Cheng M, Yu Y, Watanabe T, Yoshimoto Y, Kaji S, Yube Y, Kaneda M, Orita H, Mine S, Wu YY, Fukunaga T. Evaluation of three lymph node staging systems for prognostic prediction in gastric cancer: A systematic review and meta-analysis. World J Gastrointest Oncol 2025; 17(3): 98103 [DOI: 10.4251/wjgo.v17.i3.98103]
Corresponding Author of This Article
Tetsu Fukunaga, MD, PhD, Chief Doctor, Department of Upper Gastroenterological Surgery, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo Ward, Tokyo 113-8431, Japan. t2fukunaga@juntendo.ac.jp
Research Domain of This Article
Oncology
Article-Type of This Article
Systematic Reviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Ming Cheng, Yang Yu, Takehiro Watanabe, Yutaro Yoshimoto, Sanae Kaji, Yukinori Yube, Munehisa Kaneda, Hajime Orita, Shinji Mine, You-Yong Wu, Tetsu Fukunaga, Department of Upper Gastroenterological Surgery, Juntendo University School of Medicine, Tokyo 113-8431, Japan
Ming Cheng, You-Yong Wu, Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou 215004, Jiangsu Province, China
Yang Yu, Department of Gastrointestinal Surgery, Peking University Cancer Hospital, Beijing 100142, China
Co-corresponding authors: You-Yong Wu and Tetsu Fukunaga.
Author contributions: Cheng M and Yu Y acquired, analyzed, and interpreted the data, drafted the manuscript, and approved the final manuscript; Watanabe T, Yoshimoto Y, and Kaji S revised the manuscript and approved the final manuscript; Kaneda M provided guidance on statistical analysis and support for the article’s publication fees; Mine S, Yube Y, and Orita H interpreted the data, revised the manuscript, and approved the final manuscript; Wu YY acquired and interpreted the data, and approved the final manuscript; Fukunaga T conceptualized and designed the study, critically revised the manuscript, and approved the final manuscript.
Supported by the Clinical Medical Team Introduction Program of Suzhou, No. SZYJTD201804.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Tetsu Fukunaga, MD, PhD, Chief Doctor, Department of Upper Gastroenterological Surgery, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo Ward, Tokyo 113-8431, Japan. t2fukunaga@juntendo.ac.jp
Received: June 18, 2024 Revised: November 8, 2024 Accepted: December 25, 2024 Published online: March 15, 2025 Processing time: 241 Days and 8.1 Hours
Abstract
BACKGROUND
Lymph node status is a critical prognostic factor in gastric cancer (GC), but stage migration may occur in pathological lymph nodes (pN) staging. To address this, alternative staging systems such as the positive lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) were introduced.
AIM
To assess the prognostic accuracy and stratification efficacy of three nodal staging systems in GC.
METHODS
A systematic review identified 12 studies, from which hazard ratios (HRs) for overall survival (OS) were summarized. Sensitivity analyses, subgroup analyses, publication bias assessments, and quality evaluations were conducted. To enhance comparability, data from studies with identical cutoff values for pN, LNR, and LODDS were pooled. Homogeneous stratification was then applied to generate Kaplan-Meier (KM) survival curves, assessing the stratification efficacy of three staging systems.
RESULTS
The HRs and 95% confidence intervals for pN, LNR, and LODDS were 2.16 (1.72-2.73), 2.05 (1.65-2.55), and 3.15 (2.15-4.37), respectively, confirming all three as independent prognostic risk factors for OS. Comparative analysis of HRs demonstrated that LODDS had superior prognostic predictive power over LNR and pN. KM curves for pN (N0, N1, N2, N3a, N3b), LNR (0.1/0.2/0.5), and LODDS (-1.5/-1.0/-0.5/0) revealed significant differences (P < 0.001) among all prognostic stratifications. Mean differences and standard deviations in 60-month relative survival were 27.93% ± 0.29%, 41.70% ± 0.30%, and 26.60% ± 0.28% for pN, LNR, and LODDS, respectively.
CONCLUSION
All three staging systems are independent prognostic factors for OS. LODDS demonstrated the highest specificity, making it especially useful for predicting outcomes, while pN was the most effective in homogeneous stratification, offering better patient differentiation. These findings highlight the complementary roles of LODDS and pN in enhancing prognostic accuracy and stratification.
Core Tip: In our study, the strength of our study lies in the simultaneous inclusion of lymph nodes stage, positive lymph node ratio, and log odds of positive lymph nodes, summarizing the hazard ratios for gastric cancer prognosis after surgery, and comparing the hazard ratios to evaluate their prognostic prediction ability. We combined data from studies with the same cutoff values and plotted Kaplan-Meier survival curves for overall survival. Comparing the relative survival differentiation rates within the stratifications of the three nodal systems.
