¹⁸F-FDG PET/CT imaging and the characterization of synchronous primary lung cancer versus intrapulmonary metastasis and primary versus metastatic lung cancer: a narrative review

Introduction

Background

[¹⁸F]fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging has become an indispensable tool within the field of clinical oncology, being implemented as part of the standard work-up protocol for lung cancer diagnosis, initial staging and restaging (1-4). As an integrated imaging modality, ¹⁸F-FDG PET/CT imaging combines the advantages inherent to both morphological and functional imaging capabilities to better characterize lesions, relying upon the increase in glucose metabolism observed within cancer cells to detect malignancy with higher accuracy than CT imaging alone (5,6). This observation involving increased glucose utilization can be explained by the Warburg effect, which involves an increase in flux through the glycolytic pathway under aerobic conditions mediated through an increase in expression of membrane-bound glucose transporters and glycolytic enzymes within tumour cells (7,8).

Distinguishing between primary and secondary lung malignancy (metastasis) originating from an extrapulmonary tumour is integral for diagnosis, prognosis, and therapeutic management. From the perspective of surgery, primary lung cancer treatment is more extensive (potentially involving a lobectomy with full mediastinal lymph node dissection) whereas secondary lung cancer presenting as a solitary malignant pulmonary nodule (SMPN) may be treated with a minimally invasive metastasectomy to maximize lung parenchymal preservation (9,10). Although metastatic pulmonary disease may manifest as readily identifiable cannonball-like lesions scattered throughout both lung fields on radiography in many cases, SMPNs present more of a diagnostic challenge, whereby relying on morphological features of the lesion on CT imaging alone may prove to be insufficient for accurate characterization.

Another conundrum that may arise in the context of lung cancer screening and diagnosis on imaging studies is the detection of two discrete SMPNs, where the distinction between synchronous multiple primary lung cancer (SMPLC) and intrapulmonary metastasis (IPM) becomes of crucial importance due to its implications on staging and treatment. According to the 8^th edition of the tumour, node, metastasis (TNM) staging system, tumours of the same histological type are considered to represent IPM and are staged as T3 (same lobe; ipsilateral lung), T4 (different lobe; ipsilateral lung), or M1a (contralateral lung). Contrarily, lesions confirmed to be of different immunohistochemical origin are considered to represent SMPLC and are staged separately (11,12). Reports estimate a frequency of SMPLC ranging from 0.2% to 8% among lung cancer patients, with a more recent study detecting an incidence rate of as high as 20% (13). Traditionally, SMPLC and IPM have been differentiated using the Martini and Melamed criteria, where the diagnosis of SMPLC was heavily dependent on detecting a disparity in histological subtype on pathology (14). Although employed for many years, such an approach is obviously insufficient when nodules demonstrate identical histopathological findings and thus, TP53 gene mutation analysis and immunohistochemistry have both emerged as reliable adjuncts to distinguish between SMPLC and IPM by detecting variations at the molecular level that confirm the either divergent or shared clonal origin of these lesions, respectively (15,16). A recent review discussed the use of molecular profiling to distinguish between primary and metastatic lung cancer, with a focus on the insights gleaned from whole-exome sequencing analyses (17). Nonetheless, one study showed that comprehensive histologic assessment by light microscopy was consistent with results obtained through molecular analyses in 91% of specimens analyzed, suggesting that histopathological characterization is still important in lesion differentiation (18).

It is known that FDG uptake, characterized by maximum standardized uptake value (SUV_max), could be positive in various infectious and inflammatory processes, in addition to being affected by factors such as tumor grade, radiotracer distribution time, lesion size, tumour histological subtype, artifacts and inter-institutional variability in FDG PET protocols (19-22). A higher SUV_max has been shown to be associated with tumor grade, aggressive lymphoma, lymphovascular invasion and P-glycoprotein expression in patients with untreated lung cancer (23-27).

The histologic subtypes of lung cancer have been studied and reviewed (28-35), but are not the focus of the current review. Briefly, among non-small cell lung cancer (NSCLC) histological subtypes (i.e., adenocarcinoma, squamous cell carcinoma (SCC), and large cell carcinoma), SCC has been found to be associated with higher SUV_max measurements relative to adenocarcinoma (31,34). With respect to adenocarcinoma specifically, Sun et al. was able to further show how FDG uptake progressively increases when categorizing lung adenocarcinoma histological subtypes into low (e.g., minimally invasive adenocarcinoma), intermediate (e.g., acinar-predominant invasive adenocarcinoma), and high grade (e.g., solid-predominant invasive adenocarcinoma) lesions (35).

