Meta-Analysis Open Access
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 28, 2020; 26(40): 6279-6294
Published online Oct 28, 2020. doi: 10.3748/wjg.v26.i40.6279
Diagnostic efficacy of the Japan Narrow-band-imaging Expert Team and Pit pattern classifications for colorectal lesions: A meta-analysis
Yu Zhang, Wen-Sheng Pan, Hang-Hai Pan, Department of Gastroenterology, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou 310014, Zhejiang Province, China
Hui-Yan Chen, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
Xiao-Lu Zhou, Department of Medical College, The Medical College of Qingdao University, Qingdao 266071, Shandong Province, China
Xin-Xin Zhou, Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University, Hangzhou 310003, Zhejiang Province, China
ORCID number: Yu Zhang (0000-0003-1387-3962); Hui-Yan Chen (0000-0002-6483-5002); Xiao-Lu Zhou (0000-0002-6771-3027); Wen-Sheng Pan (0000-0002-2347-1695); Xin-Xin Zhou (0000-0003-0183-6400); Hang-Hai Pan (0000-0002-7366-8269).
Author contributions: Zhang Y contributed to the conception and design of the study, and critical revision; Pan HH and Zhou XX contributed to the acquisition of data and data analysis; Pan HH, Zhou XX, Pan WS, Chen HY and Zhou XL contributed to the interpretation of data; Pan HH and Zhou XX drafted the article; Pan WS, Chen HY and Zhou XL revised the manuscript; all authors contributed to the final approval of the manuscript.
Supported by the Natural Science Foundation of Zhejiang Province, No. LQ20H160061; and Medical Health Science and Technology Project of Zhejiang Provincial Health Commission, No. 2018255969
Conflict-of-interest statement: No potential conflicts of interest exist.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Hang-Hai Pan, MD, Attending Doctor, Department of Gastroenterology, Zhejiang Provincial People’s Hospital, People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou 310014, Zhejiang Province, China. panhanghai@163.com
Received: June 21, 2020
Peer-review started: June 21, 2020
First decision: August 21, 2020
Revised: August 26, 2020
Accepted: September 23, 2020
Article in press: September 23, 2020
Published online: October 28, 2020
Processing time: 128 Days and 22.8 Hours

Abstract
BACKGROUND

Pit pattern classification using magnifying chromoendoscopy is the established method for diagnosing colorectal lesions. The Japan Narrow-band-imaging (NBI) Expert Team (JNET) classification is a novel NBI magnifying endoscopic classification that focuses on the vessel, and surface patterns.

AIM

To determine the diagnostic efficacy of each category of the JNET and Pit pattern classifications for colorectal lesions.

METHODS

A systematic literature search was performed using PubMed, Embase, the Cochrane Library, and Web of Science databases. The pooled sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve of each category of the JNET and Pit pattern classifications were calculated.

RESULTS

A total of 19227 colorectal lesions in 31 studies were included. The diagnostic performance of the JNET classification was equivalent to the Pit pattern classification in each corresponding category. The pooled sensitivity, specificity, and area under the curve (AUC) for each category of the JNET classification were as follows: 0.73 (95%CI: 0.55-0.85), 0.99 (95%CI: 0.97-1.00), and 0.97 (95%CI: 0.95-0.98), respectively, for Type 1; 0.88 (95%CI: 0.78-0.94), 0.72 (95%CI: 0.64-0.79), and 0.84 (95%CI: 0.81-0.87), respectively, for Type 2A; 0.56 (95%CI: 0.47-0.64), 0.91 (95%CI: 0.79-0.96), and 0.72 (95%CI: 0.68-0.76), respectively, for Type 2B; 0.51 (95%CI: 0.42-0.61), 1.00 (95%CI: 1.00-1.00), and 0.90 (95%CI: 0.87-0.93), respectively, for Type 3.

CONCLUSION

This meta-analysis suggests that the diagnostic efficacy of the JNET classification may be equivalent to that of the Pit pattern classification. However, due to its simpler and clearer clinical application, the JNET classification should be promoted for the classification of colorectal lesions, and to guide the treatment strategy.

Key Words: Colorectal neoplasms, Colonoscopy, Chromoendoscopy, Japan Narrow-band-imaging Expert Team, Pit pattern, Meta-analysis

Core Tip: This systematic review and meta-analysis examined the diagnostic efficacy of each category of the Japan Narrow-band imaging Expert Team (JNET) and Pit pattern classifications for colorectal lesions. The pooled analyses of 19227 colorectal lesions from 31 studies were performed. The sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve of each category of the JNET and Pit pattern classifications were calculated. The results suggest that the diagnostic efficacy of the JNET classification may be equivalent to that of the Pit pattern classification. However, due to its simpler and clearer clinical application, the JNET classification should be promoted for classification of colorectal lesions, and to guide the treatment strategy.



