Diagnostic performance of white light endoscopy, narrow band imaging, and flexible spectral imaging color enhancement for early gastric cancer: a systematic review and meta-analysis

Introduction

Gastric cancer (GC) is one of the most prevalent malignant neoplasms globally, ranking as the fifth most frequently diagnosed cancer and the third leading cause of cancer-related mortality in 2020 (1). A significant challenge in managing GC lies in its nonspecific and inconspicuous manifestations, which often lead to delayed diagnosis. As a result, a considerable proportion of GC cases are detected at advanced stages, contributing to poor survival rates (2). Screening initiatives, particularly utilizing endoscopic examination, play a pivotal role in mitigating the incidence of advanced GC (3). Commonly employed endoscopic modalities encompass white light endoscopy (WLE), narrow band imaging (NBI), and flexible spectral imaging color enhancement (FICE).

While WLE has traditionally served as the standard endoscopic assessment, its accuracy in diagnosing EGC is limited. NBI enhances image resolution and contrast by utilizing specific wavelengths of light to visualize the superficial mucosal microvasculature and pit patterns (4). Furthermore, FICE, a form of image-enhanced endoscopic technology, has demonstrated superior performance, offering clearer lesion visualization while being less invasive and less time-intensive (5).

Numerous studies have highlighted the superior diagnostic efficacy of NBI and FICE over WLE in EGC diagnosis. However, the reported accuracy, sensitivity and specificity outcomes across studies comparing NBI with WLE exhibit considerable variability. WLE accuracy rates ranged from 59.7% to 89.2%, with sensitivity varying from 40% to 76.9%, and specificity ranging from 57.0% to 94.7%. Conversely, NBI accuracy ranged from 85.9% to 98.6%, sensitivity from 60.0% to 95.0%, and the specificity from 88.0% to 99.7% (6-14). As for studies comparing FICE and CLE, the ending points were various. Consequently, the debate regarding whether NBI and FICE outperform WLE in EGC diagnosis persists. The primary objective of this systematic review and meta-analysis was to comprehensively evaluate the efficacy of NBI, FICE, and WLE in diagnosing EGC. We present this article in accordance with the PRISMA reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-482/rc).

Methods

Registration

We registered a protocol of the study in PROSPERO with the registration number of CRD42023397899.

Search strategy

The meta-analysis was designed, conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (15). We systematically searched 4 key databases including PubMed, Web of Science, EMBASE, and the Cochrane Library up to February 2023. We used Medical Subject Headings (MeSH) items and free text words to construct search strategies. The article keywords were as follows: ‘early gastric cancer’ AND ‘diagnosis’ AND ((‘white light endoscopy’ AND ‘narrow band imaging’) OR (‘white light endoscopy’ AND ‘flexible spectral imaging color enhancement’) OR (‘narrow band imaging’ AND ‘flexible spectral imaging color enhancement’)). And the complete search strategies for each database are presented in detail in Table S1.

Study selection

We included the articles that met all the following criteria: (I) population: patients who might be diagnosed as ECG and received endoscopic biopsies; (II) intervention and comparison: the diagnostic efficacy between NBI and WLE, or between FICE and WLE, or between NBI and FICE; (III) study design: full-text publications of original randomized controlled trial or cohort study; (IV) study that provided true-positive (TP), false-positive (FP), true-negative (TN) and false-negative (FN) directly or with sufficient data for indirect calculation; (V) we adopted the Japanese standard: the revised Vienna classification as the standard as gold standard to recognize the lesions. According the classification system, category C4 (mucosal high-grade neoplasia) and C5 (submucosal invasion by neoplasia) were diagnosed as EGC. And noncancerous lesions were C1 (negative for neoplasia), C2 (indefinite for neoplasia) and C3 (mucosal low-grade neoplasia).

The exclusion criteria were: (I) duplication articles; (II) study types including reviews, meta-analyses, case reports, letters, comments, or conference abstracts; (III) the study did not contain the technologies we focused or technologies were not used to diagnose EGC; (IV) the study only contained patients which were pathologically diagnosed as EGC; (V) study lacking sufficient information for the calculation of TP, FP, TN, and FN. When multiple publications on the same study population, or the overlapping of patient cohort were found, study based on the largest cohort was included in the meta-analysis; (VI) studies aimed at comparing the diagnostic performance of WLE and NBI with magnification.