More recently, radiomics has emerged as a potentially powerful tool in assisting with the classification of different histological subtypes of lung cancer using CT and ¹⁸F-FDG PET imaging features (31,36,37). For example, using dynamic PET imaging data, Bianchetti et al. was able to develop a machine learning-based algorithm for the automatic classification of adenocarcinoma from other histological subtypes using a trained Random Forest classifier that generates probability maps based on spatial and temporal features of FDG uptake with a high degree of accuracy (31). However, review of various models and radiomics feature extraction is beyond the scope of the current review.

Rationale and knowledge gap

In spite of the evidence showing an association between FDG uptake intensity and tumor aggressiveness, there are very limited studies assessing this relationship as it applies to the diagnosis of lung cancer in more specific clinical situations, which could be further explored to improve the diagnostic accuracy of FDG PET/CT imaging.

FDG PET/CT imaging is usually performed for pulmonary lesions prior to biopsy and tumor resection. Patients with a known extrapulmonary malignancy represent a diagnostic challenge, as there is currently no reliable way to non-invasively differentiate primary from secondary lung cancer. In addition, in patients with multiple SMPNs, the distinction between SMPLC and IPM could be challenging. Assuming a common immunohistochemical origin shared between primary lung cancer and associated metastases, FDG uptake intensities of related lesions may be predicted to mirror one another more closely, a finding that may lead to improved differentiation of SMPLC from IPM; however, there are limited studies described in the literature. The FDG PET imaging studies addressing these challenges will be reviewed and discussed.

Objective

The relationship between FDG uptake intensity of lung cancer and extrapulmonary tumor grade along with metastases will be reviewed and discussed, focusing on the differentiation of primary and secondary lung cancer (metastasis) as well as the differentiation of synchronous primaries and intrapulmonary metastasis. We present this article in accordance with the Narrative Review reporting checklist (available at https://ccts.amegroups.org/article/view/10.21037/ccts-23-19/rc).

Methods

An English language literature search of PubMed, Embase and Cochrane Library databases was conducted (with the study selection flow-chart depicted in Figure 1). Keywords related to differentiating primary from secondary lung cancer ((primary lung cancer) AND (secondary lung cancer) AND (FDG PET)) were searched on February 28, 2024. Keywords related to synchronous lung cancer versus metastases ((synchronous lung cancer) OR (multiple lung lesions) OR (multiple pulmonary nodules) OR (multiple lung nodules) OR (additional lung lesions) OR (synchronous primaries)) AND (FDG PET) were searched on February 17, 2024. Additional articles identified through cross-referencing were also assessed. Published original articles that met the following criteria were included:

• Differentiation of primary and secondary lung cancer:
  • Malignant lung nodule(s) representing either primary or secondary lung cancer with known outcome by histopathology or follow-up;
  • The ability of differentiating primary lung cancer from a metastasis was assessed by FDG PET/CT imaging.
• Differentiation of synchronous primaries and intrapulmonary metastasis:
  • Presence of multiple malignant lung nodules;
  • The ability of differentiating synchronous lung cancer from primary lung cancer with intrapulmonary (and possibly extrathoracic) metastasis by FDG PET/CT imaging.

Figure 1 Outline of literature search process. (A) Literature search with keywords “primary lung cancer” and “secondary lung cancer” and “FDG PET” was performed on February 28, 2024. The selection criteria included studies with patients with malignant lung nodule(s) with known extrapulmonary malignancy and the use of FDG PET to differentiate primary lung cancer from metastasis. (B) Literature search with keywords “synchronous lung cancer”, “multiple lung lesions (nodules)”, “synchronous primaries” and “FDG PET” was performed on February 17, 2024. The selection criteria included studies with patients with multiple malignant lung nodules and the use of FDG PET to differentiate synchronous primary lung cancer from primary lung cancer with intrapulmonary/extrathoracic metastasis. FDG PET, fluorodeoxyglucose positron emission tomography.

The search strategy is summarized in Table 1.