INTRODUCTION

Colorectal cancer (CRC) is one of the most common malignant tumors in the world[1]. Accurate identification and treatment of early CRC have significantly reduced its incidence and mortality[2]. However, early CRC is generally asymptomatic, and coloscopy allows the direct visual inspection of the intestinal tract and same-session detection, biopsy, and subsequent removal of lesions. Endoscopic evaluation of colorectal lesions is important to guide the selection of an appropriate treatment. The ideal endoscopic management of colorectal lesions involves two steps. First, any mucosal lesions need to be detected. Second, the lesions need to be characterized based on histological characteristics assessed via endoscopic evaluation; which forms the basis of the endoscopic judgment on whether the lesions need to be resected. In general, there is a consensus that low-grade dysplasias (LGDs), high-grade dysplasias (HGDs), and superficial submucosal invasive (SM-s) carcinomas are considered appropriate for endoscopic resection, while observation is recommended for hyperplastic polyps (HPs), in contrast, surgery is recommended for deep submucosal invasive (SM-d) carcinomas[3].

Kudo et al[4] first proposed the “Pit patterns” (the opening shape of a colorectal crypt) based on the classification of colorectal lesions via magnifying endoscopy, and indigo carmine dye contrast[4]. Pit pattern diagnosis (Type I-V) is clinically significant as it can differentiate between neoplasia and non-neoplasia, characterize the degree of histological atypia in a tumor, and reveal the invasion depth of early carcinoma.

Usually, standard magnification requires the use of chromoagents (e.g., indigo carmine, crystal violet, or methylene blue) to clarify the pit structures in these diagnostic procedures. However, with the emergence of electronic staining endoscopic equipment, chemical staining endoscopy has gradually been replaced. In addition, simpler and more convenient procedures are desirable for magnifying procedures. It has been suggested that narrow-band-imaging (NBI) technology is as effective as chromoendoscopy in differentiating the gross type of colorectal lesions[5]. The Colon Tumor NBI Interest Group proposed the NBI International Colorectal Endoscopic (NICE) classification in 2010, which is based on non-magnifying NBI observations[6]. However, the NICE classification cannot distinguish between benign adenoma and superficial mucosal carcinoma, therefore, it plays a limited role in guiding endoscopic treatment strategies. As more endoscopic devices are being equipped with a magnifying function, NBI combined with magnifying endoscopy is increasingly being used, which further improves the diagnostic efficiency, and plays an important role in estimating the invasion depth of the lesion. In order to better guide the endoscopic treatment strategy, in 2014, the Japan NBI Expert Team (JNET) proposed the JNET classification as a universal NBI magnifying endoscopic classification[7]. The JNET classification focuses on vessel and surface patterns to diagnose colorectal lesions as Types 1, 2A and B, 3.

Recently, several studies have proposed that the JNET classification of colorectal lesions via NBI magnifying endoscopy is a useful and objective tool for differentiating the gross type of colorectal lesions. However, differences in the diagnostic performance of the JNET classification have not been reported according to each gross type. Although several investigators now accept the application of the JNET classification for colorectal lesions, to what extent we can trust the results of the JNET classification, and whether the Pit pattern classification can be replaced by the JNET classification are aspects that remain unclear.

Few studies have compared the diagnostic efficacy of the JNET and Pit pattern classifications for each gross type of lesion, and determined the correlation between endoscopic features and pathological findings, and, to our knowledge, there are no meta-analyses on this topic. Accordingly, we performed a systematic review and meta-analysis to analyze the data on existing magnifying endoscopy trials using the JNET classification and the Pit pattern classification for characterization of colorectal lesions, and, to obtain a statistically convincing conclusion on the diagnostic accuracy, and practicability of these two comparable methods.

MATERIALS AND METHODS
Literature search

We performed a systematic literature search of articles in PubMed, Web of Science, Embase and the Cochrane Library (January 1990 to May 2020) containing quantitative data, and manually searched the reference lists of retrieved articles. The following search terms were used: “Japan Narrow-Band Imaging Expert Team”, “Japan NBI Expert Team”, JNET, “Pit pattern”, “Kudo’s classification”, “Colorectal Neoplasm” “Neoplasm, Colorectal”, “Colorectal Carcinoma”, “Carcinoma, Colorectal”, “Carcinomas, Colorectal”, “Colorectal Carcinomas”, “Colorectal Cancer”, “Cancer, Colorectal”, “Cancers, Colorectal”, “Colorectal Cancers”, “Colorectal Tumors”, “Colorectal Tumor”, “Tumor, Colorectal”, “Tumors, Colorectal”, “Neoplasms, Colorectal”, “Colon polyps”, “Colorectal polyps”, and “Colorectal lesions”. The queries used are displayed in the supplementary materials. All similar possible word variations were also searched. The attained records were retrieved and managed with EndNote X 9.0 (Bld 10136, Thomson Reuters, New York, NY, United States).