Data extraction and quality assessment

We extracted various parameters including the number of lesions, country, and lesion morphology, lesion size, among others. The primary outcomes comprised the pooled sensitivity and specificity, and secondary outcomes encompassed the positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the curve (AUC). The study search, selection, and data extraction were conducted independently by two reviewers. Disagreements were resolved by a third reviewer through discussion. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was employed to evaluate the quality of included articles. QUADAS-2 is used in identifying diagnostic meta-analysis, which basically includes four areas: patient selection, index test, reference standard and flow and time. Each item is assessed based on the three possible outcomes: yes, no, unclear (16).

Statistical analysis

A meta-analysis was conducted to evaluate accuracy of the two different technologies, namely NBI, and WLE, in differentiating EGCs from other gastric lesions. The TP, FP, FN, and TN were used to calculate the pooled sensitivity, specificity, PLR, and NLR. DOR along with its corresponding 95% confidence interval (CI) were calculated. The inconsistency index (I²) and Q statistics were used to evaluate the heterogeneity, with I²>50% or P<0.1 reflecting significant heterogeneity (17). A random-effects model (DerSimonian-Laird method) would be applied if heterogeneity was significant. Subgroup analyses and influence analyses were performed subsequently. Otherwise, If the heterogeneity was not significant, a fixed-effects model (Mantel-Haenszel model) would be used. The covariates in subgroup analyses included country (China or Japan), the number of lesions (n>200 or n≤200), the type of studies (prospective or retrospective), and the source of biopsies (acquired via endoscopy or both surgery and endoscopy). We conducted sensitivity analyses using a leave-one-out-method to verify our results. A summary receiver operating characteristic (SROC) curve was plotted (18). The AUC was calculated to estimate the diagnostic accuracy of the technologies, with an AUC exceeding 0.75 indicating great diagnostic efficacy. Deeks’ asymmetry was employed to evaluate the publication bias by constructing a funnel plot of diagnostic log odds ratio vs 1/sqrt (effective sample size), using a significance level of P<0.1. Stata version 14.2 (StataCorp, College Station, TX, USA) was used for all statistical analyses.

Results

Included studies

After systematically searching PubMed, Web of Science, EMBASE, and the Cochrane Library databases, a total of 444 articles were identified. The selection process and reasons for exclusion are summarized in Figure 1. Ultimately, 16 studies were incorporated into our research. Among these studies,9were deemed suitable for meta-analyses comparing the diagnostic efficacy of NBI and WLE in detecting EGC (6-14), while others were considered eligible for evaluating the performance of FICE versus WLE (5,19-24).

Figure 1 Literature search flow diagram. FICE, flexible spectral imaging color enhancement; ME, magnifying endoscopy; NBI, narrow band imaging; WLE, white light endoscopy.

Study characteristics and quality assessment

The characteristics of studies comparing NBI and WLE are shown in Table 1. Among the included studies, 7 researches were conducted in Japan and 2 were from China. Additionally, 4 studies included less than 200 lesions, while others were not. Besides, 6 researches were prospective while others were retrospective. Most studies used irregular microvascular pattern (IMVP)/irregular micro-surface pattern (IMSP) plus demarcation line (DL) with NBI to differentiate EGC from gastric lesions, and only two studies used other criteria to diagnose EGC.

The results of QUADAS-2 quality scale are presented in Table S2 and Figure 2. If the risk of bias and concerns about applicability were assessed as low, articles were considered to be of high quality. If not, it might affect the accuracy of the results and contribute to heterogeneity (16). It is noteworthy that all included articles were assessed high quality relatively.

Figure 2 Quality evaluation table. (A) Proportion of studies with low, high, or unclear risk of bias and concerns regarding applicability. (B) Methodological evaluation according to QUADAS-2. QUADAS-2, Quality Assessment of Diagnostic Accuracy Studies-2.

Diagnostic performance of NBI and WLE

The details of extracted or calculated sensitivity, specificity, accuracy, TP, FP, TN, and FN are delineated in Table 1. Our analysis revealed that patients in NBI group exhibited superior sensitivity (0.82; 95% CI: 0.71–0.89, significant heterogeneity, I²=82.54%, P<0.01) and specificity (0.95; 95% CI: 0.90–0.97, significant heterogeneity, I²=86.80%, P<0.01) compared to those diagnosed with WLE (sensitivity: 0.59; 95% CI: 0.49–0.69; significant heterogeneity, I²=74.20%, P<0.01; specificity: 0.77; 95% CI: 0.65–0.86; significant heterogeneity, I²=96.38%, P<0.01 respectively) (Figure 3). The AUC for WLE was 0.71 (95% CI: 0.67–0.75), whereas for NBI, it was 0.95 (95% CI: 0.93–0.97) (Figure 4). Because of significant heterogeneities observed in both sensitivity (WLE: I²=74.20%, NBI: I²=82.54%) and specificity (WLE: I²=96.38%, NBI: I²=86.80%), random effect models were chosen for calculation. The pooled sensitivity, specificity, PLR, NLR, and DOR in random effect models are as presented in Table S3. As for the sensitivity analysis (Figure S1), we could see that Kakushima’s study might have influence on the results of NBI (8). However, after eliminating Kakushima’s study, the heterogeneity persisted. We also performed subgroup analyses (Table S4) to find out the potential sources of heterogeneity. Number of lesions, derivation of biopsies, study type and diagnostic criteria might be the sources of heterogeneity in the analyses of NBI’s specificity. Besides, derivation of biopsies and diagnostic criteria also appeared to contribute to the heterogeneity of NBI’s sensitivity. Additionally, the country, derivation of biopsies and the study type were implicated as sources of heterogeneity in WLE’s sensitivity. And derivation of biopsies is the source of the heterogeneity of WLE’s specificity. The results of funnel plot asymmetry texts for WLE and NBI were insignificant with the P value of 0.11 and 0.52, respectively (Figure S2).