Discussion

Differentiation of primary and secondary (metastatic) lung cancer

Studies assessing the ability of ¹⁸F-FDG PET/CT imaging to reliably diagnose SMPNs as either primary lung cancer or metastatic disease from extrapulmonary origin are limited and show conflicting results, with some studies failing to show a predictive value of FDG PET imaging (38,39) and others finding FDG uptake intensity of the lung lesion to be helpful (33,40-44). A brief summary of studies (all retrospective in nature) and key findings has been prepared (Table 2). Taralli et al. failed to observe any significant difference in SUV_max and clinico-anatomical imaging features between SMPNs representing either metachronous lung cancer (103 patients; median SUV_max =5.2) and metastasis (24 patients; median SUV_max =5.9) (38). Their study, however, did not examine the relationship between tumor grade of the extrapulmonary cancer and FDG uptake intensity of the SMPN. Similarly, Guo et al. failed to demonstrate a significant difference in SUV_max measurements between solitary pulmonary nodules (SPNs) representing either primary (n=43) or secondary (n=77) lung cancer in a group of colorectal cancer patients, citing specific morphological features, including ground glass opacity (GOO) appearance, pleural tags, and circular contour, as independent predictors in their diagnostic model (39). On the contrary, Zhu et al. studied 52 patients with a history of breast cancer presenting with pulmonary nodules and found that lung lesions with negative (cold) SUV_max measurements, along with lesions without FDG-avid mediastinal or hilar lymph nodes and pleural metastases, were more likely to be associated with primary lung cancer (33). However, the results of the study are limited by the small sample size that is heavily biased toward low grade adenocarcinoma demonstrating minimal FDG uptake in the primary lung malignancy group (where 18/22 patients in the group had primary lung adenocarcinoma). In a study examining differences in FDG uptake between primary adenoid cystic carcinoma (ACC) of the lung (n=29) and secondary ACC originating from head and neck malignancies (n=11), significantly higher SUV_max measurements were reported in the primary cancer group (median SUV_max =4.4 vs. 2.8, P<0.05), but the clinical utility of these findings are limited by the extremely low incidence of ACC of the lungs in the general population when compared to other histological subtypes of lung cancer (40). Focusing on adenocarcinoma, specifically from the perspective of dual-time-point (DTP) ¹⁸F-FDG PET/CT imaging, and comparing FDG uptake between primary (n=52) and metastatic (n=44) lung adenocarcinoma from various origins [with colorectal cancer being the most common (n=26)], one study observed significantly higher SUV_max (early 5.73 vs. 4.62, P<0.001; delayed 7.04 vs. 5.2, P<0.001) and retention index (RI) (21.75% vs. 11.24%, P<0.001) in the metastatic group (42).

Speculating that the FDG uptake of a metastatic lung lesion may mirror that of the extrapulmonary tumour due to a shared clonal origin (and therefore, implying that both lesions are of comparable tumour grade and aggressiveness), a study was performed in patients with SMPNs representing pathology-proven primary or secondary lung cancer with known histopathology of the extrapulmonary tumor (44). The study included 62 patients (46 primary and 16 secondary lung cancer patients). Extrapulmonary tumors were subdivided into low [19], intermediate [27] and high [16] tumor grade groups, and correlated with pulmonary lesion FDG uptake intensity to identify the “mismatched” group (e.g., low FDG lung lesion uptake with intermediate or high grade extrapulmonary tumor or vice-versa). The study was able to successfully predict 81% of cases of primary lung cancer in the “mismatched” group (n=37), supporting the notion that a discrepancy (or discordant findings) between FDG uptake of the SMPN and extrapulmonary tumour grade may be useful in inferring the presence of a primary lung malignancy (44). Thus, clinically correlating FDG uptake with extrapulmonary tumour grade may provide incremental gain in diagnostic accuracy in the differentiation of primary and secondary lung cancer than examining absolute lesional FDG uptake in isolation. A limitation of this study was that the tumor grade was obtained from pathology reports without incorporating information regarding the presence of lymphovascular invasion, tumor proliferation markers, hormonal status and other biological/immunological markers, which was not available in most patients. A future study design should include more extensive lesion characterization in terms of additional markers of tumour aggressiveness through collaboration with pathologists to improve diagnostic accuracy and confirm the reported outcome.

Research into the use of radiomics to extract pertinent imaging features during FDG PET or CT imaging is rapidly evolving. The use of radiomics to objectively distinguish between primary and metastatic SMPNs has shown promising results. By extracting radiomics features from CT and FDG PET datasets of 534 patients with lung lesions, one study was able to differentiate primary tumors from metastases with a high degree of accuracy using FDG PET features in particular (with AUCs of 0.70–0.79 from the CT datasets and AUCs of 0.91–0.92 from the PET datasets) (41). In another study involving 769 patients with pathology-proven primary or metastatic lung cancer, different discriminative models were developed following radiomic feature extraction (45). In differentiating primary from metastatic lung lesions, optimal feature selection resulted in the highest AUC of 0.828 in the CT dataset and the highest AUC of 0.983 in the PET dataset, the latter corresponding to an extremely high accuracy difficult to achieve with other methodology.

Findings involving FDG PET may be further combined with CT imaging features that have been shown to be potentially useful in the differentiation of primary and metastatic lung cancer to further increase the index of suspicion for one or the other. The presence of lesions demonstrating imaging evidence of cavitation/necrosis, as well as the presence of multiple pulmonary nodules of larger size (>5 mm) have been found to be independent predictors of metastatic disease on multivariate analysis (46). In addition, in patients with colorectal cancer presenting with an SPN, lesions displaying spiculation, sub-solid density, and an air bronchogram were all correlated with an increased likelihood of primary lung cancer, whereas metastatic lesions are known to more likely present as solid, round lesions in the periphery (47). Integrating clinical information, one study found that centrally located lesions, longer event-free duration following radical treatment of the extrapulmonary tumour, and lower initial extrapulmonary tumour stage were more likely to be associated with primary lung cancer (48).