Study selection

Studies were included when all of the following conditions were met: (1) Studies in which all participants received the JNET or Pit pattern classification for colorectal lesions diagnosed via endoscopy; (2) Histological diagnosis was chosen as the gold standard; and (3) Studies in which sufficient data were reported to calculate true positive (TP), false positive (FP), false negative (FN), and true negative (TN) results. In addition, we included only the latest-published article when the same population was reported in more than one article. However, conference papers and duplicate published studies that fulfilled the above two criteria were excluded. This meta-analysis was performed in compliance with the preferred reporting items for systematic reviews and meta-analyses statement (PRISMA)[8].

Data extraction

Data from the included studies were extracted and cross-checked by 2 authors independently (Zhang Y and Pan HH). If there was a discrepancy in their opinions, it was discussed with other authors to achieve a consistent result. The extracted data included the name of the first author, year of publication, demographics of the population, type of endoscope, number of included patients, number of colorectal lesions examined, design of the study, and the type of classification for colorectal lesions. The number of TP, FP, TN, and FN results for the JNET, and Pit pattern classifications were the main statistics extracted from the studies. We computed sensitivity [TP/(TP + FN)] and specificity [TN/(TN + FP)] for each technique separately.

Quality assessment

The quality of the included studies was independently assessed by 2 authors independently (Zhou XX and Chen HY) using the quality assessment of diagnostic accuracy studies II (QUADAS-II) tool[9].

Statistical analysis

The original data from each study (TP, FP, TN and FN) were integrated through meta-analysis, and the pooled sensitivity, specificity, positive likelihood ratio, and diagnostic odds ratio (DOR) of both the JNET and Pit pattern classifications were calculated by the DerSimonian Laird random effects model[10]. The heterogeneity of pooled sensitivity and specificity was calculated using the I2 statistic, and a high degree of heterogeneity was set at I2 > 50%[11]. Mose's constant linear model was used to perform the summary receiver operating characteristic curve[12]. Cochrane’s Q test was used to evaluate the accuracy of the JNET and Pit pattern classifications in the diagnosis of colorectal lesions. When heterogeneity was present, the Spearman correlation coefficient, and the P value or heterogeneity ratio caused by the threshold effect were calculated. Meta-regression was conducted to explore the existing source of heterogeneity. Publication bias was assessed using the Deeks’ Funnel Plot Asymmetry Test. The t-test was used to compare the statistical significance of the area under the curve (AUC) and pooled sensitivity, with the significance set at P < 0.05. The statistical software used for the diagnostic accuracy test was Stat 15.1. Revman5.3 was used to evaluate the quality of the included studies.

RESULTS
Study selection

A total of 1146 articles were initially searched (351 in Web of Science, 241 in PubMed, 44 in Cochrane Library, and 510 in Embase), and 31 studies[5,13-42] with a total of 14674 patients were ultimately included in this meta-analysis. 1114 studies were excluded of which 306 studies were duplicate references, 442 studies were excluded based on title and abstract, and 70 studies were excluded after full-text review. A detailed PRISMA flow diagram is shown in Figure 1.

Figure 1
Figure 1 Study identification, inclusion and exclusion for meta-analysis.
Description of studies and qualitative analysis

All studies used the JNET or Pit pattern classifications as the diagnostic criteria for colorectal lesions examined via endoscopy. The JNET classified colorectal lesions into four categories: Type 1 is a hyperplastic polyp (HP)/sessile serrated lesion (SSL), Type 2A is a LGD/adenoma, Type 2B is a HGD/M-SM-s and Type 3 is a SM-d[5]. The Pit pattern classification has five corresponding categories: Type I is normal mucosa, Type II is hyperplasia/SSL, Types IIIL/IV are LGD/adenomas, Types IIIS/VI-L, and VI-H/VN are associated with HGD/M-SM-s lesions, and SM-d lesions, respectively[13]. The detailed interpretation criteria are shown in Table 1. Our study was restricted to only cross-sectional outcomes such as sensitivity and specificity, and the screening tests were compared to the reference standard (histopathological diagnosis). General information on the included studies is presented in Table 2. Of the 31 studies[5,13-42], 11 were retrospective [5,13-22], and 20 were prospective[23-42]. Seven studies[14-20] used the JNET classification alone, 21 studies[22-42] used the Pit pattern classification alone, and only 3 studies[5,13,21] used both the JNET and Pit pattern classifications in the same population. Thirteen studies[5,13-16,18-21,24,28,35,41] used narrow-band imaging magnifying endoscopy (NBI-ME), 13 studies used magnifying chromoendoscopy[22,25-27,29,30,32-35,37,38,42], and the other studies used BLI-magnification[17,23] (n = 2), NBI[31,39] (n = 2), and chromoendoscopy[36,40] (n = 2). The QUADAS-II quality assessment for each study is presented in Figure 2.

