Lung cancer is one of the most frequent malignancies in humans [
Multiple histopathological factors influence tumor biology in lung cancer. According to the literature, different molecular markers play a key role here [
Furthermore, some reports analyzed associations between imaging parameters and histopathological features in lung cancer [
The purpose of this meta-analysis was to provide evident data about associations between SUV and histopathological parameters in lung cancer.
The strategy of data acquisition is shown in Figure
Flowchart of the data acquisition.
For associations between PET and different biomarkers, the following search words were used:
Secondary references were also recruited. Overall, 802 records were identified. After exclusion of doublets, review articles, case reports, non-English publications, and articles, which not contain correlation coefficients between PET and histopathological parameters, there were 40 articles [
The following data were extracted from the literature: authors, year of publication, number of patients, histopathological parameters, and correlation coefficients, according to our previous descriptions [
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) was used for the research [
The methodological quality of the acquired 40 studies was independently checked by two observers (Alexey Surov and Hans Jonas Meyer) using the Quality Assessment of Diagnostic Studies (QUADAS) instrument according to previous descriptions [
Methodological quality of the involved 40 studies according to the QUADAS criteria.
QUADAS criteria | Yes (%) | No (%) | Unclear (%) |
---|---|---|---|
Patient spectrum | 38 (95.0) | — | 2 (5.0) |
Selection criteria | 28 (70.0) | 1 (2.50) | 11 (27.5) |
Reference standard | 40 (100) | — | — |
Disease progression bias | 40 (100) | — | — |
Partial verification bias | 40 (100) | — | — |
Differential verification bias | 40 (100) | — | — |
Incorporation bias | 40 (100) | — | — |
Text details | 40 (100) | — | — |
Reference standard details | 40 (100) | — | — |
Text review details | 16 (40.0) | 4 (10.0) | 20 (50.0) |
Diagnostic review bias | 17 (42.5) | 4 (10.0) | 19 (47.5) |
Clinical review bias | 39 (97.5) | — | 1 (2.5) |
Uninterpretable results | 39 (97.5) | — | 1 (2.5) |
Withdrawal explained | 38 (95.0) | 1 (2.5) | 1 (2.5) |
Associations between PET and histopathological findings were analyzed by Spearman’s correlation coefficient. The reported Pearson’s correlation coefficients in some studies were converted into Spearman’s correlation coefficients according to the previous description [
Furthermore, the meta-analysis was undertaken by using RevMan 5.3 (Computer Program, version 5.3, The Cochrane Collaboration, 2014, The Nordic Cochrane Centre, Copenhagen). Heterogeneity was calculated by means of the inconsistency index
Associations between 18F-FDG PET and KI 67 were reported in 23 studies (1362 patients) [
Forest plots of correlation coefficients between SUVmax and KI 67 in patients with lung cancer.
In 2 studies (180 patients), relationships between 18F-FDG PET and expression of cyclin D1 were analyzed [
Forest plots of correlation coefficients between SUVmax and expression of cyclin D1.
Associations between 18F-FDG PET and HIF-1
Forest plots of correlation coefficients between SUVmax and expression of HIF-1
Associations between 18F-FDG PET and MVD were investigated in 5 studies (310 patients) [
Forest plots of correlation coefficients between SUVmax and microvessel density.
In 6 studies (305 patients), relationships between 18F-FDG PET and p53 were analyzed [
Forest plots of correlation coefficients between SUVmax and expression of p53.
There were 6 studies (415 patients) which investigated associations between SUV and expression of VEGF in lung cancer [
Forest plots of correlation coefficients between SUVmax and VEGF expression.
There were 5 studies (202 patients) which investigated associations between 18F-FDG PET and PCNA in lung cancer [
Forest plots of correlation coefficients between SUVmax and PCNA.
There were 5 studies (409 patients) which investigated associations between 18F-FDG PET and expression of EGFR in lung cancer [
Forest plots of correlation coefficients between SUVmax and EGFR expression.