Figure 3 The forest plot of pooled sensitivity and specificity of NBI/WLE to diagnose EGC. (A) The sensitivity and specificity of NBI to diagnose EGC. (B) The sensitivity and specificity of WLE to diagnose EGC. CI, confidence interval; EGC, early gastric cancer; NBI, narrow band imaging; WLE, white light endoscopy.

Figure 4 The AUC of NBI (A) and WLE (B) to diagnose EGC. AUC, area under the curve; EGC, early gastric cancer; NBI, narrow band imaging; SENS, sensitivity; SPEC, specificity; WLE, white light endoscopy.

Diagnostic performance of FICE and WLE

We only systematically reviewed the full studies on comparison between FICE and WLE instead of conducting meta-analyses due to the challenges in extracting information, variations in endpoints, and overall low quality of the available researches. However, our research indicated that FICE exhibited superior diagnostic performance compared to WLE, especially in terms of the improvement the quality of images. We summarized the characteristics of these researches in Table 2. We had to admit that our research was unable to fully integrate the comparison between FICE and WLE due to a dearth of data, which in turn compromised the robustness of our conclusions regarding FICE. It is imperative to conduct targeted analyses to bridge this gap.

Discussion

GC, one of the most common malignant tumors, remains the leading cause of cancer-related mortality globally. The 5-year survival rate of GC is poor for most patients are diagnosed at advanced stages, which was reported to be only 30% (2). However, studies have demonstrated the 5-year survival rate of EGC was around 90% (25). In order to decrease the mortality rate of GC, it is imperative for clinicians to diagnose GC early. It is acknowledged that endoscopy is a precise examination. There are a lot of endoscopic technologies applied in clinical practice to help doctors diagnose EGC at early stage. Thanks to the implementation of these advanced technologies including WLE, NBI, and FICE, GC could be diagnosed earlier.

There were studies concluding that NBI and FICE could improve the diagnostic rate of EGC and had overcome several limitations of WLE, just as Manfredi summarized (26). And some meta-analyses mainly focused on one endoscopic technology, such as magnifying narrow band imaging (M-NBI) (27,28). However, there were also some original studies focusing on the difference of diagnostic performance between NBI and WLE, or FICE and WLE. Therefore, it is necessary to conduct a study that focused on identifying the diagnostic performance of NBI and FICE compared with WLE.

After full-text reading, we found all the articles chose pathologic results as gold standard. However, the standard of pathology varies between the Western and Japan (29,30). In our study, we adopted the Japanese standard: the revised Vienna classification as the standard. As mentioned above, category C4 and C5 were diagnosed as EGC, while noncancerous were C1, C2 and C3.

The results of two meta-analyses which aimed at the diagnostic performance of NBI were consistent with ours, demonstrating NBI is a powerful tool in detecting EGC. The pooled sensitivity, specificity, and AUC of NBI in our study were 0.82, 0.95, and 0.95, respectively. Likewise, the pooled sensitivity, specificity, and AUC of Huang’s study were 0.84, 0.95, and 0.96, respectively. The pooled sensitivity, specificity, and AUC of Hu’s study were 0.96, 0.86, and 0.9623, respectively (27,28). Additionally, Hu’s study mentioned that six of included articles also compared the diagnostic performance between NBI and WLE (28). The pooled sensitivity and specificity were 0.57 and 0.79, respectively, which were similar with our results for WLE (the pooled sensitivity and specificity were 0.59 and 0.77, respectively). Until now, there was only one diagnostic meta-analysis comparing the diagnostic efficacy of WLE and M-NBI (31). Their diagnostic indicators of WLE were poor (the pooled sensitivity, specificity, and AUC was 0.48, 0.67, and 0.62, respectively) than ours (the pooled sensitivity, specificity, and AUC was 0.59, 0.77 and 0.71, respectively). However, the results of NBI were alike, affirming its potential as a promising tool in EGC detection.