Table 1 Detailed tentative criteria for interpretation of the Japan Narrow-band-imaging Expert Team and Pit pattern classifications compared with histologic diagnosis.
InterpretationJNETPit patternHistological diagnosisTherapeutic strategy
Non-neoplasticINormal mucosa
Type 1IIHyperplastic/SSLFollow-up observation
NeoplasticType 2AIIIL + IVAdenoma/LGDEndoscopic resection (polypectomy/EMR)
Type 2BIIIS + VI-LHGD/M-SM-s cancerEndoscopic resection (ESD)
Type 3VN + VI-HSM-d cancerSurgery
Table 2 General characteristics of the included studies.
Ref.YearCountryStudy designPatients (n)Lesions (n)Type of endoscopyTypeof classificationCategory
Shibagaki et al[13]2020JapanRetrospective432718A-NBI-MEJNET/Pit pattern1/2A/2B/3; I, II, III/IV, VI-L, VI-H, VN
Kobayashi et al[5]2019JapanRetrospective7501402NBI-MEJNET/Pit pattern1/2A/2B/3; I, II, IIIL, IIIS, IV, IVH, VI-L, VI-H, VN
Sumimoto et al[14]2017JapanRetrospective19012933NBI-MEJNET1/2A/2B/3
Murano et al[15]2019JapanRetrospective6801472NBI-MEJNET1/2A/2B/3
Komeda et al[16]2017JapanRetrospective199199NBI-MEJNET1/2A/2B/3
Suzuki et al[17]2019JapanRetrospective145185BLI-MEJNET1/2A/2B/3
Hirata et al[18]2019JapanRetrospective61386138NBI-MEJNET1/2A/2B/3
Sakamoto et al[19]2019JapanRetrospective246246NBI-MEJNET1/2A/2B/3
Gonai et al[20]2020JapanRetrospective153169NBI-MEJNET2A/2B/3
Kawasaki et al[21]2019JapanRetrospective1717NBI-MEJNET/Pit pattern1/2A/2B/3; III, IV, VI-L, VI-H, VN
Nakano et al[23]2017JapanProspective506799BLI-MEPit patternII, IIIL, IIIS/IV, VI-L, VI-H/VN
Miroslaw et al[24]2015PolandProspective270386WL, NBI-MEPit patternI/II/IIIL/IIIS/IV/V
Su et al[25]2004ChinaProspective230270Magnifying chromoendoscopyPit patternI/II/IIIS/IIIL/IV/V
Kiesslich et al[26]2001GermanyProspective100283Magnifying chromoendoscopyPit patternI/II/IIIS/IIIL/IV/V
Kawaguti et al[27]2019BrazilProspective121123Magnifying chromoendoscopyPit patternII/IIIS/IIIL/IV/V
East et al[28]2008United KingdomProspective62116NBI-MEPit patternI/II/IIIS/IIIL/IV/V
Liu et al[29]2003ChinaProspective948954Magnifying chromoendoscopyPit patternI/II/IIIL/IIIS/IV/V
Kato et al[30]2006JapanProspective180210Magnifying chromoendoscopyPit patternNeoplastic/non-neoplastic
Van den Broek et al[31]2011NetherlandsProspective48153NBIPit patternNeoplastic/non-neoplastic
Togashi et al[32]2009JapanProspective50107Magnifying chromoendoscopyPit patternNeoplastic/non-neoplastic
Kiesslich et al[33]2003GermanyProspective84118Magnifying chromoendoscopyPit patternNeoplastic/non-neoplastic
Hurlstone et al[34]2005United KingdomProspective350288Magnifying chromoendoscopyPit patternNeoplastic/non-neoplastic
Tischendorf et al[35]2007GermanyProspective52100NBI-MEPit patternNeoplastic/non-neoplastic
Tischendorf et al[35]2007GermanyProspective47100Magnifying chromoendoscopyPit patternNeoplastic/non-neoplastic
Su et al[36]2006ChinaProspective79110ChromoendoscopyPit patternNeoplastic/non-neoplastic
Dos Santos et al[37]2012BrazilProspective69120Magnifying chromoendoscopyPit patternNeoplastic/non-neoplastic
Dos Santos et al[38]2010BrazilProspective72137Magnifying chromoendoscopyPit patternNeoplastic/non-neoplastic
Rogart et al[39]2008United StatesProspective131265NBIPit patternNeoplastic/non-neoplastic
Apel et al[40]2006GermanyProspective158273ChromoendoscopyPit patternNeoplastic/non-neoplastic
Van den Broek et al[41]2008NetherlandsProspective5098NBI-MEPit patternNeoplastic/non-neoplastic
Chiu et al[42]2007ChinaProspective133180Magnifying chromoendoscopyPit patternNeoplastic/non-neoplastic
Liu et al[22]2008ChinaRetrospective223451Magnifying chromoendoscopyPit patternNeoplastic/non-neoplastic
Figure 2
Figure 2 Quality assessment of the included studies.
Diagnostic efficacy