Citation: Cheng M, Yu Y, Watanabe T, Yoshimoto Y, Kaji S, Yube Y, Kaneda M, Orita H, Mine S, Wu YY, Fukunaga T. Evaluation of three lymph node staging systems for prognostic prediction in gastric cancer: A systematic review and meta-analysis. World J Gastrointest Oncol 2025; 17(3): 98103
Gastric cancer (GC) ranks as the fifth most common cancer and stands as the fourth leading cause of cancer-related mortality worldwide, making it a major public health concern[1,2]. Although the global incidence and mortality rates of GC have been decreasing in recent decades, the incidence among individuals under 50 years has been gradually increasing. Regional variations exit, with Asia exhibiting the highest rates while Europe and North America demonstrate comparatively lower rates[2]. The etiology of GC is multifactorial, commonly associated with Helicobacter pylori infection, smoking, alcohol consumption, chronic gastric ulcers, gastric polyps, chronic atrophic gastritis, and family history[3-5]. Most GC symptoms are insidious, and a significant number of patients lack regular medical check-ups, leading to delayed diagnosis at advanced stages. Despite the possibility for radical gastrectomy with lymphadenectomy in patients with advanced GC, however, their survival rates are significantly lower than those diagnosed at early stages. With advancements in diagnostic technologies and the implementation of enhanced screening protocols, early-stage detection of GC is now feasible even in the absence of clinical symptoms. Currently, surgery remains the only effective treatment; however, local recurrence and distant metastasis rates remain high. In the case of recurrence, no effective curative treatments are currently available. Therefore, it is crucial for clinicians to accurately assess postoperative recurrence risk, reliably predict patient prognosis, and implement appropriate interventions to extend survival and improve outcomes.
Various clinical indicators, including blood tumor markers, tumor differentiation, histological type, tumor infiltration, and lymph node metastasis, are usetilized for preliminary prognosis evaluation[6,7]. However, the 8th edition of the Tumor node metastasis (TNM) staging system for GC, published by the International Union Against Cancer (UICC) and the American Joint Committee on Cancer (AJCC) in 2016, is the primary staging system in clinical practice, with pathological lymph nodes (pN) staging playing a crucial role. The TNM classification requires the dissection of at least 15 lymph nodes for accurate staging, however, this requirement is often not met practically, especially in Western countries[8]. Clinicians frequently observe significant prognostic differences among patients with the same TNM stage receiving standardized treatment[9,10]. Consequently, accurate staging and subsequent treatment to improve patient prognosis remain contentious. Many researchers have proposed that lymph node ratio (LNR) is a significant prognostic parameter, demonstrating similar effects in various cancers[11-13]. Metastatic lymph node involvement is a crucial variable in GC, associated with poor prognosis. Some studies have shown that with the expansion of lymph node dissection, the number of lymph nodes significantly increases, potentially leading to stage migration in pN staging[14,15]. Simultaneously, studies indicate that LNR can prevent stage migration in the TNM classification system. However, Bilici et al[16] found that compared to the numerical-based pN staging, LNR-based staging does not have a clear prognostic advantage.
Currently, in the prognostic prediction of lymph node-negative GC, LNR0 is consistent with pN0 when staging based on the proportion of positive lymph node. However, some researchers have found that the prognosis of patients varies with the number of lymph nodes dissected. Therefore, a novel solution was recently proposed: Log odds of positive lymph nodes (LODDS)[17-19]. LODDS could further distinguish prognosis of pN0 stage in GC, and it was first introduced in breast cancer. It has also showed to be a good prognostic indicator for both lymph node-positive and lymph node-negative patients[20]. It was later validated by several other cancers, including GC. Some researchers found that LODDS classification was superior to LNR and pN for predicting the prognosis of GC with fewer than 15 lymph nodes examined and in the pN0 stage[21]. However, Liu et al[22] found that among the staging systems of pN0, LNR and LODDS, LNR staging was superior to both pN and LODDS staging.
LNR and LODDS are considered important prognostic predictive factors, and many researchers recommended them as staging methods for predicting prognostic GC. However, due to conflicting results in studies on LNR and LODDS, as well as previous research designs that primarily focused on capable of prediction either LNR or LODDS staging alone or only comparing two nodal staging systems at a time, it has been challenging to gain fully understanding of the relative merits in the three nodal staging systems. While Zhu et al[23] and Li et al[24] conducted meta-analyses on LNR and LODDS as prognostic predictive factors for GC, they did not compare the three nodal staging systems simultaneously. So, they were unable to definitively determine which system exhibited the strongest predictive ability for GC prognosis. Therefore, it is necessary to perform a meta-analysis to simultaneously evaluate the three nodal staging systems. Additionally, many researchers, particularly regarding cutoff values, lack uniform criteria. In term of homogeneous stratification comparison, previous studies simply merged different cutoff values intervals for analysis, which might result in erroneous conclusions[24]. Based on identical cutoff values intervals for pN, LNR, and LODDS from different studies, we merged same groups to evaluate the predictive ability of mutually stratification. We conducted a systematic review and meta-analysis to evaluate the prognostic predictive capability of the pN, LNR, and LODDS. Subgroup analyses were performed based on extent of lymph node dissection (D2 vs D1 and 2), race, publication year, inclusion of neoadjuvant therapy, number of examined lymph nodes (NLN), number of metastatic lymph nodes (MLN), and TNM staging version to evaluate the stratified prognostic prediction performance of pN, LNR, and LODDS.
