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Nuclear Medicine Communications:
doi: 10.1097/MNM.0b013e3283599999
Original Articles

18F-FDG-PET evaluation of pathological tumour response to neoadjuvant therapy in patients with NSCLC

Zhang, Chenpenga; Liu, Jianjuna; Tong, Jinlub; Sun, Xiaoguanga; Song, Shaolia; Huang, Ganga,c

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aDepartment of Nuclear Medicine

bDepartment of Gastroenterology, Shanghai Institute of Gastrointestinal Diseases, Renji Hospital, Shanghai Jiaotong University School of Medicine

cInstitute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, China

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Website (www.nuclearmedicinecomm.com).

Chenpeng Zhang and Jianjun Liu contributed equally to the writing of this article.

Correspondence to Gang Huang, Department of Nuclear Medicine, Renji Hospital, Shanghai Jiaotong University School of Medicine, 200127 Shanghai, China Tel: +86 021 68383497; fax: +86 021 68383116; e-mail: huang2802@163.com

Received May 25, 2012

Accepted August 22, 2012

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Abstract

Objectives: The ability to identify potential responders to neoadjuvant treatment may improve patient selection or surgery and may help in the development of response criteria suitable for routine monitoring of response. The aim of this study was to evaluate the value of PET in predicting the pathological tumour response of non-small-cell lung cancer (NSCLC) to neoadjuvant therapy using a meta-analysis.

Methods: All available published studies investigating the value of PET in predicting the pathological response of NSCLC to neoadjuvant therapy were collected. Pooled sensitivity and specificity data were obtained using statistical software. Subgroup analysis was performed to explore the sources of heterogeneity.

Results: A total of 13 studies comprising 414 patients with NSCLC were included in the meta-analysis. Pooled sensitivity, specificity, positive predictive value and negative predictive value for PET-predicted response was 83% [95% confidence interval (CI); 76–89%], 84% (95% CI; 79–88%), 74% (95% CI; 67–81%) and 91% (95% CI; 87–94%), respectively. Significant heterogeneity (P<0.05) was observed. On the basis of our subgroup analyses, methodological quality could be responsible for this heterogeneity in our metaregression. The predictive value of PET in NSCLC patients with pathological response (considered the gold standard) was significantly higher than that of computed tomography (P<0.05).

Conclusion: PET scanning has an important role in predicting nonresponders to neoadjuvant therapy in cases of NSCLC, and the predictive value of PET for evaluating pathologically documented responses is superior to that of computed tomography. However, additional evaluations using prospective clinical trials will be required to assess the clinical benefit of this strategy.

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Introduction

Non-small-cell lung cancer (NSCLC) accounts for 75–80% of all lung cancers 1. Surgical resection might be the best option for patients with NSCLC. Unfortunately, advanced disease [stage IIIA (bulky disease)–IV] is usually considered inoperable. Only when significant tumour responses to adjuvant treatment are achieved can patients experience a significant survival advantage from undergoing surgery; otherwise, patients submit to the risks and complications of surgery without benefit. Thus, the ability to identify potential responders to adjuvant treatment may improve patient selection for surgery and help develop response criteria that are suitable for routine response monitoring, especially for guiding treatment in patients with inoperable disease.

Computed tomography (CT) and PET are most often used to assess NSCLC response to treatment. However, only a limited number of studies have evaluated the roles of PET as a predictor of treatment response, with pathological response being the gold standard, and the results of these studies vary. Several systematic reviews 2,3 provided an overview of the literature on the value of 18F-fluorodeoxyglucose (18F-FDG)-PET for monitoring and predicting the response of NSCLC to adjuvant therapy. However, none of these studies either pooled overall sensitivity and specificity using pathological outcome as the gold standard or performed a heterogeneity analysis. More recently, in a systematic review by Rebollo-Aguirre et al. 4 that included nine published studies, three studies 5–7 did not report pathological response at the primary tumour stage. Here, we have performed a meta-analysis to review the updated relevant literature using pathological outcome as the gold standard (13 studies) and have compared the predictive values of PET and CT. In this study, we hope to develop response criteria that are suitable for routine response monitoring.