In total, 19227 colorectal lesions in 14674 patients were identified in the 31 included studies. Table 3 summarizes the pooled sensitivity, specificity, DOR, and AUC in each category of the JNET and Pit pattern classifications corresponding to the pathological results.

Table 3 Summary of the results of each category for the Japan Narrow-band-imaging Expert Team classification and the Pit pattern classification corresponding to histological diagnosis in the included studies.
JNETSensitivitySpecificityDORAUCPit patternSensitivitySpecificityDORAUC
10.73 [0.55, 0.85]0.99 [0.97, 1.00]245 [64, 936]0.97 [0.95, 0.98]II0.76 [0.62, 0.86]0.96 [0.88, 0.98]68 [15, 309]0.92 [0.90, 0.94]
2A0.88 [0.78, 0.94]0.72 [0.64, 0.79]19 [11, 33]0.84 [0.81, 0.87]IIIL + IV0.80 [0.67, 0.89]0.80 [0.74, 0.86]17 [8, 34]0.87 [0.83, 0.89]
2B0.56 [0.47, 0.64]0.91 [0.79, 0.96]13 [7, 24]0.72 [0.68, 0.76]IIIS + VI-L0.45 [0.23, 0.69]0.88 [0.75, 0.94]6 [1, 26]0.79 [0.75, 0.82]
30.51 [0.42, 0.61]1.00 [1.00, 1.00]801 [267, 2398]0.90 [0.87, 0.93]VN + VI-H0.73 [0.55, 0.85]0.99 [0.98, 1.00]449 [93, 2182]0.98 [0.97, 0.99]
Non-neoplastic0.73 [0.55, 0.85]0.99 [0.97, 1.00]245 [64, 936]0.97 [0.95, 0.98]Non-neoplastic0.86 [0.81, 0.90]0.94 [0.90, 0.96]88 [48, 156]0.95 [0.93, 0.97]

Ten studies[5,13-21] involving 13479 colorectal lesions reported the diagnostic efficacy of the JNET classification. The pooled values for each category were as follows. Type 1 (non-neoplastic): Sensitivity, 0.73 (95%CI: 0.55-0.85); specificity, 0.99 (95%CI: 0.97-1.00) (Figure 3A); DOR, 245 (95%CI: 64-936); and AUC, 0.97 (95%CI: 0.95-0.98) (Figure 4A). Type 2A: sensitivity, 0.88 (95%CI: 0.78-0.94); specificity, 0.72 (95%CI: 0.64-0.79) (Figure 8A); DOR, 19 (95%CI: 11-33); and AUC, 0.84 (95%CI: 0.81-0.87) (Figure 4B). Type 2B: sensitivity, 0.56 (95%CI: 0.47-0.64); specificity, 0.91 (95%CI: 0.79-0.96) (Figure 5A); DOR, 13 (95%CI: 7-24); and AUC, 0.72 (95%CI: 0.68-0.76) (Figure 4C); Type 3: sensitivity, 0.51 (95%CI: 0.42-0.61); specificity, 1.00 (95%CI: 1.00-1.00) (Figure 6A); DOR, 801 (95%CI: 267-2398); and AUC, 0.90 (95%CI: 0.87-0.93) (Figure 4D).

Figure 3
Figure 3 Forest plots of pooled sensitivity and specificity. A: Japan Narrow-band-imaging Expert Team type 1; B: Pit pattern II.
Figure 4
Figure 4 Summary receiver operating characteristic of the Japan Narrow-band-imaging Expert Team classification. To diagnose colorectal lesions with the corresponding 95% confidence region. A: Type 1; B: Type 2A; C: Type 2B and Type 3. SROC: Summary receiver operating characteristic.
Figure 5
Figure 5 Forest plots of pooled sensitivity and specificity. A: Japan Narrow-band-imaging Expert Team type 2B; B: Pit pattern IIIS + VI-L.
Figure 6
Figure 6 Forest plots of pooled sensitivity and specificity. A: Japan Narrow-band-imaging Expert Team type 3; B: Pit pattern VN + VI-H.

