Characterization of ¹⁸F-fluorodeoxyglucose metabolic spatial distribution improves the differential diagnosis of indeterminate pulmonary nodules and masses with high fluorodeoxyglucose uptake

Introduction

With the increase in lung cancer screening and the growing global aging population, the incidence of lung cancer is increasing rapidly. Lung cancer has become the leading cause of cancer-associated death in the United States (1). Early detection strategies and surgical resection are the key to reducing lung cancer mortality (2).

Positron emission tomography/computed tomography (PET/CT), a noninvasive imaging technique, is widely used in the diagnosis and staging of pulmonary nodules (3,4). The maximum standard uptake value (SUV_max) is a key semiquantitative parameter for PET that measures the metabolic activity of a lesion. An SUV_max of 2.5 is usually used as the cut-off value for differentiating malignant from benign pulmonary lesions. However, ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) is not a tumor-specific agent; thus, intense FDG uptake can be observed in a significant percentage of benign pulmonary lesions, including inflammatory pseudotumors, cryptococcosis, and tuberculosis (5-7). Previous studies on the diagnosis of pulmonary lesions have reported high false positive rates associated with SUV (8,9). In particular, when the morphological characteristics of benign and malignant lesions are atypical, the false positive rate is higher. The high rates of false positive results have seen a corresponding increase in the rates of unnecessary invasive lung surgery and associated morbidity (10).

Therefore, a novel, alternative PET/CT-derived method is urgently needed to compensate for the high false positive rates resulting from the SUV method. In tumor development and progression, cancer cells migrate to and invade blood vessels (11); therefore, it can be speculated that cell metabolic activity will be higher in the proximal part of malignant tumors than in the distal part. Our previous findings have confirmed that the relative activity distribution (RAD) of ¹⁸F-FDG in malignant and benign solitary pulmonary nodules (SPNs) are markedly different. Furthermore, the use of SPN characterization with ¹⁸F-FDG RAD was much more specific than conventional SUV diagnostic methods in differentiating malignant from benign SPNs (12). However, the previous study only analyzed local metabolic distribution on small pulmonary nodules, and data needed to be obtained by performing complex diameter measurements and calculations. Therefore, the clinical feasibility of ¹⁸F-FDG metabolic spatial distribution (FMSD) in differentiating pulmonary nodules requires a wider range of samples and further simplification.

Therefore, the aim of the present study was to evaluate whether visual assessment of ¹⁸F-FDG metabolic spatial distribution (V-FMSD), an easy-to-operate method, could improve the differential diagnosis of hypermetabolic pulmonary nodules and masses.

Methods

Patients

This retrospective study included 301 consecutive subjects (186 men and 115 women; mean age, 61.6±11.2 years; range, 21–89 years) who underwent FDG PET/ CT for suspected malignant pulmonary nodules or masses between January 1, 2015 and April 26, 2017. The exclusion criteria were as follows: (I) ¹⁸F-FDG SUV_max of the lesion <2.5; (II) typical benign signs (central laminated or diffuse calcification, or popcorn pattern of calcification) or malignant signs (any three of the following: densely spiculated margin, vacuole sign, air bronchogram, vessel convergence, pleural indentation, and lesion with cavitation and wall thickness >16 mm) detected by chest CT; (III) metastatic lesions or evidence of metastasis; (IV) multiple hypermetabolic nodules or masses in both lungs; (V) a previous history of lung biopsy or neoadjuvant chemoradiotherapy; (VI) a previous history of other cancer; (VII) obvious motion artifacts.

The pathological specimens were obtained by fiber optic bronchoscopy, percutaneous biopsy, resection by thoracotomy, or video-assisted thoracoscopic surgery after PET/CT examination. All malignant lesions were finally confirmed by pathology, and a definitive diagnosis of a benign lesion was made based on pathological analysis or at least 2 years of follow-up by chest CT. This study was approved by the Institutional Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University, and written informed consent was obtained from all participants.

