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. 2026 Jul 3;26:1839. doi: 10.1186/s12903-026-09103-x

Prognostic value of surgical margin and margin to depth of invasion ratio in oral squamous cell carcinoma: a meta-analysis

Jin Xu 1,#, Qian Cui 2,#, Yinqi Chen 3,#, Wenyuan Yu 4, Yong Xu 1, Yu Shao 5, Zhonghao Liu 1,4,✉, Zhenggang Chen 1,4,✉
PMCID: PMC13617838  PMID: 42399960

Abstract

Background

Oral squamous cell carcinoma (OSCC) refers to a prevalent cancer in the neck and head region, with poor prognosis and a high risk of recurrence after surgery. Surgical margin (SM) and the margin to depth of invasion ratio (MDR) are critical factors influencing survival outcomes. Currently, the impact of SM width and MDR on prognosis remains controversial. This study seeks to systematically compare the predictive value of SM width and MDR for OSCC prognosis, providing evidence for optimizing SM evaluation standards.

Methods

We systematically searched PubMed, Web of Science, Embase, and Cochrane Library for relevant studies from database inception to April 1, 2026. Two researchers separately reviewed the included studies and extracted the data. The quality of the included studies was assessed using the Quality In Prognosis Studies (QUIPS) tool. Meta-analysis was carried out via STATA 18.0 software, and the related summary plots were generated.

Results

A total of 41 studies involving 61,863 individuals were included. A poor SM was significantly associated with unfavorable overall survival (OS) (HR = 1.56, 95% CI 1.37–1.77, p < 0.001), disease-free survival (DFS) (HR = 1.72, 95% CI 1.29–2.28, p < 0.001), and recurrence-free survival (RFS) (HR = 1.99, 95%CI 1.53–2.58, p < 0.001). A low margin to depth of invasion ratio (MDR) was significantly associated with worse OS (HR = 1.36, 95% CI 1.21–1.54, p < 0.001) and DFS (HR = 1.40, 95% CI 1.12–1.74, p = 0.003). Subgroup analysis demonstrated that these associations might be influenced by different cutoff values, data analysis sources, and geographical factors.

Conclusion

This study revealed significant associations between SM, MDR, and the OSCC prognosis. Nevertheless, the clinical value of these indicators may be affected by variability in cutoff values across studies. Future large-scale, multicenter prospective studies are required to validate these findings and further investigate the prognostic impact of varying cutoff values.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12903-026-09103-x.

Keywords: Oral squamous cell carcinoma, Surgical margin, Margin to depth of invasion ratio, Overall survival, Disease-free survival, Recurrence-free survival, Meta-analysis

Background

Oral squamous cell carcinoma (OSCC), the major cancer of the neck and head, poses a growing disease burden across the globe. In 2020, global epidemiological data reported over 370,000 new cases and more than 170,000 deaths caused by OSCC [1]. Although significant advances have been made in comprehensive treatments, including surgery combined with radiotherapy and chemotherapy, the five-year survival rate (SR) for individuals with OSCC is under 60%. Distant metastasis and local recurrence stand as the leading causes of poor therapeutic outcomes [2]. The growing risk of incidence and death in OSCC patients continues to threaten their long-term survival and quality of life. Surgical resection serves as the cornerstone of OSCC treatment, and the status of the surgical margin (SM) directly affects tumor control and the survival outcomes of OSCC patients [3–5]. SMs are classified into three categories by the National Comprehensive Cancer Network: close SM (≤ 5 mm), clear SM (> 5 mm), and involved or positive SM (presence of tumor cells at the margin) [6, 7]. Currently, SM width is widely employed as a key metric for assessing OSCC prognosis. Prior meta-analyses have demonstrated that a SM width of less than 5 mm is significantly linked to poor prognosis [4]. However, recent studies suggest that even at higher SM cutoff values, poor prognosis may still occur [5]. This finding highlights the limitations of relying solely on SM width for prognostic assessment. Therefore, using SM width alone may fail to comprehensively reflect the true invasive characteristics of the tumor and local control. Although previous studies have examined the value of the ratio of SM to depth of invasion ratio (MDR) (defined as the ratio of SM to depth of invasion) in predicting OSCC, the conclusions remain inconsistent [4, 8]. Furthermore, a systematic analysis is lacking. Moreover, heterogeneity across studies limits the generalizability of these findings in clinical practice. Preliminary research reveals that the MDR serves as an independent indicator for predicting OSCC. Individuals with a low MDR have a significantly higher risk of recurrence and death [9]. Thus, this study integrated existing literature to assess the impact of SM and MDR on OSCC prognosis.