Twenty-one studies[5,13,21-42] involving 6150 colorectal lesions reported the diagnostic value of the Pit pattern classification. The pooled values for each category were as follows. Types I + II (non-neoplastic): sensitivity, 0.86 (95%CI: 0.81-0.90); specificity, 0.94 (95%CI: 0.90-0.96) (Figure 7); DOR, 88 (95%CI: 48-156); and AUC, 0.95 (95%CI: 0.93-0.97) (Figure 9A). Types IIIL + IV: sensitivity, 0.80 (95%CI: 0.67-0.89); specificity, 0.80 (95%CI: 0.74-0.86) (Figure 8B); DOR, 17 (95%CI: 8-34); and AUC, 0.87 (95%CI: 0.83-0.89) (Figure 9B). Types IIIS + VI-L: sensitivity, 0.45 (95%CI: 0.23-0.69); specificity, 0.88 (95%CI: 0.75-0.94) (Figure 5B); DOR, 6 (95%CI: 1-26); and AUC, 0.79 (95%CI: 0.75-0.82) (Figure 9C). Types VN + VI-H: sensitivity, 0.73 (95%CI: 0.55-0.85); specificity, 0.99 (95%CI: 0.98-1.00) (Figure 6B); DOR, 449 (95%CI: 93-2182); and AUC, 0.98 (95%CI: 0.97-0.99) (Figure 9D).

Figure 7
Figure 7 Forest plots of pooled sensitivity and specificity of non-neoplastic lesions by Pit pattern.
Figure 8
Figure 8 Forest plots of pooled sensitivity and specificity. A: Japan Narrow-band-imaging Expert Team type 2A; B: Pit pattern IIIL + IV.
Figure 9
Figure 9 Summary receiver operating characteristic of Pit pattern classification. To diagnose colorectal lesions with the corresponding 95% confidence region. A: II; B: IIIL + IV; C: IIIS + VI-L and VN + VI-H. SROC: Summary receiver operating characteristic.

The Student’s t-test was used to compare the sensitivities, specificities, and AUCs of each corresponding category of these two classifications. A statistically significant difference was found in sensitivity between JNET Type 3, and Pit pattern Types VN + VL-H (P < 0.05), however, no significant difference was found in specificity, and AUC. The results showed that no significant differences was found between the remaining categories among sensitivities, specificities, and AUCs: JNET Type 1 (non-neoplastic) vs Pit pattern Types I + II (non-neoplastic), JNET Type 2A vs Pit pattern Types IIIL + IV, and JNET Type 2B vs Pit pattern Types IIIS + VI-L.

Heterogeneity analysis

Significant heterogeneity existed among the included studies. Estimation of Spearman’s correlation coefficient (coef) and the P-value for each category of the JNET and Pit pattern classifications are shown in Table 4. Significant differences were noted for JNET Type 1 (non-neoplastic) (coef. = 0.14, P = 0.02), JNET Type 3 (coef. = -0.17, P = 0.03), and Pit pattern Types I + II (non-neoplastic) (coef. = -0.12, P = 0.02), while for the remaining subtypes of these two classifications, there were no significant differences. These results indicated the threshold effect existed in JNET Types 1, 3, and Pit pattern Types I + II. Additionally, for the non-threshold effect, we performed meta-regression analysis, including the population in the study (Asian or non-Asian), design of the study (retrospective or prospective), patient sample size (≥ 100 or < 100), QUADAS-2 score (≥ 7 or < 7), publication year (before, or after 2014), as well as the type of endoscopy as covariates. The sources of potential heterogeneity in the sensitivity and specificity were detected by univariate regression analysis, and the results are shown in Table 5. Deeks’ Funnel Plot Asymmetry Test revealed no publication bias in each category of these two classifications (Table 6).

Table 4 Summary of the results of Spearman’s correlation coefficient of each category for the Japan Narrow-band-imaging Expert Team classification and the Pit pattern classification corresponding to histological diagnosis in the included studies.
JNET (type)CoefP valuePit pattern (type)CoefP value
Non-neoplastic-0.120.02
Type 1 (Non-neoplastic)0.140.02II0.450.20
Type 2A-0.700.49IIIL + IV-0.240.06
Type 2B-1.001.00IIIS + VI-L0.410.17
Type 3-0.170.03VN + VI-H1.001.00
Table 5 Summary of the results of meta-regression analysis of each category for the Japan Narrow-band-imaging Expert Team classification and the Pit pattern classification corresponding to histological diagnosis in the included studies.
ClassificationTypeP value