PET/CT acquisition

Patients fasted for at least 6 hours, and their serum glucose levels were <110 mg/dL before intravenous injection of ¹⁸F-FDG (3.7 MBq/kg). Approximately 60 minutes after the injection, images were acquired using a hybrid PET/CT scanner (GEMINI TF 64, Philips, Best, Netherlands). The detailed PET/CT protocol can be found in our previous published study (13). The dual time point scans of the pulmonary lesions were performed 120 minutes after the injection. PET images were reconstructed using the ordered subset expectation maximization (OSEM) method (33 subsets per iteration). All collected data were transferred onto the Philips Extend Brilliance Workstation (EBW, Philips, Best, Netherlands) 3.0 for reconstruction of PET, CT, and PET/CT fusion images.

Image analysis

Data measurement was independently performed by a nuclear medicine physician with 3 years of experience in PET/CT diagnosis. For each lesion, the SUV_max values of early and delayed scans were quantified. The retention index (RI) was calculated according to the following formula:

RI=100%×(DualtimepointSUVmax−EarlySUVmax)EarlySUVmax[1]

In the assessment of SUV, the findings were considered positive when SUV_max ≥2.5, and for delayed PET scans, RI > 0 was defined as positive.

The combined PET/CT images were independently assessed by two radiologists, each with over 30 years’ experience in radiological diagnosis, who were blinded to the FMSD characteristics of the lesions. First, definitely positive findings were considered when ¹⁸F-FDG uptake in lesions was significantly higher than that in the mediastinal blood pool. Second, for lesions with a slightly high uptake of ¹⁸F-FDG, the morphological and metabolic lesion characteristics were combined to draw a comprehensive diagnosis. The diagnostic results were divided into probably positive or probably negative. When the findings were inconsistent between the two readers, a consensus was reached by discussion. Finally, definitely or possibly positive lesions were recorded as positive, and possibly negative lesions were recorded as negative.

The V-FMSD was analyzed by two nuclear medicine physicians each with 10 years of experience in PET/CT diagnosis, who were blinded to the information of the CT images and the final diagnoses of the patients. The proximal region of the lesion was defined as the area near the ipsilateral hilar, and the area far from the ipsilateral hilar was defined as the distal region (14). A 5-point scoring system was used to interpret the characteristics of FMSD, which represent the likelihood of the lesion being benign or malignant (Table 1, Figure 1). When the characteristics of FMSD in lesions were not obvious, the metabolic threshold could be manually adjusted on transverse PET images to assist the interpretation. When the interpretation scores were inconsistent between the two readers, a consensus was reached by discussion. Finally, a score of ≤2 was recorded as negative, while a score of ≥3 was recorded as positive.

Table 1 The 5-point scoring system for the interpretation of the characteristics of FMSD
Full table

Figure 1 Representative FDG PET/CT images for the interpretation of the characteristics of FMSD. FDG PET/CT fused images demonstrate examples of lesions with FMSD grade 1–5, respectively. FDG, fluorodeoxyglucose; PET/CT, positron emission tomography/computed tomography; FMSD, ¹⁸F-FDG metabolic spatial distribution.

Statistical analysis

Statistical analyses were performed with SPSS software, version 23.0 (IBM Corp., Armonk, New York, USA). The Mann–Whitney U-test was used to evaluate the continuous variables between malignant and benign lesions, and the results were described as mean ± standard deviation. The Chi-squared test was performed to analyze the categorical data, and the results were presented as frequencies and percentages. Receiver operating characteristic (ROC) curve analysis was used to assess the performance of SUV_max, RI, combined PET/CT assessment, and V-FMSD for distinguishing malignant and benign lesions. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) for each diagnostic method were also calculated. McNemar’s test was used to compare the sensitivity, specificity, and accuracy of V-FMSD with those of the other methods. The intra-observer variability in visual scores was determined using Cohen’s kappa coefficient (κ). A P value of <0.05 was considered to be statistically significant.