Methods

This study was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines [10]. The protocol was registered in the International Prospective Register of Systematic Reviews (registration number: CRD42025636196).

Search strategy

PubMed, Embase, Cochrane Library, and Web of Science databases were searched from database inception to April 1, 2026, and the literature language was limited to English. The search strategy was designed by combining subject terms and free text terms. The keywords included “margins of excision,” “mouth squamous cell carcinoma,” and “surgical margin”. Table S1 illustrates the search strategy. Additionally, to ensure a comprehensive search, reference lists of published systematic reviews were examined to identify potentially missed studies.

Inclusion and exclusion criteria

Inclusion criteria:

  • (i)

    study participants: individuals with a pathological diagnosis of OSCC;

  • (ii)

    exposure factors: the effect of SM width and MDR on prognosis at different cutoff values, with available data extracted from survival curves or reported HRs and 95% CI;

  • (iii)

    study type: cohort studies;

  • (iv)

    outcome measures: recurrence-free survival (RFS), overall survival (OS), and disease-free survival (DFS).

Exclusion criteria:

  • (i)

    cell experiments, case reports, animal studies, reviews, editorials, letters, conference abstracts;

  • (ii)

    studies with missing or severely erroneous data;

  • (iii)

    duplicate publications;

  • (iv)

    studies with inaccessible full text;

  • (v)

    duplicate cohort studies (articles published by the same research team, based on the same database, or with overlapping research populations, only the one with the larger sample size and the most recent publication date was included, and the rest of the duplicate or highly overlapping studies were excluded.

Literature selection and data extraction

The retrieved studies were managed via EndNote software. Two researchers separately screened titles and abstracts to include eligible studies according to the inclusion and exclusion criteria. Furthermore, irrelevant studies were removed upon a full-text review. Discrepancies were addressed by discussion or by consulting a third researcher. The extracted data from the eligible studies included country of study, sample size, study variables, publication year, first author, patient age, gender distribution, tumor stage, data sources, and survival outcomes. All the data were organized in Excel 2016. The safe SM/safe MDR defined by each study was used as the reference group, i.e., SM > corresponding cutoff values, MDR > corresponding cutoff values. The HR and 95% CI of OS, DFS and RFS were extracted. If the HR was not reported directly, the data were extracted from the survival curve [11].

Quality assessment

The risk of bias in included studies was independently assessed using the Quality in Prognosis Studies (QUIPS) tool [12]. The QUIPS tool is specifically designed to assess the risk of bias in studies on prognostic factors. It evaluates six domains: participant selection, loss to follow-up, measurement of prognostic factors, measurement of outcomes, control of confounding factors, and statistical analysis and reporting. A study was assessed to have a low, medium, or high risk of bias in each domain. The risk of bias assessment was completed independently by two researchers. Any inconsistencies were resolved by a third researcher. This approach ensured the quality and reliability of included studies and improved the accuracy of the analysis results.

Statistical analysis

The pooled HR and its 95% CI were used to analyze the effect of SM width and MDR on survival prognosis. The Q-test and I² statistic were employed to assess heterogeneity. If I² ≥ 50% and p < 0.1, a random-effects model was used; otherwise, a fixed-effects model was employed. In cases of substantial heterogeneity, subgroup analyses based on cutoff values, geographical regions, and data sources were carried out. Sensitivity analysis was conducted by removing individual studies one by one to examine the stability of the findings. Begg’s and Egger’s tests were employed to evaluate publication bias. If bias was found, the trim-and-fill approach was applied to correct it. STATA 18.0 software was applied to carry out statistical analyses. A two-tailed p-value of less than 0.05 suggested statistical significance.

Results

Literature search and screening process

5120 studies were initially selected, with 1,620 duplicates excluded. Upon reviewing titles and abstracts, 3,349 studies were removed. Full-text reviews of the remaining studies were conducted in accordance with the inclusion and exclusion criteria. The remaining 151 studies were subjected to full-text assessment, and a total of 110 studies were excluded. The relevant reasons for exclusion are detailed in Table S2. Ultimately, 41 studies were included [8, 9, 13–51]. Figure 1 illustrates the screening process.