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Materials and methods

Data sources

We conducted a systematic literature search for articles on NSCLC and neoadjuvant treatment. The PubMed database (up to February 2012) and the EMBASE (up to February 2012) were searched using the keywords ‘Non-Small Cell Lung Carcinomas or Non-Small Cell Lung Cancer’; ‘Neo-adjuvant Therapies, Neo-adjuvant Treatment, or Neo-adjuvant Treatments’; and ‘Positron Emission Tomography or PET scan, or PET’. The scope of the literature search was enlarged using the references in those studies. The titles and abstracts of the retrieved articles were then reviewed.

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Study selection

We included studies that investigated the predictive values of 18F-FDG-PET for pathological tumour response to neoadjuvant therapy in patients with NSCLC. The inclusion criteria were as follows: (i) histologically proven NSCLC; (ii) pathological outcome as determined by surgery, which was considered the ‘gold standard’ in our meta-analysis; (iii) use of 18F-FDG as a tracer; (iv) use of an 18F-FDG-PET scanning apparatus in humans; (v) adequate sample size to calculate the sensitivity and specificity required, with at least 10 participants in each study because a small sample size would make statistical errors appear too large 8,9; and (vi) articles reported in English.

The exclusion criteria were as follows: (i) use of other radiotracers; (ii) no pathological response reported at the primary tumour stage; (iii) data from the same research used in more than one article with the exclusion of a small sample size; (iv) animal and ex-vivo studies; (v) abstracts, reviews, editorials, letters and comments; and (vi) insufficient data to calculate estimates.

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Data extraction

Using the above-mentioned inclusion and exclusion criteria, the data were extracted independently by two reviewers (C.Z. and J.L.). Disagreements between them were resolved by a third reviewer (J.T.), who participated in the discussion and made the ultimate decision.

We extracted the following information from the eligible studies: (i) first author and the year of publication; (ii) sample size (the number of participants); (iii) demographic characteristics such as age and sex; (iv) items of methodological quality appraisal; (v) technical specification of 18F-FDG-PET and interpretation of PET results; and (vi) number of true-positive, false-positive, true-negative and false-negative results, which were based on whether the patients were PET responders and whether they had experienced treatment failure.

In our meta-analysis, histological response was assessed on the basis of histological grade, for which we merged four grades into two: well-defined and moderately defined responses were combined into low-grade response and poorly and undifferentiated responses into high-grade response 10.

The methodological quality of the included studies was assessed on the basis of the Quality Assessment of Studies of Diagnostic Accuracy Included in Systematic Reviews (QUADAS) criteria 11. Two reviewers (C.Z. and J.T.) assessed the study quality, and any disagreements between them over QUADAS items were resolved by the third reviewer (J.L.), who participated in the discussion and made the ultimate decision.

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Statistical analysis

For each study, we constructed a 2×2 contingency table consisting of true-positive, false-positive, false-negative and true-negative results in which all patients were categorized according to the criteria mentioned above. We calculated the sensitivity and specificity of each study method and then estimated the summary sensitivity and specificity with their corresponding 95% confidence interval (CI). We also drew summary receiver-operating characteristic curves and confidence regions for summary sensitivity and specificity.

The heterogeneity among studies was analysed using the I2 index. An I2 value of 0% indicates no observed heterogeneity, whereas larger values show increasing heterogeneity 12. The Spearman ρ value between the logit of sensitivity and the logit of 1−specificity was calculated to determine the presence of a threshold effect 13. If the analyses suggested the presence of substantial heterogeneity, the reasons for such heterogeneity could be explored by relating the study level covariates to an accuracy measure using the metaregression techniques of the Littenberg and Moses Linear model weighted by the inverse of the variance 14. If a particular study level covariate was significantly associated with diagnostic accuracy, the ratio of diagnostic odds ratios (rDORs) had a low P value. The following covariates were extracted, and their influence on sensitivity and specificity was also analysed: sample size, tumour response definitions of PET, therapy regimens, PET scanner type, and total methodological quality score. We also analysed the predictive values of CT for evaluating response using pathologic response as the gold standard. Further, we performed a Z test to determine whether the area under the curve of PET was significantly different from that of CT and P values less than 0.05 were considered statistically significant.