Differentiation of synchronous primaries and intrapulmonary metastasis

¹⁸F-FDG PET/CT imaging may be useful in the differentiation of synchronously presenting lesions in a non-invasive manner; specifically, when a larger inter-lesional difference in FDG uptake is detected, it may be suggestive of SMPLC involving lesions of differing histopathological subtype, grade, and/or proliferation rate. Representative cases representing SMPLC and IPM with imaging results and FDG uptake values are shown in Figure 2 [reproduced from Karpinski et al. (49) with permission]. All studies included are retrospective in nature except one (50), with key findings summarized and presented in Table 3.

Figure 2 Examples of maximum intensity projection FDG PET images. (A) A 62 years old male with biopsy proven SCC in the RUL and adenocarcinoma in the LLL (Synchronous group). (B) A 78 years old female with two hypermetabolic lung lesions status post LUL lobectomy with pT3pN0 staging (Metastasis group). Reproduced from Fig. 2 from Karpinski et al. (49). SUV_max, maximum standardized uptake value; FDG PET, fluorodeoxyglucose positron emission tomography; SCC, squamous cell carcinoma; RUL, right upper lobectomy; LLL, left lower lobectomy; LUL, left upper lobectomy.

The first study that investigated the possible utility of ¹⁸F-FDG PET/CT imaging in differentiating synchronous primary lung cancer from metastatic disease was carried out by Dijkman et al. in 2010, which found a significantly higher difference in relative SUV_max {defined as [(larger SUV_max − smaller SUV_max)/larger SUV_max] × 100%} amongst a group of 16 synchronous primary cancer patients relative to 21 metastatic disease patients (58% vs. 28% respectively, P<0.001; optimal cut-off value =41%) (51). In their study, the metastatic patient cohort included patients with distal metastases from lung cancer and extrapulmonary primary metastases to the lungs. A similar study carried out more recently determined an optimal cut-off value of 35% (P<0.001) among a group of 18 synchronous primary tumour and 47 metastatic disease cases (52). Assessing 19 patients with histopathologically-confirmed synchronous multiple primary cancer (SMPC) involving different organ systems using Dijkman et al.’s suggested relative ΔSUV_max cut-off value of 41% for classifying synchronous primary cancer cases, Pang et al. was able to correctly classify 14 cases (corresponding to an accuracy of 73.7%) (53). In addition, while analyzing variability in FDG uptake among 296 metastatic lesions in 75 patients with primary lung malignancy, Kosaka et al. arrived at a similar conclusion in terms of observing a stronger correlation in FDG uptake between lesions related by metastasis, where >85% of metastatic lesions were found to fall within the range of 50–200% in terms of SUV_max ratio (defined as metastatic lesion SUV_max/primary lesion SUV_max), implying that synchronous primary malignancy was more probable if the difference in FDG uptake fell outside of this range (54). The results of such studies are further supported by a more recent study carried out by Luo et al. involving a cohort of 37 patients presenting with SMPC, with calculations of average relative ΔSUV_max (50.3%±29.3%) and SUV_max ratio (defined as larger SUV_max/primary lesion SUV_max) (4.4±6.9) meeting recommended cut-off values previously suggested (55). Nonetheless, all of these studies mentioned included patients with extrapulmonary tumours as part of their analysis, which limits the conclusions that can be draw when it comes to differentiating SMPLC and IPM specifically.

In classifying pairs of nodules in 59 SMPLC cases and 23 IPM cases using SUV_max ratio (defined as larger SUV_max/smaller SUV_max), Liu et al. found a significant difference between the two groups (2.3±1.6 vs. 1.5±0.4 respectively; P<0.01), determining an optimal cut-off value of 1.7 for differentiation (56). Another study, which had a disproportionately higher number of adenocarcinoma cases among both groups, was able to find a significant difference (P<0.001) in absolute SUV_max (7.53±4.33) in the second primary tumour group relative to the metastasis group (4.35±2.58), which suggests that even absolute differences in FDG uptake could potentially be used to infer the presence of SMPLC involving adenocarcinomas of different pathological subtype (57). Nonetheless, using dynamic ¹⁸F-FDG PET/CT imaging to look for differences in SUV_max (measuring dynamic parameter K_i and net influx rate) normalized to maximum diameter (D_max) measurements involving the two largest lesions in each patient, Lv et al. did not find a significant difference between SMPLC (n=19) and IPM (n=24) groups in terms of SUV_max/D_max (50). They did, however, find a good correlation involving both parameters (SUV_max/D_max and K_i/D_max) in the IPM group (Pearson r=0.91, 95% CI: 0.79–0.96, P<0.0001), but not the SMPLC group (Pearson r=0.45, P>0.05), suggesting that lesions of common clonal origin (metastases) demonstrate more predictable patterns of FDG uptake relative to tumours of distinct clonal origin (synchronous primaries). This correlational finding in FDG uptake in the setting of IPM has been further confirmed by a more recent study, which showed a strong correlation in lesional FDG uptake between lesions in the IPM group (n=32; Pearson r=0.81) as compared to the SMPLC group (n=62; Spearman ρ=0.53) (49). Furthermore, the study builds upon and adds additional support to the findings from previous studies, observing a significantly higher mean relative ΔSUV_max (synchronous primary: 0.50±0.23; metastasis: 0.34±0.17; P<0.001) and mean SUV_max ratio (synchronous: 2.6±1.7; metastasis: 1.7±0.6; P=0.001) in the SMPLC group, with optimal cut-off values of 1.85 (AUC =0.72) and 0.41 (AUC =0.71) reported, respectively (49).