ProdesignSampsizeQscoreEndoscopyCategoryCountryPubyear
JNETType 10.260.630.06
Type 2A0.880.580.760.00
Type 2B
Type 30.570.120.19
Pit patternII0.520.000.040.350.00
IIIL + IV0.630.770.410.330.770.000.09
IIIS + VI-L0.350.350.660.350.350.210.02
VN + VI-H
Non-neoplastic0.490.600.040.520.050.030.00
Table 6 Summary of the results of Deek’s test for publication bias of each category for the Japan Narrow-band-imaging Expert Team classification and the Pit pattern classification corresponding to histological diagnosis in the included studies.
JNET (type)n of studyPubbias (P value)Pit pattern (type)No. of studyPubbias (P value)
Type 180.26II50.54
Type 2A100.62IIIL + IV100.41
Type 2B90.52IIIS + VI-L60.09
Type 390.50VN + VI-H80.91
Non-neoplastic80.26Non-neoplastic230.13
DISCUSSION

To best of our knowledge, no meta-analysis has been reported that systematically compared the diagnostic efficacy of detailed histologic characteristics and interobserver diagnostic agreements between the JNET and Pit pattern classifications. The present meta-analysis compared and evaluated the diagnostic outcomes of each category of these two classifications corresponding to the histological diagnosis. Our results revealed that the diagnostic performance of the JNET classification is equivalent to the Pit pattern classification in each corresponding category. We also proposed a treatment strategy for colorectal lesions using the JNET classification with its confidence level.

Endoscopic diagnosis via magnification colonoscopy has been reported to provide high diagnostic accuracy and improve the prognosis of colorectal lesions. The Pit pattern classification is the most frequently used criteria for the accurate diagnosis of colorectal neoplasms. To date, several trials[5,13-21] have evaluated the efficacy of the JNET classification, which provides useful criteria for optical-histologic diagnoses of colorectal lesions. Whereas, JNET Types 1 and 2A correspond to Pit pattern Types I + II, and Type IIIL + IV, respectively; JNET Type 2B and 3 correspond to Pit pattern Types IIIS + VI-L and VI-H + VN, respectively. However, there are some differences between the JNET classification and Pit pattern classification. The Pit pattern classification is only based on the surface structure of lesions, while the JNET classification is based on the surface structure combined with the microvascular structure of lesions. In addition, the JNET classification is more concise in terms of guiding appropriate treatment strategies. According to our proposed treatment strategy, most JNET Type 1 lesions are HPs, which generally do not require resection and could be followed up by endoscopy. However, whether using the JNET classification or Pit pattern classification, it is difficult to distinguish between HP and SSL with endoscopy. Endoscopic resection is also recommended if the colorectal lesion is large or tends to enlarge obviously with endoscopy follow-up. Type 2A lesions could be resected by polypectomy or endoscopic mucosal resection (EMR). Type 2B lesions should be resected en bloc by endoscopic submucosal dissection (ESD) to obtain a precise histologic diagnosis concerning the invasion depth and determine endoscopic curability. If the lesion is relatively small, EMR is also recommended. Lastly, surgical resection is recommended for Type 3 lesions. However, few studies[5,13-21] have been published that have used the JNET classification for treatment of colorectal lesions in practice.

The JNET Type 1 is considered to have as high diagnostic efficacy as the Pit pattern Type II, moreover, no significant difference was found between these categories in this meta-analysis, which implies that the magnification technology could accurately distinguish non-neoplastic from neoplastic lesions and guide clinicians to formulate the appropriate treatment.

In this study, JNET Type 2B had a relatively low pooled sensitivity and AUC of 0.56 and 0.72, respectively. Interestingly, similar results were also obtained for Pit pattern Types IIIS + VI-L, with a sensitivity of 0.45 and AUC of 0.79. Through data analysis, it was found that, in these two categories, the evaluation regarding the dysplasia of some lesions was too low, or the depth of invasion was too shallow. Firstly, one reason might be that a large lesion often contained several pathological features, for example, a lesion contained two or more histological features of HGD, SM-s carcinoma, and SM-d carcinoma at the same time. Most of the surface structures showed the features of HGD, or SM-s carcinoma, while, only a focal, or the deep part of these lesions showed the features of SM-d carcinoma; therefore, the lesions were identified as Type 2B. Secondly, due to the large size, or the special location of the lesions, the endoscopist might be unable to observe the full picture of these lesions, which might be one reason for the low sensitivity. In addition, due to the spontaneous or contact bleeding of some large lesions, the blood might be attached to the surface of the lesions and then affect the judgment of pathological Types. Therefore, for large lesions, special location lesions and pedicled lesions, endoscopists are required to observe carefully, to obtain the whole picture of the lesions, and make a comprehensive judgement, which might be helpful in improving sensitivity.

