords

BMC Infectious Diseases

ISSN: 1471-2334

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