Results

General characteristic of subjects

Of 301 nodules and masses, 194 (64.5%) were malignant, and 107 (35.5%) were benign. The average maximum diameter of the lesions was 27.7±13.2 mm (range, 8–63 mm). The malignant lesions were pathologically confirmed as adenocarcinoma (129, 66.5%), squamous cell carcinoma (41, 21.1%), adenosquamous carcinoma (13, 6.7%), small cell carcinoma (4, 2.1%), neuroendocrine carcinoma (4, 2.1%), and sarcomatoid carcinoma (3, 1.6%). The benign lesions, confirmed by pathology or follow-up, included tuberculosis (45, 42.1%), cryptococcal disease (18, 16.8%), chronic inflammation (12, 11.2%), hemangioma (9, 8.4%), focal organizing pneumonia (8, 7.5%), granulomatous inflammation (6, 5.6%), inflammatory pseudotumor (3, 2.8%), interstitial pneumonia (2, 1.9%), fungal infection (2, 1.9%), angiofollicular hyperplasia (1, 0.9%), and sclerotic silicotic nodule (1, 0.9%). The general characteristics of benign and malignant lesions are shown in Table 2. Compared with benign lesions, malignant tumors presented with significantly higher values for lesion size, SUV_max, delayed SUV_max, and RI.

Table 2 General characteristics of subjects
Full table

The V-FMSD scoring results

The visual scoring results of FMSD in the benign and malignant lesions are shown in Table 3. There were 180 (92.8%) malignant lesions with a score of ≥3, which indicated that FDG metabolic distribution in the proximal region of the malignant lesions was significantly higher than that in the distal region (Figure 2). There were 78 (72.9%) benign lesions with a score of ≤2, which indicated that FDG metabolic distribution in the proximal region of benign lesions was significantly lower than that in the distal region (Figure 3). The intra-observer variability in visual scores by the two observers is shown in Table 4. The Cohen’s kappa coefficient demonstrated good agreement between the two observers’ visual scores, with a kappa value of 0.635.

Table 3 Scoring results of V-FMSD in benign and malignant lesions
Full table

Figure 2 Images from a 65-year-old female patient with adenocarcinoma. Transaxial CT (A) showed a nodule in the right upper lobe. The nodule presented profound FDG uptake with an SUV_max of 6.4 on transaxial PET (B) and fused (C) images. The FMSD in the proximal region of the nodule was significantly higher than that in the distal region, and the V-FMSD score was 5, which indicated a malignant lesion. CT, computed tomography; FDG, fluorodeoxyglucose; SUV_max, maximum standard uptake value; PET, positron emission tomography; FMSD, ¹⁸F-FDG metabolic spatial distribution; V-FMSD, visual assessment of ¹⁸F-FDG metabolic spatial distribution.

Figure 3 Images from a 49-year-old female patient with tuberculosis. Transaxial CT (A) showed a mass in the right lower lobe. The lesion displayed profound FDG uptake with a SUV_max of 8.4 on transaxial PET (B) and fused (C) images. The FMSD in the proximal region of lesion was significantly lower than that in the distal region, and the V-FMSD score was 1, which indicated a benign lesion. CT, computed tomography; FDG, fluorodeoxyglucose; SUV_max, maximum standard uptake value; PET, positron emission tomography; FMSD, ¹⁸F-FDG metabolic spatial distribution; V-FMSD, visual assessment of ¹⁸F-FDG metabolic spatial distribution.

Table 4 Assessment of intra-observer variability in visual scores
Full table

The diagnostic performance of different methods

The performance of V-FMSD for distinguishing malignant from benign lesions is shown in Table 5. When V-FMSD score ≥3 was used as the cut-off value, the sensitivity, specificity, PPV, NPV, and diagnostic accuracy of V-FMSD were 92.8%, 72.9%, 85.7%, 86.1%, and 89.2%, respectively. The diagnostic performance of V-FMSD showed no significant difference in distinguishing malignant and benign lesions in nodules of different sizes (all P>0.05). Although the sensitivity of V-FMSD did not differ significantly from those of the other three methods (all P>0.05), its specificity (72.9%) was significantly higher than those of SUV_max (0%, P<0.001) and RI (26.2%, P<0.001) (Table 6).