The I² values of our results were more than 50%, which indicated the existence of heterogeneity. Similarly, Zhang’s study also demonstrated high grade of heterogeneity (31). They performed further sensitivity analyses for the studies of small lesions and studies with a score of 12 points or greater using QUADAS criteria in the literature quality assessment. To investigate potential sources of heterogeneity and confirm the robustness of our results, we conducted sensitivity analyses using a leave-one-out method and undertook subgroup analyses.

As for the articles comparing FICE and WLE, most of them highlighted the superior quality of color differentiation achieved through FICE compared to conventional imaging techniques (5,20-23). It indicated that FICE could help endoscopists identify EGC from other lesions precisely.

There are several mechanisms which can explain why NBI and FICE performed better than WLE in diagnosing EGC. NBI, an advanced endoscopic imaging tool, enhances the resolution and contrast of images by visualizing the superficial mucosal microvasculature and pit patterns clearly (4). This capability enables the identification of intrapapillary capillary loops, which are pivotal in the detection of early-stage cancer. Therefore, many studies have adopted the VS classification system proposed by Yao et al. to diagnose EGC: the presence of a DL and an IMVP or an IMSP (32). In addition, FICE is one of image-enhanced endoscopic tools which can improve the diagnostic accuracy of gastrointestinal cancer. In contrast to WLE, FICE captures images by each wavelength and reconstitute virtual images according to the choice of different combinations of wavelength to provide high-contrast images (5). In essence, the enhanced imaging capabilities of NBI and FICE, facilitated by their respective mechanisms, enable clinicians to identify EGC more accurately.

We evaluated the publication bias through constructing funnel plots. The P value of WLE and NBI in funnel plot asymmetry tests were 0.11 and 0.52, respectively, which demonstrated that the publication bias was insignificant. Considering the AUC, which were 0.71 (95% CI: 0.67–0.75) for WLE and 0.95 (95% CI: 0.93–0.97) for NBI, it is evident that the diagnostic performance of NBI surpasses that of WLE.

There were several advantages in our study. Firstly, it was the first study aimed at comparing the diagnostic performance of WLE, NBI, and FICE, which can supply more evidence to support the conclusion that NBI and FICE play an important role in the detection of EGC and guide further studies. Secondly, we performed sensitivity analyses and subgroup analyses to elucidate the sources of heterogeneity.

However, our study also had several limitations. Firstly, all the included articles were conducted in China and Japan. Therefore, it might not represent cases in other regions worldwide. Further studies conducted in diverse populations are necessary to validate our conclusions across different demographic and clinical contexts. Secondly, high heterogeneity was observed in our study. Although we conducted sensitivity analyses using a leave-one-out-method and sub-group analyses to eliminate it, the heterogeneity was still existed. Maybe other unreported factors could affect the overall estimation. Thirdly, we also attempted to clarify whether FICE was more accurate than WLE in the detection of EGC. However, we found that only seven articles were included and the information supplied was insufficient, indicating that we could not perform meta-analyses between FICE and WLE. Thirdly, many articles included were retrospective, which may introduce potential biases. Therefore, it is crucial to highlight this concern alongside the pressing need for well-designed prospective studies. Lastly, the detection rate of EGC varies significantly depending on the endoscopists. The variation is influenced by factors such as the duration of the examination and the implementation of Image Enhanced Endoscopy (IEE) (33-36). We only tried to find out whether the implementation of IEE have impact on the detection rate of EGC for the duration of the examination was unrecorded.

Conclusions

Our study demonstrated that NBI is a powerful and promising endoscopic tool which can be used in the diagnosis of EGC, surpassing the performance of WLE. Additionally, our results suggest that FICE hold promise as a reliable endoscopic tool which can improve EGC diagnosis. Further high-quality studies are needed to identify the diagnostic performance of NBI and FICE. By continuing to advance our understanding of the diagnostic capabilities of NBI and FICE, we can improve the detection rates of EGC, ultimately leading to better patient outcomes and reduced mortality from GC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-482/rc

Funding: This work was supported by Qingdao Science and Technology Demonstration and Guidance Special Fund for the Benefit of the People (No. 21-1-4-rkjk-7-nsh to Q.Y.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-482/coif). Q.Y. receives funding from Qingdao Science and Technology Demonstration and Guidance Special Fund for the Benefit of the People (No. 21-1-4-rkjk-7-nsh). The other authors have no 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/.

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