MATERIALS AND METHODS
Search strategy
We conducted a systematic review of studies published up to March 2024, using the following terms and corresponding Medical Subject Headings (MeSH): (1) Stomach neoplasms (MeSH Terms) OR (neoplasm, stomach) OR (stomach neoplasm) OR (neoplasms, stomach) OR (gastric neoplasms) OR (gastric neoplasm) OR (neoplasm, gastric) OR (neoplasms, gastric) OR (cancer of stomach) OR (stomach cancers) OR (gastric cancer) OR (cancer, gastric) OR (cancers, gastric) OR (gastric cancers) OR (stomach cancer) OR (cancer, stomach) OR (cancers, stomach) OR (cancer of the stomach) OR (gastric cancer, familial diffuse); (2) (lymph nodes ratio) OR (LNR); (3) (log odds of positive lymph nodes) OR (LODDS); and (4) (1 AND 2) OR (1 AND 3). We followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines in reporting this systematic review (PRISMA 2009 Checklist), and our review protocol was registered with PROSPERO (Registration No. CRD42024533947).
Study selection
The inclusion criteria were as follows: (1) Studies including ≥ 100 pathologically confirmed GC patients who underwent R0 resection and had at least five years of follow-up; and (2) Retrospective or prospective cohort studies simultaneously reporting pN, LNR and LODDS according to the AJCC system with corresponding outcomes, or outcomes that can be extracted from studies.
The exclusion criteria were: (1) Distant metastasis before treatment; (2) Case reports, reviews, letters, and conference abstracts; (3) Studies lacking necessary statistical data; (4) Studies based on non-original research; and (5) Studies with duplicated population data.
Data extraction and outcome measures
Two authors (Cheng M, Yu Y) independently extracted the following data: Study design, country/region, number of patients, gender, age, type of surgery, tumor stage, extent of lymph node dissection, NLN, MLN, inclusion of neoadjuvant therapy, publication year, and TNM staging version. The primary observation measure was overall survival (OS). If hazard ratio (HR) and 95% confidence interval (CI) for survival were not reported, they were extracted from survival curves using Engage Digitizer software v11.1 and converted to lnHR and standard errors for further analysis[25]. Secondary observation measures included the extent of lymph node dissection (D2 vs D1 and 2), inclusion of neoadjuvant therapy, race, publication year, TNM staging version, NLN, MLN, and the prognostic prediction stratification effect of pN, LNR, and LODDS on five-year survival rates. Continuous variables presented as medians (range) were converted to mean ± SD using established methods[26]. After data extraction and analysis, discrepancies were resolved by a third author to eliminate errors. Due to unavailable data, not all studies could participate in each outcome analysis.
Statistical analysis
Studies with extractable valid data were included in the final meta-analysis. For dichotomous data, HR and 95%CI were calculated; for continuous data, mean difference and 95%CI were calculated. A random-effects model was performed if I² > 50%, indicating significant heterogeneity; otherwise, a fixed-effects model was performed. The main outcome meta-analysis was performed using Review Manager version 5.3. A P value < 0.05 indicated statistical significance. The meta-analysis aimed to evaluate the association of pN, LNR, and LODDS with the survival time of GC by summarizing HRs and 95%CI. I² statistics were used to assess statistical heterogeneity, with I² < 25% considered low heterogeneity and I² > 75% considered high heterogeneity[27]. Sensitivity analyses were conducted to assess the stability of the results by recalculating HRs and 95%CI after sequentially removing each study. Kaplan-Meier (KM) curves were used to evaluate the stratification differences of pN, LNR, and LODDS, and mean and SD of relative survival difference rates were used to assess the superiority of three nodal staging systems.
Risk of bias
The Cochrane risk of bias assessment tool was used to evaluate the degree of bias risk in each individual study (Supplementary Figure 1), and the most of included studies were of high quality. Begg’s and Egger’s tests were performed to quantitatively assess potential publication bias. We used R software v4.3.3 to perform Begg’s and Egger’s tests for publication bias and created funnel plots to assess the risk of various biases.
Subgroup analysis
We performed subgroup analyses based on parameters including the extent of lymph node dissection, (D2 vs D1 and 2), inclusion of neoadjuvant therapy, race, publication year, TNM edition, NLN, and MLN. Meta-regression models were used to investigate potential impact of these factors on heterogeneity and to assess the influence of each risk factor or potential confounder in the subgroup analyses. By comparing with HR and I² among subgroups, we analyzed reasons for the origin of heterogeneity.
RESULTS
Study characteristics
The flow diagram of the literature selection process used in current studies is shown in Figure 1. A total of 1983 articles were initially retrieved from PubMed, EMBASE, Web of Science, and the Cochrane Library up to March 1, 2024, using specific keywords with language restrictions. After manual screening and filtering, 12 studies were finally selected, encompassing a total of 20089 patients. The characteristics of the included studies are summarized in Table 1[28-36]. All patients underwent radical surgery, with a median age range of 58 to 71 years. Among these studies, eight involved Asian patients and four involved European patients. Data extracted included the extent of lymph node dissection (D2 vs D1 and 2), NLN, MLN, inclusion of neoadjuvant therapy, OS, and cutoff values of three nodal staging systems. All studies provided HRs and 95%CI for OS and respective cutoff values, as listed in Table 2. Nine studies directly reported HRs and 95%CI for OS, while HRs of three studies were extracted from KM curves using Engauge Digitizer software, which were then used to calculate and pool HRs and 95%CI based on the number of patients in each stratified group.