Table 5 The performance of V-FMSD for distinguishing benign and malignant lesions
Full table

Table 6 Comparison of diagnostic value among the different methods
Full table

The ROC curves for the different methods in the diagnosis of malignant and benign lesions are shown in Figure 4. The areas under the ROC curves (AUCs) of SUV_max, RI, combined PET/CT assessment, and V-FMSD were 0.626, 0.670, 0.788, and 0.886, respectively. The AUC of V-FMSD was significantly larger than those of SUV_max, RI, and combined PET/CT (all P<0.05) (Table 7).

Figure 4 The ROC curves plotted for the different diagnostic methods. ROC, receiver operating characteristic.

Table 7 Comparison of AUCs among the different methods
Full table

Discussion

The present study firstly evaluated the performance of V-FMSD for distinguishing pulmonary malignant and benign lesions. The results indicated that FDG uptake in the proximal region of malignant lesions was significantly higher than that in the distal region, and the FMSD of benign lesions was opposite to that of malignant lesions. Furthermore, FMSD demonstrated higher diagnostic efficacy than SUV_max and RI in the diagnosis of pulmonary hypermetabolic lesions, especially in terms of specificity. Therefore, this method should be considered as a valuable investigative addition in the assessment of indeterminate pulmonary lesions.

¹⁸F-FDG PET/CT is a well-established imaging modality for the diagnosis of patients with suspected lung cancer. However, the overlap of SUV_max between benign and malignant pulmonary lesions limits PET/CT diagnostic value. Previous studies have reported the specificity of PET for the diagnosis of malignant pulmonary nodules to vary between 13% and 89% (15-19). Inflammatory lesions have been shown to account for 73% of benign non-neoplastic lesions with increased ¹⁸F-FDG uptake (20). The ¹⁸F-FDG uptake which represents the levels of cellular glucose metabolism in inflammatory and oncological diseases shares the similar mechanism and is mediated by the glucose transporters GLUT1–5 (21,22). In active inflammation and granulomatous diseases, such as tuberculosis and sarcoidosis, increased ¹⁸F-FDG accumulation appears in cells due to the expression of glucose transporters, especially GLUT1 in activated leucocytes, macrophages, and T lymphocytes (23-25).

Since the prevalence of tuberculosis in China is higher than in Western countries, the accuracy of ¹⁸F-FDG PET/CT for the identification of pulmonary abnormalities may be decreased. In the present study, almost half (42.1%) of benign lesions were tuberculosis, and the average SUV_max of those tubercular lesions was 6.7. All of the cases included in this study had hypermetabolic lesions with an SUV_max >2.5. When SUV_max 2.5 was used as the cut-off value to diagnose lung cancer, the number of false positive cases was 107, and the specificity and accuracy were 0% and 64.5%, respectively. This showed that the value of SUV_max in the diagnosis of hypermetabolic lung nodules and masses was extremely limited. PET/CT combines lesion morphology and metabolic information. However, for hypermetabolic pulmonary nodules and masses with atypical CT morphology, the diagnostic basis is more dependent on the metabolic information of the lesion. In this study, the number of false positive cases with PET/CT combined analysis was 41, of which tuberculosis and cryptococcal bacteria accounted for a large proportion. Therefore, to further improve diagnostic accuracy for hypermetabolic nodules and masses, it is necessary to explore novel auxiliary analysis methods.