In 3 studies (718 patients), relationships between 18F-FDG PET and expression of PD L1 were analyzed [
Forest plots of correlation coefficients between SUVmax and EGFR expression.
Analysis of interactions between imaging findings, in particular, between PET and histopathology can significantly improve oncologic diagnostics [
Our meta-analysis showed that SUV can reflect different histopathological parameters in lung cancer. As shown, SUV correlated moderately with KI 67. This finding is not surprisingly. KI 67 is a nonhistone, nuclear protein synthesized throughout the whole cell cycle except the G0 phase and has been shown to be responsible for cell proliferation [
Similarly, our analysis found only slight correlation between SUVmax and expression of EGFR (0.38). EGFR is a cell membrane tyrosine kinase receptor [
Furthermore, we analyzed associations between SUVmax and expression of p53. As seen, these parameters correlate weakly (0.30). According to the literature, p53 is a protein encoded by the TP53 gene and plays a key role in tumor suppression and in the cellular response to DNA damage [
Programmed cell death-ligand 1 or PD L1 is another very important biomarker in lung cancer [
Our analysis also showed that SUVmax cannot predict expression of cyclin D1 in lung cancer. As reported previously, data of the role of this protein are inconsequent. For example, Gautschi et al. found a strong pathological role for cyclin D1 deregulation in bronchial neoplasia [
The present meta-analysis identified a moderate pooled correlation between SUVmax and hypoxia-inducible factor-1 alpha (HIF-1
Similarly, we calculated a moderate pooled correlation between SUVmax and expression of VEGF. Previous reports indicated that VEGF overexpression is associated with poor prognosis for NSCLC patients [
Finally, the strongest correlation was found between SUVmax and microvessel density (0.54). This finding seems to be logical. In fact, high metabolic activity may induce a high perfusion, which is associated with more vessels. SUV may identify hypervascularized tumor areas. Therefore, SUV may be used for evaluation of response to therapy with angiogenesis inhibitors.
The present meta-analysis also identified several other problems. Overall, most analyzed biomarkers are associated with SUV. This finding suggests that SUVmax may reflect different histopathological features in lung cancer. However, as mentioned above, the calculated pooled correlations are slightly-to-moderate. Therefore, our analysis showed that SUVmax cannot be used as an ultimate one-to-one surrogate marker for different receptor expressions in lung cancer.
Some reports suggested that other PET parameters like metabolic tumor volume or total lesion glycolysis are more sensitive than SUVmax [
Furthermore, lung cancer involves several carcinomas with different histopathological features and behavior. Presumably, different subtypes of lung cancer may have also different associations between PET and histopathology. This question should also be analyzed by further investigations.
There were also other problems. Only 40 reports with small number of patients investigated associations between PET parameters and histopathological features in lung cancer. Furthermore, most of the acquired studies were retrospective. Finally, according the QUADAS criteria, all involved studies showed partial verification bias, differential verification bias, and incorporation bias. Also, most of the studies had clinical review bias and diagnostic review bias. Clearly, further prospective studies with more patients are needed to investigate associations between PET and histopathology in lung cancer.
Some recent reports indicated that other histopathological markers like tumor-infiltrating CD8-positive T lymphocytes, cyclooxygenase-2, and survivin play also a great role in lung cancer [
In conclusion, our meta-analysis showed that SUVmax may predict microvessel density and expression of VEGF, KI 67, and HIF-1
The data used to support the findings of this study are available from the corresponding author upon request.
The study was approved by the institutional review board of the University of Leipzig. All procedures performed in the study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
There are no conflicts of interest.
Alexey Surov conceptualized the data. Alexey Surov, Hans Jonas Meyer, and Andreas Wienke performed data curation. Alexey Surov, Hans Jonas Meyer, and Andreas Wienke did formal analysis. Alexey Surov, Hans Jonas Meyer, and Andreas Wienke investigated the data. Alexey Surov, Hans Jonas Meyer, and Andreas Wienke designed the methodology. Alexey Surov administered the project