All 12 studies investigated the association between LNR and OS. The HR for OS from these studies were pooled and the analysis revealed a high degree of heterogeneity among the studies (I² = 97%; P < 0.001). Therefore, we used a random-effects model for analysis. As shown in Figure 2A, a higher LNR was associated with poorer OS, and there was a significant association between LNR and OS in GC. The HR was 2.05 (95%CI: 1.65-2.55), indicating that LNR could be considered an independent risk factor for predicting prognosis in GC. To evaluate the robustness and stability of the HR, we performed a sensitivity analysis by successively excluding each study and recalculating HR and 95%CI. Sensitivity plots created using R software (Supplementary Figure 2A) showed that no single study significantly altered the result, demonstrating the robustness and stability of the association between LNR and OS.
Figure 2 Forest plots.
A: Positive lymph node ratio and overall survival; B: Log odds of positive lymph nodes and overall survival; C: Pathological lymph nodes and overall survival. CI: Confidence interval.
LNR subgroup and meta-regression analyses: To identify the origin of heterogeneity in the LNR staging system, we conducted meta-regression and subgroup analyses. In subgroup analyses, the variables included the extent of lymph node dissection (D2 vs D1 and 2), inclusion of neoadjuvant therapy, race, publication year, TNM staging version, NLN, and MLN. Across all subgroup analyses, the OS of patients in each subgroup was significantly associated with LNR, as detailed in Supplementary Table 1. Based on the extent of lymph node dissection (D2 vs D1 and 2), HR for the D2 group was 2.63 (95%CI: 1.56-4.42, P < 0.001, I² = 87%), and HR for the D1 and D2 group was 1.81 (95%CI: 1.37-2.38, P < 0.001, I² = 97%). The prognostic predictive ability of LNR for OS did not significantly differ between the two groups (P < 0.001). Furthermore, despite the high heterogeneity, the D2 group exhibited a stronger predictive capability compared to the D1 and D2 group. Similarly, in other subgroup analyses, there were no significant differences in the predictive ability of LNR for OS among the various subgroups. However, HR values indicated that LNR exhibited a more advantageous predictive ability for OS in patients who received neoadjuvant therapy, Asian patients, studies published between 2017-2024, TNM 7th edition, and those with a higher NLN and MLN. The inclusion of neoadjuvant therapy, NLN, and MLN were potential origins of heterogeneity.
Publication bias: Publication bias was evaluated using both Begg’s test and Egger’s test. The P values for Begg’s test (Figure 3A) and Egger’s test (Supplementary Figure 3A) were 0.95 and < 0.001, respectively. The results indicated significant publication bias according to Egger’s test, whereas Begg’s test showed no publication bias. To ensure the reliability of combined HRs, we used the Trim and Fill analysis, which revealed no missing studies and indicated no significant change in the results (Supplementary Figure 3B), confirming a significant association between LNR and OS.
Figure 3 Begg’s plots.
A: Funnel plots showing association of positive lymph node ratio and overall survival; B: Funnel plots showing association of log odds of positive lymph nodes and overall survival; C: Funnel plots showing association of pathological lymph nodes and overall survival. HR: Hazard ratio.
Relationship between LODDS and survival outcomes
All 12 studies investigated the association between LODDS and OS. After combining HRs from these studies, significant heterogeneity was revealed among the studies (I² = 86%; P < 0.001). Therefore, a random-effects model was used for the analysis. As shown in Figure 2B, a higher LODDS was associated with poorer OS. The HR was 3.07 (95%CI: 2.15-4.37, P < 0.001), indicating a significant association between LODDS and OS. This suggests that LODDS could be considered an independent risk factor for predicting the prognosis of GC. A sensitivity analysis was performed to evaluate the robustness and stability of HR by successively excluding each study and recalculating HR. As shown in Supplementary Figure 2B, the association between LODDS and OS demonstrated good stability.
LODDS subgroup and meta-regression analyses: To identify the origin of heterogeneity in LODDS staging system, we conducted meta-regression and subgroup analyses. As shown in Supplementary Table 2, LODDS was closely associated with OS across all subgroup analyses, serving as an independent risk factor for predicting prognosis in GC in these subgroups. Furthermore, we found no significant differences in the predictive ability of LODDS for OS among the various groups. However, LODDS showed greater predictive capability for OS in the D1 and D2 group, patients without neoadjuvant therapy, Asian patients, studies published between 2017-2024, TNM 7th edition, and patients with a higher NLN and MLN. Regarding heterogeneity, the MLN and TNM edition could potentially be identified as the primary origin of heterogeneity.
Publication bias: The Begg’s test and Egger’s test were also conducted for evaluating the potential publication bias. Begg’s test (P = 0.02) and Egger’s test (P < 0.001) both indicated distinct publication bias, as shown Figure 3B and Supplementary Figure 3C. Consequently, we applied Trim and Fill analysis to ensure the reliability of HR. The funnel plot (Supplementary Figure 3D) showed no significant changes, with no hypothetical studies added, indicating that the HR remained stable. This further confirmed a significant association between LODDS and OS.