Our previous study confirmed the difference in RAD of ¹⁸F-FDG between malignant and benign SPNs (12). To further simplify the evaluation, the present study used the hilar as a reference for the division of lesions into proximal and distal regions, and visually interpreted the spatial distribution of ¹⁸F-FDG uptake in these two regions of benign and malignant lesions. The results of this study showed that in the malignant lesions, the ¹⁸F-FDG uptake in the proximal region was significantly higher than that of the distal region. In benign lesions, the spatial distribution of ¹⁸F-FDG was opposite to that in malignant lesions, with the ¹⁸F-FDG uptake lower in the proximal region than in the distal region. In regard to the pathological mechanisms underlying the metabolic spatial heterogeneity between benign and malignant lesions, one of the possible reasons may be related to angiogenesis and its distribution. Recent studies have indicated that angiogenesis plays an important role in tumor formation, growth, metastasis, and recurrence (26,27). Abnormal tumor vasculature may lead to altered perfusion, which is believed to be the major extrinsic driver of metabolism through its effects on oxygen and substrate delivery (28). Also, the main blood supply for lung tumors comes from the bronchial artery, while inflammation is more likely to stimulate the extrapulmonary circulation artery to participate in blood supply. Therefore, the blood supply in the proximal region of lung tumors is more abundant, and the same phenomenon can be observed in the distal region of lung inflammation. However, the metabolic distribution in a lesion is influenced by many factors, including the tumor microenvironment, genetic heterogeneity, and immune factors (29-31).

The hallmarks of the V-FMSD method are its simplicity and easy operability in clinic. Furthermore, this simple visual analysis can achieve a high diagnostic performance. Although the sensitivity of V-FMSD was similar to those of the PET/CT conventional markers, the specificity of this new method was significantly higher than those of SUV_max and RI. However, the visual difference in metabolic distribution in small nodules was not obvious due to their small volume, especially for nodules <10 mm in size. However, the diagnostic performance of V-FMSD for nodules of different sizes (>10 mm) showed no statistical significance in distinguishing between malignant and benign lesions. The pathologies of the 14 malignant nodules with FMSD grade 2 were adenocarcinoma (9, 64.3%), squamous cell carcinoma (2, 14.3%), adenosquamous carcinoma (2, 14.3%), and small cell carcinoma (1, 7.1%), respectively. Of the 29 cases of benign nodules with FMSD grade 3–5, almost half were tuberculosis (13, 44.8%). Compared with the findings of previous studies, our results still showed the conventional PET/CT markers to have lower sensitivity and specificity (3,32,33). The major reason for this might be the high proportion of benign hypermetabolic lesions included in this study. However, this novel visual assessment can still improve the diagnostic performance when the CT morphology is atypical. Therefore, V-FMSD can be considered a novel routine auxiliary diagnostic method for the diagnosis of indeterminate pulmonary nodules and masses with high ¹⁸F-FDG uptake.

The main limitation of this study is that visual analysis is a subjective empirical judgment which may introduce subjective bias. In the present study, the Kappa value for intra-observer agreement was 0.635, which suggested that consistency could be further improved. In clinical practice, multiple comparative analysis of FMSD by adjusting the SUV threshold can be used to improve the repeatability of the assessment. In patient selection, we only included pulmonary nodules and masses with avid ¹⁸F-FDG uptake, which might have introduced selection bias. Moreover, this study did not explore the related molecular mechanisms. However, our pre-experiments showed that the expression levels of CD31 and epidermal growth factor receptor (EGFR) were different between the proximal and distal regions of benign and malignant lung lesions, which suggested that the difference in FMSD may be correlated with tumor angiogenesis and its distribution. Therefore, future studies will explore the possible related mechanisms.

Conclusions

In summary, we found that pulmonary benign and malignant lesions had distinct FMSD characteristics. FDG uptake in the proximal region of malignant lesions was significantly higher than that in the distal region, and the FMSD of benign lesions was opposite to that of malignant lesions. V-FMSD is a simple and easy procedure; therefore, it can be used as a novel auxiliary marker in clinical practice to improve the diagnostic accuracy for indeterminate pulmonary nodules and masses with avid ¹⁸F-FDG uptake.

Acknowledgments

Funding: This work was supported by Zhejiang Province Public Welfare Technology Application Research Project (No. LGF19H180012) and Wenzhou Science and Technology Program (No. Y20170833).

Footnote

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-768). The authors have no conflicts of interest to declare.

Ethical Statement: This study was approved by the Institutional Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University. Written informed consent was obtained from all patients.

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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