Generally, SM-d carcinomas correspond to JNET Type 3. Our meta-analysis showed that JNET Type 3 has a lower sensitivity than that of Pit pattern Type VN + VL-H. Some polypoid advanced lesions could have a slightly less irregular NBI appearance than Type 3 because the surface microvillous structure persisted, which might be classified into JNET Type 2B. Thus, it has been proposed that Type 2B can be divided into two subtypes, Type 2B-low, and Type 2B-high[14]. The classification subtypes help to optimize the choice of treatment strategies, which also indicates that the JNET classification may need to be updated and optimized by experts to further improve the sensitivity of diagnosis. In addition, two studies[5,14] suggested that the endoscopist needed to perform an additional Pit pattern diagnosis using chromoendoscopy to differentiate Type 2B from Type 3, which might help to improve sensitivity; however, this requires further validation. In terms of specificity, compared with the Pit pattern classification, the overall JNET types were slightly higher, which was attributed to the evaluation of vessel structure by NBI-ME.

There are several limitations to this meta-analysis. First, the high degree of statistical heterogeneity with a high I2 value could not be avoided. The quality of endoscopic images, type of endoscopy, size of the population, year of publication, and experience of the endoscopists (expert or non-expert) possibly affected the heterogeneity of the included studies. For the Pit pattern classification, chemical staining magnifications were used in most of the included studies, but several studies used electronic staining magnification, and a few studies also used non-magnifying technology, which might have affected the results. Second, previous studies of the JNET classification were all retrospective single-center studies and the included populations were all Japanese. This indicates a potential need for large-scale prospective multi-center validation studies of the JNET classification in the future. Additionally, it is better to compare the JNET classification with the Pit pattern classification in the same endoscopic and histopathologic center.

CONCLUSION

In conclusion, this meta-analysis has shown that the diagnostic efficacy of the JNET classification is equivalent to that of the Pit pattern classification as both classifications are divided into four major categories according to similar histopathology. The sensitivity of JNET Type 2B can be further improved by differentiating subtypes and combining it with the Pit pattern classification. Due to its simpler and clearer application, it is easier to guide the choice of treatment strategy, which suggests that we can promote the application of the JNET classification for colorectal lesions in the clinic. However, future prospective multi-center studies with a uniform endoscopic and histopathology protocol are required to validate our results.

ARTICLE HIGHLIGHTS
Research background

The Pit pattern classification using magnifying chromoendoscopy is the established and traditional choice for diagnosing colorectal lesions. The Japan Narrow-band imaging (NBI) Expert Team (JNET) classification is a novel NBI magnifying endoscopic classification, which focuses on vessel and surface patterns to diagnose colorectal lesions. However, the comparative diagnostic efficacy of the JNET classification is inconclusive.

Research motivation

Several studies have proposed that the JNET classification of colorectal lesions via NBI magnifying endoscopy is a useful and objective tool for differentiating the gross type of colorectal lesions. However, to what extent we can trust the results of the JNET classification, and whether the Pit pattern classification can be replaced by the JNET classification are aspects that remain unclear.

Research objectives

To perform a systematic review and meta-analysis on the diagnostic efficacy of each category of the JNET and Pit pattern classifications for colorectal lesions.

Research methods

A systematic literature search was performed using PubMed, Embase, the Cochrane Library and Web of Science. The pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve (AUC) of each category of the JNET and Pit pattern classifications were calculated.

Research results

A total of 19227 colorectal lesions in 31 studies were included. The diagnostic performance of the JNET classification was equivalent to the Pit pattern classification in each corresponding category. The pooled sensitivity, specificity, and AUC for each category of the JNET classification were as follows: 0.73 (95%CI: 0.55-0.85), 0.99 (95%CI: 0.97-1.00), and 0.97 (95%CI: 0.95-0.98), respectively, for Type 1; 0.88 (95%CI: 0.78-0.94), 0.72 (95%CI: 0.64-0.79), and 0.84 (95%CI: 0.81-0.87), respectively, for Type 2A; 0.56 (95%CI: 0.47-0.64), 0.91 (95%CI: 0.79-0.96), and 0.72 (95%CI: 0.68-0.76), respectively, for Type 2B; 0.51 (95%CI: 0.42-0.61), 1.00 (95%CI: 1.00-1.00), and 0.90 (95%CI: 0.87-0.93), respectively, for Type 3.

Research conclusions

Our study found that the diagnostic efficacy of the JNET classification is equivalent to that of the Pit pattern classification as both classifications are divided into four major categories according to similar histopathology.

Research perspectives

The JNET classification is easier for guiding the choice of treatment strategy, which suggests that we can promote the application of the JNET classification for colorectal lesions in the clinic. However, future prospective multi-center studies with uniform endoscopic and histopathology protocol are required to validate our results.

Footnotes

Manuscript source: Unsolicited manuscript

Specialty type: Gastroenterology and hepatology

Country/Territory of origin: China

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): B

Grade C (Good): 0

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Chiba H S-Editor: Yan JP L-Editor: Webster JR P-Editor: Liu JH

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