Relationship between pN and survival outcomes
All 12 studies investigated the association between pN and OS. After combining HRs from these studies, a high degree of heterogeneity was revealed (I² = 96%; P < 0.001). Therefore, a random-effects model was used for the analysis. As shown in Figure 2C, the HR was 2.16 (95%CI: 1.72-2.73, P < 0.001), indicating a significant association between pN and OS. This also suggests that pN could be considered an independent risk factor for predicting the prognosis of GC. In the sensitivity analysis, as shown Supplementary Figure 2C, the association between pN and OS demonstrated robustness and stability by successively excluding each study and recalculating HR.
pN subgroup and meta-regression analyses: To identify the origin of heterogeneity in pN staging system, we conducted meta-regression and subgroup analyses. As shown in Supplementary Table 3, pN was closely associated with OS across all subgroup analyses, serving as an independent risk factor for predicting prognosis in patients in these subgroups. As revealed by the various subgroup analyses, no significant differences in the prognostic predictive ability of pN for OS. Furthermore, pN showed greater predictive capability for OS in the D2 group, patients without neoadjuvant therapy, Asian patients, studies published between 2017-2024, TNM 7th edition, and those with a higher NLN and fewer MLN. Regarding heterogeneity, the extent of lymph node dissection, NLN, and MLN could potentially be identified as the primary origins of heterogeneity.
Publication bias: Begg’s test and Egger’s test were also conducted for evaluating the potential publication bias of pN. Begg’s test (P = 0.02) and Egger’s test (P < 0.001) both indicated distinct publication bias, as shown Figure 3C and Supplementary Figure 3E. Consequently, we applied Trim and Fill analysis to ensure the reliability of HR. The funnel plot (Supplementary Figure 3F) showed no significant changes, with no hypothetical studies added, indicating that the HR remained stable. This further confirmed a significant association between pN and OS.
Evaluation of prognostic prediction effect of pN, LNR, and LODDS on OS
In our meta-analysis, we collected 12 studies that simultaneously evaluated the prognostic prediction effect of pN, LNR, and LODDS on OS in GC. Since each study involved the same patient populations and similar study designs, the HR for three nodal staging systems were comparable. HR measures the hazard of a specific event occurring over time, with a lower HR indicating a lower risk of the event occurring. Therefore, we performed a meta-analysis to pool the HRs from different studies and compare the prognostic prediction capabilities of pN, LNR, and LODDS for OS in GC. The HRs were 3.15 (95%CI: 2.15-4.37) for LODDS, 2.16 (95%CI: 1.72-2.73) for pN, and 2.05 (95%CI: 1.65-2.55) for LNR, respectively (Figure 2). Comparing these HRs, we found that LODDS exhibited a superior prognostic prediction capability for OS in GC compared to LNR and pN. Additionally, pN had better prognostic prediction capability than LNR.
Homogenized stratified prognostic evaluation of pN, LNR, and LODDS
Among the 12 studies, there was no uniform standard for the cutoff values of pN, LNR, and LODDS, as shown in Table 2. Three studies used the following LODDS cutoff values (LODDS0 ≤ -1.5, -1.5 < LODDS1 ≤ -1.0, -1.0 < LODDS2 ≤ -0.5, -0.5 < LODDS3 < 0, LODDS4 > 0). Additionally, three studies used the following LNR cutoff values (LNR0, 0 < LNR1 ≤ 0.2, 0.2 < LNR2 ≤ 0.5, LNR3 > 0.5). The remaining studies either had different or unspecified cutoff values or lacked corresponding KM curves. We compared the staging and stratification of pN, LNR, and LODDS using the eighth edition pN staging cutoff values (N0, N1, N2, N3a, N3b). First, data from studies with the same cutoff values were combined, and KM curves for OS were plotted. As shown in Figure 4A-C, it was observed that all three nodal staging systems exhibited statistically significant differences in the stratification of GC. Subsequently, the relative survival differentiation rates within the subgroups of three nodal staging systems were compared. As shown Figure 4D, it was observed that the relative survival differentiation rate between LNR2 and LNR3 was higher, suggesting that the survival rates in this range of GC could be further stratified. Compared to LNR stratification, pN and LODDS showed greater advantages. In terms of MD and SD of survival differentiation rates, the MD and SD of relative survival differences at 60 months were 27.93% ± 0.29%, 41.70% ± 0.30%, and 26.60% ± 0.28% for pN, LNR, and LODDS respectively, which revealed that LNR stratification had the highest survival differentiation rate, indicating greater sensitivity. Additionally, the smaller SD in pN stratification indicated more uniform and precise prognostic stratification for GC patients.
Figure 4 Overall survival according to the different staging methods.
A: Positive lymph node ratio; B: Log odds of positive lymph nodes; C: Pathological lymph nodes; D: Relative survival difference rates among staging methods (lymph node ratio, log odds of positive lymph nodes, and pathological lymph nodes). Relative survival difference rates (LNR 0:1) = [(OS(LNR0) - OS(LNR1))/OS(LNR0)]. OS: Overall survival; LNR: Lymph node ratio; LODDS: Log odds of positive lymph nodes; pN: Pathological lymph nodes.