Since FDG uptake is theorized to correlate with tumour aggressiveness, similarities in FDG uptake may also be found when comparing tumours of different clonal origin (and either similar or different histological subtype) that show similar metabolic behaviour; thus, it is likely that larger differences in SUV_max (suggesting the presence of synchronous primary cancers) may confer a greater diagnostic benefit than comparatively smaller differences (where the distinction between synchronous primary cancers and metastatic disease is far less clear). Consequently, the findings reported by studies in the literature and their clinical utility are likely limited by this inherent nature of uncertainty when FDG uptake is similar between multiple-presenting lesions.

Ultimately, the main clinical implication that can be derived from these studies is that substantially larger differences in FDG uptake between pulmonary nodules should increase the clinician’s suspicion of SMPLC consistent with the presence of lesions of divergent clonal origin.

Assuming that cases involving multiple lesions where one or more nodules possess pure ground glass or opacity predominant part-solid appearance likely represent SMPLC, morphological features of lung nodules on CT could be useful for differentiating between multiple primary lung cancers and intrapulmonary metastasis. However, the value of preoperative CT findings for differentiating between SMPLC and IPM in patients with multiple malignant lesions has been rarely investigated (36,58-61). One previous study by Hattori et al. showed that the combination of the same CT lesion types (pure solid nodule + pure solid nodule) was associated with worse survival compared to other combinations in surgically resected stage I multiple lung cancers, and no IPM was present in combinations containing at least one pure ground glass nodule or opacity predominant part-solid nodule within a pair in their study population (58). In a recent study assessing CT and FDG PET features in 126 patients with multiple malignant lung lesions, Suh et al. proposed a diagnostic four-step algorithm based on morphological features, FDG uptake, and tumor grade for differentiating between SMPLC and IPM (59). In step one, 67 pairs showing at least one pure or opacity predominant part-solid nodule were classified as SMPLC with an accuracy rate of 79%. The subsequent steps involved features of spiculation and air-bronchogram presence on CT (favouring SMPLC), FDG uptake intensity (SUV_max difference ≥5.0 favouring SMPLC) and regional/distal metastases (favouring IPM). With the proposed algorithm, PET was only diagnostic in 3 of 13 patients assessed with a positive predictive value of 66.7%. Although the authors claimed having a diagnostic accuracy of 89% (with inconclusive findings in 8 patients), the diagnostic accuracy would be much lower if the study was limited to pure solid pulmonary nodules, which are the most challenging to diagnose.

Conclusions

Based on the review of limited studies in the literature, profoundly larger differences in FDG avidity in lung nodules should raise suspicion of synchronous lung primaries over IPM. In solitary pulmonary nodule patients with known extrapulmonary malignancy, tumor grade may be helpful to differentiate primary lung cancer from a distal metastasis. Although differentiating primary from secondary lung cancer or synchronous lung cancer from intrapulmonary metastasis non-invasively is challenging with current imaging modalities, some progress has been made to address these challenges by considering the association between FDG uptake intensity of lung cancer and tumor grade, and the similarity of FDG uptake between primary lung cancer and its metastases. Main limitations include retrospective study design, without thorough assessment of tissue specimens, and relatively small sample size. Future studies, with prospective study design involving closer collaboration with pathologists with full lesional characterization, are needed to validate the current findings and improve diagnostic accuracy.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the Guest Editors (Donna E. Maziak and Patrick J. Villeneuve) for the series “Comprehensive Lung Cancer Care: A Continuum” published in Current Challenges in Thoracic Surgery. The article has undergone external peer review.

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://ccts.amegroups.org/article/view/10.21037/ccts-23-19/rc

Peer Review File: Available at https://ccts.amegroups.org/article/view/10.21037/ccts-23-19/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://ccts.amegroups.org/article/view/10.21037/ccts-23-19/coif). The series “Comprehensive Lung Cancer Care: A Continuum” was commissioned by the editorial office without any funding or sponsorship. The authors have no other 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.

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/.