All statistical analyses were performed on a per-patient basis using Meta-DiSc (version 1.4; Meta-DiSc, Unit of Clinical Biostatistics Team of the Ramony Cajal Hospital, Madrid, Spain) or Stata Software (version 12.0; Stata Corp., College Station, Texas, USA).

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Results

Literature searches

A total of 294 articles were initially retrieved from the PubMed and EMBASE databases. Of these, 221 were excluded because they did not meet the defined inclusion criteria, whereas 39 studies were excluded because they did not yield any data or yielded insufficient data such that a 2×2 contingency table could not be constructed for calculating each method’s sensitivity and specificity for predicting tumour response. One article was excluded because the data in it were used in another study by the same group 15. Nine studies were excluded because fewer than 10 patients with NSCLC had been included, whereas another 11 studies were excluded because the Response Evaluation Criteria In Solid Tumours (RECIST) had been used as the gold standard. Ultimately, 13 studies investigating 414 patients were included in this study 16–28 (Supplementary Appendix Fig. 1, http://links.lww.com/NMC/A1). The characteristics of the included studies and the methodological quality assessment results are shown in Table 1. Details of the technologies used in each study and any associated image interpretation issues are shown in Supplementary Appendix Table 1 (http://links.lww.com/NMC/A2).

Table 1
Table 1
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Quality assessment of the published studies

We have modified the QUADAS questions as shown in Supplementary Appendix Tables 2 (http://links.lww.com/NMC/A3) and 3 (http://links.lww.com/NMC/A4). No article was granted the maximum 14 points. We used 10 points as the cutoff to differentiate low scores from high scores 8,9. The main weaknesses were in the criteria evaluated by items 8, 10 and 11. The total methodological quality score, expressed as a fraction of the maximum score, was 50–93% (median, 64%).

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Sensitivity, specificity, likelihood ratios and summary receiver-operating characteristic curves

The criterion of PET with better predictive values was considered in the pooling calculation if more than one parameter was used within a single study. On the basis of the random-effect model, the pooled sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 18F-FDG-PET for the assessment of treatment response in NSCLC were 83% (95% CI; 76–89%), 84% (95% CI; 79–88%), 74% (95% CI; 67–81%) and 91% (95% CI; 87–94%), respectively. The detailed sensitivity and specificity with a 95% CI reported in the included individual study are shown in the Forest plot (Fig. 1). The Q* statistic for the summary receiver-operating characteristic curve was 0.8282 (Fig. 2).

Fig. 1
Fig. 1
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Fig. 2
Fig. 2
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Heterogeneity, cutoff effect and subgroup analysis

Studies showed heterogeneity in their estimates of sensitivity and specificity (P<0.05). A Spearman Ρ value of −0.081 did not suggest the presence of a threshold effect (P>0.05).

It is noted that those studies differed substantially in methodological features and reference standards. First, nine articles used PET and four articles used PET/CT to predict the therapy response. Second, six articles used the response index, four articles used the standardized uptake value (SUV) as the PET reference and the others used contrast ratio, model prediction probability, or did not report any method to reference the PET. Third, five articles used chemotherapy and radiotherapy and two articles used only chemotherapy. The therapies in the other articles mixed two different treatment modalities in one study. All researchers assessed the response at 2–4 weeks after the completion of preoperative treatment in our meta-analysis. Therefore, we undertook several subgroup analyses using metaregression techniques (Table 2). The pooled sensitivity and pooled specificity results of the subgroup analysis are shown in Table 3. The predictive values of 18F-FDG-PET in predicting tumour response did not significantly differ in terms of sample size, tumour response definitions of PET, therapy regimens, or PET scanner type (P>0.05). However, significantly increased relative DOR were observed in the subgroup analyses in terms of total methodological quality score (high DOR for studies with a total score >60%). The pooled sensitivity, specificity, PPV and NPV of 18F-FDG-PET in studies with 95% CI and with total scores greater than 60% were 91% (95% CI; 84–95%), 83% (95% CI; 77–88%), 78% (95% CI; 70–85%) and 94% (95% CI; 88–97%), respectively.