DISCUSSION
The TNM staging system for malignant tumors, developed by the UICC and AJCC, originated in the 1940s and was initially proposed by French scholar Pierre Denoix. Subsequently, the AJCC and UICC gradually established international staging standards, and the TNM staging system for GC was first introduced in the second edition, published in 1974. By 2016, the pN staging for GC had been updated to the eighth edition of the TNM system. Lymph node metastasis is one of the most important indicators for prognostic prediction in GC. Accurate pN staging is crucial for determining postoperative adjuvant treatment plans. From the first four editions, which were based on the location of MLN, to the fifth edition of the TNM staging system for GC the pN staging adopted the number of MLN as the criterion and stipulated that at least 15 lymph nodes were dissected. At the same time, 1-6 MLN were defined as N1, 7-15 as N2, and more than 15 as N3. Subsequently, there were numerous debates regarding the pN cutoff values[37-39]. Based on extensive research, the AJCC and UICC updated the metastatic lymph node cutoff values. The seventh edition established N1 (1-2), N2 (3-6), N3a (7-15), and N3b (> 15)[40]. These changes resulted in a more refined pN staging for GC, thereby enabling more timely treatment and more accurate prognosis assessment for GC patients.
The importance of TNM staging is now widely accepted globally, but many scholars still question the prognostic accuracy of the pN staging within the TNM system. In the term of lymph node dissection quantity, it is common in clinical practice that the total number of dissected or examined lymph nodes to be insufficient[41-44]. A comparison of national database cases between Japan and the United States by Ito et al[45] found a 5-year relative survival rate of 81.0% in Japan and 45.0% in the United States, mainly due to differences in the number of lymph nodes examined. Currently, D2 lymphadenectomy is recommended by experienced centers in the West, while in Japan, D2 lymph node dissection is the standard surgical approach[46]. D2 radical surgery accounts for about 29% of radical surgeries in Western countries, whereas the rate in Japan and South Korea can reach 60%-80%[47]. Inadequate lymph node dissection (< 15 lymph nodes) can limit the pathological staging accuracy of pN for GC. Additionally, extended lymphadenectomy may significantly increase the number of MLN, leading to stage migration in pN staging[42]. In GC without lymph node metastasis, differences in the number of lymph nodes dissected can result in survival differences[19,22]. Simultaneously, there is still ongoing debate over the cutoff values for pN staging[38,39]. To address these issues, some researchers have proposed potential alternative classification systems such as nomogram prognostic scoring models, NLN, LNR, and LODDS, aiming to improve the accuracy and effectiveness of prognostic prediction[19,22,48-50].
Currently, LNR and LODDS are the most extensively studied, with some scholars indicating that LNR and LODDS were superior to pN staging[29,41,51]. However, these studies primarily focused on comparing different LNR or LODDS stratifications with earlier versions of pN staging, lacking research on uniform homogenized stratification. Therefore, the debate over whether LNR and LODDS are superior to pN for prognostic prediction of GC continues. In our study, we combined HRs from 12 studies on pN staging, and the results indicated that pN is an independent factor for predicting the prognosis of GC. Subgroup analysis on the extent of lymph node dissection, inclusion of neoadjuvant therapy, race, publication year, TNM staging version, NLN, and MLN showed no significant differences in the prognostic prediction effect of pN staging on OS among GC patients across different subgroups. This confirms pN staging as an independent prognostic predictive factor.
LNR analysis
Previous studies found that insufficient lymph node dissection could lead to stage migration, particularly during perigastric lymphadenectomy (D1), with a stage migration rate of 10% to 15%, thereby reducing the prognostic accuracy of pN staging[52,53]. Okusa et al[54] were the first to propose LNR as a novel indicator for evaluating the prognosis of GC. The primary factors influencing LNR are the number of dissected or examined lymph nodes and the number of MLN. Some researchers observed that there was a significant correlation between the number of MLN and the NLN. The more lymph nodes dissected or examined, the greater the possibility of pN stage migration, whereas LNR staging might remain unchanged or even decrease[14,15].
In terms of LNR’s prognostic predictive effectiveness for GC, Lee et al[32] found that LNR was correlated with the number of MLN but not with the number of dissected or examined lymph nodes, thus minimizing the risk of stage migration. Therefore, using LNR for prognosis prediction could reduce the impact of insufficient lymph node dissection or examination. In a multicenter cohort study involving 3284 patients, Lee et al[55] found that LNR staging predicted patient prognosis better than pN staging, suggesting that LNR staging could replace pN staging for evaluating postoperative lymph node status. A study from the SEER database involving 9357 GC patients showed that despite most Western patients undergoing suboptimal lymph node dissection, LNR could still effectively predict patient prognosis[56]. However, Bilici et al[16] indicated that both LNR and pN staging are independent prognostic indicators for OS in GC, but the superiority of LNR over pN staging has not been definitively confirmed. In our study, the HR of LNR was combined, and the HR was 2.05 (95%CI: 1.65-2.55), indicating that LNR was an independent prognostic predictive factor for GC. In subgroup analysis, the predictive ability of LNR for OS did not significantly differ across subgroups defined by the extent of lymph node dissection, inclusion of neoadjuvant therapy, race, publication year, TNM staging version, NLN, and MLN. The origin of heterogeneity might include the inclusion of neoadjuvant therapy, NLN, and MLN.