References

1. Kitajima K, Doi H, Kanda T, et al. Present and future roles of FDG-PET/CT imaging in the management of lung cancer. Jpn J Radiol 2016;34:387-99. [Crossref] [PubMed]
2. Kim AW, Cooke DT. Additional pulmonary nodules in the patient with lung cancer: controversies and challenges. Clin Chest Med 2011;32:811-25. [Crossref] [PubMed]
3. Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med 2007;48:932-45. [Crossref] [PubMed]
4. Ettinger DS, Wood DE, Aisner DL, et al. NCCN Guidelines® Insights: Non-Small Cell Lung Cancer, Version 2.2023. J Natl Compr Canc Netw 2023;21:340-50. [Crossref] [PubMed]
5. Tang K, Wang L, Lin J, et al. The value of 18F-FDG PET/CT in the diagnosis of different size of solitary pulmonary nodules. Medicine (Baltimore) 2019;98:e14813. [Crossref] [PubMed]
6. Almuhaideb A, Papathanasiou N, Bomanji J. 18F-FDG PET/CT imaging in oncology. Ann Saudi Med 2011;31:3-13. [Crossref] [PubMed]
7. Huang YE, Huang YJ, Ko M, et al. Dual-time-point (18)F-FDG PET/CT in the diagnosis of solitary pulmonary lesions in a region with endemic granulomatous diseases. Ann Nucl Med 2016;30:652-8. [Crossref] [PubMed]
8. Sai KKS, Zachar Z, Bingham PM, et al. Metabolic PET Imaging in Oncology. AJR Am J Roentgenol 2017;209:270-6. [Crossref] [PubMed]
9. Petrella F. Diagnosis and Treatment of Primary and Secondary Lung Cancers. Cancers (Basel) 2021;13:448. [Crossref] [PubMed]
10. Welter S, Arfanis E, Christoph D, et al. Growth patterns of pulmonary metastases: should we adjust resection techniques to primary histology and size? Eur J Cardiothorac Surg 2017;52:39-46. [Crossref] [PubMed]
11. Feng SH, Yang ST. The new 8th TNM staging system of lung cancer and its potential imaging interpretation pitfalls and limitations with CT image demonstrations. Diagn Interv Radiol 2019;25:270-9. [Crossref] [PubMed]
12. Zhao L, Liu C, Xie G, et al. Multiple Primary Lung Cancers: A New Challenge in the Era of Precision Medicine. Cancer Manag Res 2020;12:10361-74. [Crossref] [PubMed]
13. Smith NE, Harris ES, Gallant BP, et al. Unexpected encounters: high prevalence of synchronous primary lung cancers in a tertiary academic center. J Thorac Dis 2020;12:2082-7. [Crossref] [PubMed]
14. Martini N, Melamed MR. Multiple primary lung cancers. J Thorac Cardiovasc Surg 1975;70:606-12. [Crossref] [PubMed]
15. Inamura K. Update on Immunohistochemistry for the Diagnosis of Lung Cancer. Cancers (Basel) 2018;10:72. [Crossref] [PubMed]
16. van Rens MT, Eijken EJ, Elbers JR, et al. p53 mutation analysis for definite diagnosis of multiple primary lung carcinoma. Cancer 2002;94:188-96. [Crossref] [PubMed]
17. Vokes NI, Zhang J. The Role of Whole Exome Sequencing in Distinguishing Primary and Secondary Lung Cancers. Lung Cancer (Auckl) 2021;12:139-49. [Crossref] [PubMed]
18. Girard N, Deshpande C, Lau C, et al. Comprehensive histologic assessment helps to differentiate multiple lung primary nonsmall cell carcinomas from metastases. Am J Surg Pathol 2009;33:1752-64. [Crossref] [PubMed]
19. Niyonkuru A, Chen X, Bakari KH, et al. Evaluation of the diagnostic efficacy of 18F‐Fluorine‐2‐Deoxy‐D‐Glucose PET/CT for lung cancer and pulmonary tuberculosis in a Tuberculosis‐endemic Country. Cancer Med. 2019;9:931-42. [Crossref] [PubMed]
20. Kwee TC, Cheng G, Lam MG, et al. SUVmax of 2.5 should not be embraced as a magic threshold for separating benign from malignant lesions. Eur J Nucl Med Mol Imaging 2013;40:1475-7. [Crossref] [PubMed]
21. Patz EF Jr, Lowe VJ, Hoffman JM, et al. Focal pulmonary abnormalities: evaluation with F-18 fluorodeoxyglucose PET scanning. Radiology 1993;188:487-90. [Crossref] [PubMed]
22. Beggs AD, Hain S, Curran K, O’Doherty M. FDG-PET as a “metabolic biopsy” tool in non-lung lesions with indeterminate biopsy. Eur J Nucl Med Mol Imaging 2002;29:542-6. [Crossref] [PubMed]
23. Higashi K, Ito K, Hiramatsu Y, et al. 18F-FDG uptake by primary tumor as a predictor of intratumoral lymphatic vessel invasion and lymph node involvement in non-small cell lung cancer: analysis of a multicenter study. J Nucl Med 2005;46:267-73. [PubMed]