Table 2
Table 2
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Table 3
Table 3
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Comparison of positron emission tomography and computed tomography predictive value for treatment response

Seven studies including 255 patients used CT to assess NSCLC treatment response 18,21–24,27,28. The pooled sensitivity, specificity, PPV and NPV of CT in studies with a 95% CI were 71% (95% CI; 59–81%), 68% (95% CI; 61–75%), 46% (95% CI; 37–56%) and 86% (95% CI; 79–91%), respectively. The Z test results showed a significant difference in the area under the curve between PET and CT for predicting the therapeutic response (P<0.05). Therefore, the predictive value of PET for patients with NSCLC using pathological response as the gold standard was significantly higher than that of CT.

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Discussion

Earlier systematic reviews reported the predictive value of PET for NSCLC response to neoadjuvant therapy. The review by Vansteenkiste 2 included only four published studies, whereas the review by De Geus-Oei 3 included seven articles. Both reviews concluded that 18F-FDG-PET has an important role for predicting the response of NSCLC to neoadjuvant therapy, but they did not provide a subgroup analysis to explore the heterogenic results, nor did they provide an optimum PET-guided policy. Our statistical results updated the studies and confirmed the standpoint of relatively high-pooled sensitivity (83%), specificity (84%) and NPV (91%) and relatively low PPV (74%). The high NPV of 18F-FDG-PET (91%) might help us discern patients whose disease is not responding to therapy. This situation is perhaps interpreted as follows: PET imaging has limitations in terms of image noise created by nonmalignant metabolic processes such as treatment-related inflammation, stunning and proliferation. These noises probably produce metabolic uptake that obscures the tumour response, resulting in a relatively low sensitivity for detecting complete response, which leads to nonresponse overestimation.

In our study, we also compared the predictive values between PET and CT for evaluating the pathologic response at the primary tumour stage. The histological responses used in our meta-analysis are recommended by the College of American Pathologists. They are all included in category IIA, which includes factors repeatedly shown to have predictive value for therapy. Therefore, lack of uniformity in reporting pathologic findings might not be a significant limitation in this study. The sensitivity and specificity for therapy response with PET were 83 and 84%, whereas those with CT were only 71 and 68%. Significant differences were observed between the two techniques according to the Z test (P<0.05). Although both CT and PET responses were significantly associated with pathological response, PET was a much better predictor of NSCLC therapy response. These results were consistent with those of other studies 18,21,22,28. This situation is perhaps best interpreted as follows: first, tumour response has traditionally been assessed by comparing tumour sizes on CT scans before and after treatment 29–31. However, changes in viable cell fraction do not always result in major changes in tumour size because tumour tissue can be replaced by necrotic or fibrotic tissue, and CT is unable to differentiate between these different tissue types. In contrast, 18F-FDG-PET is based on the higher glucose metabolism of tumour cells compared with healthy cells. 18F-FDG preferentially accumulates in viable tumour cells and not in fibrotic or necrotic tissue 32. Therefore, PET uses metabolic criteria to help differentiate tumours from scar tissue. Second, major volume changes may take several weeks to months to become apparent, especially after radiotherapy, hampering the use of CT to tailor treatment to the individual patient. Meanwhile, a more rapid change is usually observed in cellular metabolism than in tumour size. Therefore, 18F-FDG-PET might be able to assess responses earlier compared with CT. In summary, functional imaging with 18F-FDG-PET is a more promising modality than structural imaging techniques such as CT for predicting NSCLC therapy response. Nevertheless, PET still has a long way to go before taking the place of the RECIST criteria because much better standardization and large-scale experiments are needed to confirm these findings.