Currently, the greatest controversy surrounding the role of LNR in prognostic prediction for GC lies in the choice of cutoff values. Many studies reported different LNR cutoff values, with some indicating advantages over pN staging in stratification[9,11,12]. The selection of different cutoff values might be related to researchers who conducted the study design, the number of cases, and the extent of lymph node dissection. Zhao et al[57] used X-tile plots to determine the optimal cutoff values, while others used receiver operating characteristic curves or commonly used cutoff values from previous studies to report their findings[58]. Nevertheless, there is no consensus on the optimal LNR cutoff values for GC.
LODDS analysis
In recent years, researchers observed that even with the same LNR, patients with different numbers of dissected lymph nodes had different prognoses. Therefore, a novel prognostic indicator, LODDS was proposed and has been studied in various cancers, including gastric, breast, bladder, and colorectal cancer[17-19,59]. However, different research centers have varying views on the prognostic role of LODDS. In colorectal cancer prognosis, Scarinci et al[17] found that LODDS had a better predictive role than LNR. Conversely, Song et al[60] argued that LNR was more suitable for prognostic evaluation than both pN and LODDS, noting that the complexity of LODDS might limit its clinical application. In the progonsis of GC, Sun et al[34] analyzed 2547 patients who underwent D2 radical surgery and discovered that LODDS served as an independent prognostic prediction factor. It is superior to pN and LNR in its ability to predict prognosis, particularly in reducing stage migration caused by insufficient lymph node dissection. Díaz Del Arco et al[41] observed that the advantage of LODDS lay in its reduced dependency on the NLN and its ability to predict prognosis even when fewer than 15 lymph nodes were dissected. This conclusion was also reached by Cao et al[30]. Conversely, Liu et al[22] found that LNR staging was the best predictor of OS in GC, whereas LODDS and pN are not. In our study, the HRs of LODDS were combined, and the HR was 3.15 (95%CI: 2.23-4.46), indicating that LODDS was an independent prognostic predictive factor for GC. In subgroup analysis, the predictive ability of LODDS staging for OS did not significantly differ across subgroups defined by the inclusion of neoadjuvant therapy, race, extent of lymph node dissection, publication year, TNM staging version, NLN, and MLN.
Although many researchers considered LODDS staging superior to pN and LNR staging as a novel prognostic predictive system for GC, controversy remained over its prognostic ability and the best selection of cutoff values for LODDS staging. Most researchers classified cutoff values with an interval of 0.5 to establish a LODDS staging system, such as LODDS ≤ -1.5, -1.5 < LODDS ≤ -1.0, -1.0 < LODDS ≤ -0.5, -0.5 < LODDS ≤ 0, and LODDS > 0. However, the specific stratification numbers or cutoff values vary across different studies, resulting in different evaluation outcomes for pN, LNR, and LODDS. For instance, Gu et al[29] found that pN and LODDS showed significant advantages over LNR, with LODDS having the potential to distinguish different prognoses in LNR0 and pN0 stage groups. Conversely, Liu et al[22] utilized different LNR cutoff values and found that LNR staging exhibited superior stratification ability compared to pN and LODDS staging. This was primarily attributed to the varying selections of cutoff values.
Advantages of pN, LNR, and LODDS
In our study, all three nodal systems (pN, LNR, and LODDS) were independent prognostic prediction factors for GC. However, controversy remains among scholars regarding which system was the most effective for predicting prognosis. Díaz Del Arco et al[41] found that the LODDS showed superior prognostic prediction capabilities over pN, especially in cases without lymph node metastasis or when fewer than 16 lymph nodes are dissected. However, in their stratification analysis, they used the seventh edition of pN staging without distinguishing between pN3a and pN3b, resulting in four layers for pN, while LODDS staging adopted five layers, lacking a uniform stratification standard. We observed from their study that LODDS showed significant advantages only when fewer than 16 lymph nodes were examined. Lee et al[32] indicated that all three classifications of LODDS, pN, and LNR were all applicable for prognostic prediction in GC, However, their study revealed that LODDS exhibited an advantage in stratified analysis compared to pN and LNR. They used five stratification layers for LODDS, LNR, and pN, observing that LODDS presented an advantage over pN, and LODDS, and generally, LODDS displayed superiority over LNR, which might be related to the LNR cutoff values employed. Li et al[24] conducted a meta-analysis and concluded that LODDS was superior to pN and LNR for predicting prognosis in GC. However, they simply combined data from different cutoff value groups, which possibly led to biased outcomes. Different cutoff values might lead to different evaluation outcomes for LNR and LODDS. As reported in many studies, it remains inconclusive which of pN, LNR, or LODDS stratification best predicts prognosis in GC due to the variability in LNR and LODDS cutoff values.