24. Noda Y, Goshima S, Kanematsu M, et al. F-18 FDG uptake on positron emission tomography as a predictor for lymphovascular invasion in patients with lung adenocarcinoma. Ann Nucl Med 2016;30:11-7. [Crossref] [PubMed]
25. Higashi K, Ueda Y, Ikeda R, et al. P-glycoprotein expression is associated with FDG uptake and cell differentiation in patients with untreated lung cancer. Nucl Med Commun 2004;25:19-27. [Crossref] [PubMed]
26. Schöder H, Noy A, Gönen M, et al. Intensity of 18fluorodeoxyglucose uptake in positron emission tomography distinguishes between indolent and aggressive non-Hodgkin's lymphoma. J Clin Oncol 2005;23:4643-51. [Crossref] [PubMed]
27. Bruzzi JF, Macapinlac H, Tsimberidou AM, et al. Detection of Richter's transformation of chronic lymphocytic leukemia by PET/CT. J Nucl Med 2006;47:1267-73. [PubMed]
28. Thuillier P, Liberini V, Rampado O, et al. Diagnostic Value of Conventional PET Parameters and Radiomic Features Extracted from 18F-FDG-PET/CT for Histologic Subtype Classification and Characterization of Lung Neuroendocrine Neoplasms. Biomedicines 2021;9:281. [Crossref] [PubMed]
29. Liu X, Zou Q, Sun Y, et al. Role of multiple dual-phase (18)F-FDG PET/CT metabolic parameters in differentiating adenocarcinomas from squamous cell carcinomas of the lung. Heliyon 2023;9:e20180. [Crossref] [PubMed]
30. Kaseda K. Recent and Current Advances in FDG-PET Imaging within the Field of Clinical Oncology in NSCLC: A Review of the Literature. Diagnostics (Basel) 2020;10:561. [Crossref] [PubMed]
31. Bianchetti G, Taralli S, Vaccaro M, et al. Automated detection and classification of tumor histotypes on dynamic PET imaging data through machine-learning driven voxel classification. Comput Biol Med 2022;145:105423. [Crossref] [PubMed]
32. Vesselle H, Salskov A, Turcotte E, et al. Relationship between non-small cell lung cancer FDG uptake at PET, tumor histology, and Ki-67 proliferation index. J Thorac Oncol 2008;3:971-8. [Crossref] [PubMed]
33. Zhu L, Bian H, Yang L, et al. 18 Fluorodeoxyglucose-positron emission tomography/computed tomography features of suspected solitary pulmonary lesions in breast cancer patients following previous curative treatment. Thorac Cancer 2019;10:1086-95. [Crossref] [PubMed]
34. Sunnetcioglu A, Arısoy A, Demir Y, et al. Associations between the standardized uptake value of (18)F-FDG PET/CT and demographic, clinical, pathological, radiological factors in lung cancer. Int J Clin Exp Med 2015;8:15794-800. [PubMed]
35. Sun XY, Chen TX, Chang C, et al. SUVmax of (18)FDG PET/CT Predicts Histological Grade of Lung Adenocarcinoma. Acad Radiol 2021;28:49-57. [Crossref] [PubMed]
36. Chen TF, Yang L, Chen HB, et al. A pairwise radiomics algorithm-lesion pair relation estimation model for distinguishing multiple primary lung cancer from intrapulmonary metastasis. Precis Clin Med 2023;6:pbad029. [Crossref] [PubMed]
37. Han Y, Ma Y, Wu Z, et al. Histologic subtype classification of non-small cell lung cancer using PET/CT images. Eur J Nucl Med Mol Imaging 2021;48:350-60. [Crossref] [PubMed]
38. Taralli S, Scolozzi V, Triumbari EK, et al. Is 18F-fluorodeoxyglucose positron emission tomography/computed tomography useful to discriminate metachronous lung cancer from metastasis in patients with oncological history? Q J Nucl Med Mol Imaging 2020;64:291-8. [Crossref] [PubMed]
39. Guo R, Yan S, Wang F, et al. A novel diagnostic model for differentiation of lung metastasis from primary lung cancer in patients with colorectal cancer. Front Oncol 2022;12:1017618. [Crossref] [PubMed]
40. Sun X, Gu W, Yuan H, et al. Clinical use of (18)F-FDG PET/CT in the differential diagnosis of patients with primary and secondary adenoid cystic carcinoma of the lung: a retrospective cohort study. Transl Lung Cancer Res 2022;11:1643-56. [Crossref] [PubMed]
41. Kirienko M, Cozzi L, Rossi A, et al. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging 2018;45:1649-60. [Crossref] [PubMed]