Because of the increasing use of 18F-FDG-PET in patients with NSCLC, the development of a recommendation for standardized PET parameters to predict NSCLC therapy response has become imperative. Visual assessment, maximum standardized uptake value (SUV max), and response index (RI; equal to the per cent SUV reduction) are the current widely used parameters for the assessment of therapeutic response in tumours. Because a limited number of the included studies used visual assessment, we performed subgroup analysis of SUV and RI only. The metaregression analysis results did not show any significant difference between the two predictive parameters. However, SUV can be influenced by technical and physiological factors. In the studies included in our meta-analysis, 18F-FDG doses varied between 370 and 550 MBq, 18F-FDG uptake was for 60 min, and duration of fasting among patients was between 4 and 6 h. Therefore, overall, the scanning meta-analysis involved relatively small variations. However, the reproducibility of SUV and of visual scores in clinical practice was poor in different institutions, which could be overcome by using RI; this will allow more consistent predictions of therapy outcomes across different institutions. Therefore, using RI might be the better choice.

The drawbacks of this study limit the current strength of evidence. First, the studies included in our meta-analysis produced heterogeneous results. Subgroup analysis revealed that methodological quality could be responsible for this heterogeneity. Results of the comparisons among groups demonstrated that the predictive values of the studies with a total score greater than 60% were higher than those of studies with a total score less than 60%. The pooled sensitivity and specificity of the studies with a total score greater than 60% were 91 and 83%, respectively. Nevertheless, the included studies were of moderate methodological quality, and all studies showed that 18F-FDG-PET is a significant predictor of therapy outcome. The main weaknesses in the criteria were as follows: in 76.92% of the included studies, an interpretation of the findings on PET performed without knowledge of the pathological findings was not reported. The 18F-FDG-PET protocol (scanner type, acquisition mode, reconstruction method, etc.) was not described in sufficient detail in 69.23% of the included studies. To improve methodological quality, future studies should adhere to the Standards for Reporting of Diagnostic Accuracy Guidelines for Reporting the Results of Diagnostic Accuracy 33,34. The National Institutes of Health in the USA updated the guidelines for performing serial 18F-FDG-PET evaluations and for reporting metabolic responses 30. These recommendations will make quantitative measurements of 18F-FDG uptake by tumours more consistent across different sites for larger prospective studies in the future. Second, our results should be interpreted with caution because adenocarcinomas and squamous carcinomas were also included in these studies. As SUVmax is often lower in the case of adenocarcinomas than in squamous carcinomas, the degree of change and measurement of response may be different. Researchers should reconsider the reference values on the basis of their patients’ pathology. Third, weaknesses of this study include the fact that the study, which assesses the response during the treatment using the pathological outcome as gold standard, was none as far as we know. All researchers assessed the response relatively immediately after treatment in our meta-analysis. However, early prediction of a therapy response has the potential to improve disease management in nonresponders by avoiding the side effects and costs of ineffective treatment, especially as valid second-line treatment has become available. As our previous study reported that early evaluation of response during therapy may be more promising for rectal cancer 35, future prospective studies for assessing the response relatively early in the treatment for NSCLC are needed. Fourth, some of the included studies were retrospective in design and did not use a sample representative of the original study population. Therefore, the results of this study should serve as a foundation for future prospective studies. Additional prospective data are needed before this promising method can be used widely.

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Conclusion

According to our meta-analysis results, 18F-FDG-PET scanning has an important role in predicting NSCLC nonresponders to neoadjuvant therapy. The predictive value of PET was superior to that of CT for evaluating pathologically documented responses. However, additional evaluations using prospective clinical trials will be required to assess the clinical benefit of this strategy.

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Acknowledgements

The study was supported by research grants from the National Natural Science Foundation of China (No. 30830038, 30970842, 81071180, 81000929); ‘973’ Project (2012CB932604); New Drug Discovery Project (2012ZX09506-001-005); Key Project of Science and Technology Commission of Shanghai Municipality (No. 10JC1410000) and by Shanghai Leading Academic Discipline Project (No. S30203).

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Conflicts of interest

There are no conflicts of interest.

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Keywords:

lung cancer; meta-analysis; neoadjuvant treatment; non-small-cell lung cancer; positron emission tomography; predicting response

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