In our analysis, three studies used the same cutoff values for LNR (0.1/0.2/0.5), while others had different cutoff values or only one study per cutoff value. We combined data from studies with the same cutoff values and plotted KM survival curves for OS. Similarly, we combined data for pN (N0, N1, N2, N3a, N3b) from four studies, LNR from three studies (0.1/0.2/0.5), and LODDS (-1.5/-1.0/-0.5/0) from three studies and plotted KM survival curves for OS. Comparing the relative survival differentiation rates within the stratifications of the three nodal systems, as shown in figure, we observed that there were statistically significant differences among three nodal staging systems in stratifying OS for GC. In figures, we observed that the differentiation rate between LNR2 and LNR3 was the highest, suggesting the possibility of further stratifying survival rates within this range. Compared to LNR stratification, pN and LODDS showed greater advantages. In terms of MD and SD of survival differentiation rates, we observed that LNR had the highest differentiation rate, indicating higher sensitivity, while pN had the smallest SD, indicating more uniform prognostic stratification.
In clinical practice, we aim for both differentiation among stratifications (sensitivity) and uniformity within stratifications to avoid inaccurate prognosis predictions due to insufficient stratification. While many scholars reported significant advantages of LODDS in pN0 and LNR0, the authors contend that since pN0 and LNR0 have relatively high survival rates, LODDS may not be as crucial for stratification in these groups. The focus of prognostic evaluation systems lies in selecting appropriate cutoff values to ensure a balanced and uniform stratification while maintaining distinct differences between them. Hence, LODDS can complement pN and LNR staging systems and serve as an ideal prognostic staging system for GC, with its effectiveness depending on the choice of cutoff values. Our study found that among the three nodal systems-pN (N0, N1, N2, N3a, N3b), LNR (0.1/0.2/0.5), and LODDS (-1.5/-1.0/-0.5/0)-pN showed the best prognostic prediction effect for GC, followed by LODDS, which was superior to LNR. Given the current lack of consensus on the optimal cutoff values for LNR and LODDS in GC, establishing standardized protocols is imperative. Collaborative efforts to systematically review existing literature and clinical outcomes could greatly benefit the field, aiding in the development of consensus on specific cutoff values for both LNR and LODDS. Conducting multi-center studies with uniform definitions for these metrics may improve data collection processes and ultimately lead to more generalizable findings. At the same time, the complexity of calculating LNR and LODDS often poses challenges for clinicians in clinical practice. Streamlining these calculation methods could significantly enhance the integration of these prognostic metrics into routine workflows. By creating user-friendly tools or software to simplify calculations, clinicians would have easier access to LNR and LODDS data, improving their ability to make informed treatment decisions. This simplification not only reduces potential confusion but also encourages the broader implementation of these critical prognostic indicators in the management of GC.
The strength of our study lies in the simultaneous inclusion of pN, LNR, and LODDS, summarizing HRs for GC prognosis after surgery, and comparing HRs to evaluate their prognostic prediction ability. The results indicated that pN, LNR, and LODDS were independent predictors of patient survival after radical surgery, with LODDS demonstrating the strongest prognostic correlation, followed by pN and LNR. This suggested that LODDS has the most specificity for evaluating GC prognosis. However, several limitations exist in this meta-analysis. First, due to the lack of multifactorial HRs in some studies, we used univariate HR extracted from KM survival curves, which may lead to discrepancies in the combined HRs. Additionally, requiring studies to include pN, LNR, and LODDS results might have caused selection bias due to the limited number of articles. Secondly, all studies were retrospective with significant heterogeneity among them. Subgroup analysis identified the part origin of heterogeneity, but we did not perform further analysis due to the lack of original data. Other potential sources of heterogeneity include tumor location, size, and lymphatic invasion, which we do not assess due to the absence of original data. Thirdly, although we observed that pN stratification was superior to LODDS and LNR, this discrepancy was primarily attributed to cutoff values. For instance, Lee et al[32] utilized LODDS cutoff values of (-4/-2.5/-2/-0.5), demonstrating an advantage over pN and LNR. The limited number of studies with varying cutoff values prevented us from combining data, leading to potential bias in evaluating the superiority of the three nodal staging systems.
CONCLUSION
In conclusion, the results of this meta-analysis indicated that pN, LNR, and LODDS are all independent prognostic predictive predictors for GC. By comparing their respective HRs, we concluded that LODDS had the strongest prognostic specificity for GC, followed by pN, and then LNR. In terms of stratified staging, among the three nodal systems-pN (N0, N1, N2, N3a, N3b), LNR (0.1/0.2/0.5), and LODDS (-1.5/-1.0/-0.5/0)-pN showed the best prognostic prediction performance, followed by LODDS, which was superior to LNR. Different cutoff values can result in varying quality of prognostic prediction performance. Currently, the optimal cutoff values for LNR and LODDS have not been established. Therefore, large-scale clinical studies are needed to explore their potential as new prognostic factors and more accurate and sensitive stratification tools for GC.
ACKNOWLEDGEMENTS
I would like to sincerely thank all the faculty members in the Department of Upper Gastrointestinal Surgery at Juntendo University for their invaluable support and guidance.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Oncology
Country of origin: Japan
Peer-review report’s classification
Scientific Quality: Grade C
Novelty: Grade B
Creativity or Innovation: Grade B
Scientific Significance: Grade B
P-Reviewer: Gong ES S-Editor: Fan M L-Editor: A P-Editor: Zhang L
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