42. Pahk K, Chung JH, Kim S, et al. Predictive value of dual-time (18)F-FDG PET/CT to distinguish primary lung and metastatic adenocarcinoma in solitary pulmonary nodule. Tumori 2018;104:207-12. [Crossref] [PubMed]
43. Zhou Y, Ma XL, Zhang T, et al. Use of radiomics based on (18)F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur J Nucl Med Mol Imaging 2021;48:2904-13. [Crossref] [PubMed]
44. Ghossein J, Gingras S, Zeng W. Differentiating primary from secondary lung cancer with FDG PET/CT and extra-pulmonary tumor grade. J Med Imaging Radiat Sci 2023;54:451-6. [Crossref] [PubMed]
45. Zhou Y, Ma XL, Zhang T, et al. Use of radiomics based on (18)F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur J Nucl Med Mol Imaging 2021;48:2904-13. [Crossref] [PubMed]
46. Caparica R, Mak MP, Rocha CH, et al. Pulmonary Nodules in Patients With Nonpulmonary Cancer: Not Always Metastases. J Glob Oncol 2016;2:138-44. [Crossref] [PubMed]
47. Lee JE, Jeong WG, Kim YH. Differentiation of primary lung cancer from solitary lung metastasis in patients with colorectal cancer: a retrospective cohort study. World J Surg Oncol 2021;19:28. [Crossref] [PubMed]
48. Ge J, Gou HF, Chen Y, et al. Clinical characteristics of patients with solitary pulmonary mass after radical treatment for primary cancers: pulmonary metastasis or second primary lung cancer? Cancer Invest 2013;31:397-403. [Crossref] [PubMed]
49. Karpinski S, Al Bimani Z, Dobson JL, et al. FDG uptake of pulmonary lesions in synchronous primary lung cancers and lung metastases. Research in Diagnostic and Interventional Imaging. 2024;9:100041. [Crossref]
50. Lv W, Yang M, Zhong H, et al. Application of Dynamic (18)F-FDG PET/CT for Distinguishing Intrapulmonary Metastases from Synchronous Multiple Primary Lung Cancer. Mol Imaging 2022;2022:8081299. [Crossref] [PubMed]
51. Dijkman BG, Schuurbiers OC, Vriens D, et al. The role of (18)F-FDG PET in the differentiation between lung metastases and synchronous second primary lung tumours. Eur J Nucl Med Mol Imaging 2010;37:2037-47. [Crossref] [PubMed]
52. Aboukhadrah RS, Sherbash GS. Role of PET/CT in Differentiating Synchronous Second Primary Lung Masses from Lung Metastases. The Medical Journal of Cairo University 2020;88:2091-2102. [Crossref]
53. Pang L, Liu G, Shi H, et al. Nineteen cases with synchronous multiple primary cancers studied by (18)F-FDG PET/CT. Hell J Nucl Med 2017;20:36-40. [PubMed]
54. Kosaka N, Tsuchida T, Tsuji K, et al. Standardized uptake value differences between primary and metastatic lesions in ¹⁸F-FDG PET/CT of patients with lung cancer. Acta Radiol 2015;56:1329-35. [Crossref] [PubMed]
55. Luo ZH, Qi WL, Jin AF, et al. The role of (18)F-FDG PET/CT in patients with synchronous multiple primary malignant neoplasms occurring at the same time. Front Oncol 2022;12:1068055. [Crossref] [PubMed]
56. Liu Y, Tang Y, Xue Z, et al. SUVmax Ratio on PET/CT May Differentiate Between Lung Metastases and Synchronous Multiple Primary Lung Cancer. Acad Radiol 2020;27:618-23. [Crossref] [PubMed]
57. Huy HQ. Comparing the SUVmax between lung metastases and synchronous second primary lung tumours in patients with non-small cell lung cance. Biomedical Research 2018;29:3842-6.
58. Hattori A, Matsunaga T, Takamochi K, et al. Radiological classification of multiple lung cancers and the prognostic impact based on the presence of a ground glass opacity component on thin-section computed tomography. Lung Cancer 2017;113:7-13. [Crossref] [PubMed]
59. Suh YJ, Lee HJ, Sung P, et al. A Novel Algorithm to Differentiate Between Multiple Primary Lung Cancers and Intrapulmonary Metastasis in Multiple Lung Cancers With Multiple Pulmonary Sites of Involvement. J Thorac Oncol 2020;15:203-15. [Crossref] [PubMed]
60. Han X, Fan J, Liu T, et al. Differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis by CT features, EGFR mutations and ALK rearrangement status. J Thorac Dis 2020;12:5505-16. [Crossref] [PubMed]
61. Zhang Y, Li G, Li Y, et al. Imaging Features Suggestive of Multiple Primary Lung Adenocarcinomas. Ann Surg Oncol 2020;27:2061-70. [Crossref] [PubMed]