Fig. 1.

Fig. 1

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Flow diagram of study selection

Basic characteristics of included studies

The 41 included studies came from 12 countries and involved a total of 61,863 patients aged 16–100 years. These studies were from 12 countries, including India, the United States, and China. Table 1 provides the basic characteristics of these studies. In addition, among the included studies, 36 studies focused on SM alone, 1 study on MDR alone, and 4 studies on both SM and MDR.

Table 1.

Basic characteristics of included studies

Study Year Country Sample size Age Sex(M/F) Tumor staging Variables Data analysis source Outcomes
Loree et al. 1990 America 398 19–95 242/156 T1-4 margin cutoff; 5 mm margin cutoff; Kaplan–Meier OS
Liao et al. 2008 China 827 49(25–83) 778/49 T(p)1–4 margin cutoff; 7 mm margin cutoff; Multivariable analysis OS, RFS
Nason et al. 2009 Canada 277 63.3 ± 12 — T

margin cutoff; OS-coutinious;

margin cutoff; RFS-5 mm

OS-Multivariate analyses

RFS-Kaplan–Meier

 OS, RFS
Chiou et al. 2010 China 110 53.7(26 —82) 103/77 T(p)1–4 margin cutoff;5 mm margin cutoff; Multivariable analysis DFS, RFS
Kurita et al. 2010 Japan 148 66 60/88 T1-4 margin cutoff; 5 mm margin cutoff; Multivariable analysis RFS
Ebrahimi et al. 2011 Australia 498 61.5(22.5–91.5) 311/187 T1-T4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS
Jan et al. 2011 China 415 51 ± 11.4(25–84) 406/9 T1-T4 margin cutoff; 5 mm margin cutoff; Kaplan–Meier OS
Iseli et al. 2012 Australia 78 21–90 46/32 T1-2 margin cutoff; 1 mm margin cutoff; Kaplan–Meier RFS
Wong et al. 2012 England 192 61(55–64) 122/70 T1-4 margin cutoff; 1 mm margin cutoff; Multivariable analysis RFS
Priya et al. 2012 India 306 50(19–80) 237/69 T1-4 margin cutoff; 5 mm margin cutoff; Kaplan–Meier DFS
Chen et al. 2012 China 407 52(22–90) 347/60 T1-4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS, DFS
Varvares et al. 2015 America 108 59.26 ± 12.09 61/47 T1-4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS, DFS, RFS
Luryi et al. 2015 America 6830 61.7 4201/2629 T1-T4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS
Dillon et al. 2015 America 54 60.5(19–85) 40/14 T1-4 margin cutoff; 5 mm margin cutoff; Kaplan–Meier OS, DFS
Quinlan-Davidson et al. 2017 America 289 58.9(20–88) 189/100 T1-T4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS
Jang et al. 2017 South Korea 325 — 190/135 T1-4 margin cutoff; 5 mm margin cutoff; Multivariable analysis RFS
Fridman et al. 2018 Israel 1257 56 931/323 T1-4 margin cutoff; 5 mm margin cutoff; Kaplan–Meier OS, DFS, RFS
Cariati et al. 2019 Spain 200 19–91 136/64 T(p)1–4 margin cutoff;5 mm margin cutoff; Kaplan–Meier OS
Mishra et al. 2019 India 563 — 442/121 T1-4 Margin cutoff;5.5 mm margin cutoff; Univariate analysis RFS
Ding et al. 2019 America 149 59(28–88) 105/44 T1-T4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS, DFS
Chen et al. 2019 China 1642 55 ± 11 1409/233 T1-4 margin cutoff; 5 mm margin cutoff; Kaplan–Meier OS, DFS
Bajwa et al. 2020 England 669 60.9 ± 11.8 — T(p)1–4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS, DFS, RFS
Brinkman et al. 2020 Ireland 254 27–93 164/80 T1-4 margin cutoff; 3 mm; Multivariate analyses+Univariate analysis OS, RFS
Lien et al. 2020 China 385 53.2(28.5–88.7) 353/32 T1-T4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS
Lin et al. 2021 China 15,654 54(16–100) 14,151/1503 AJCC margin cutoff;5 mm margin cutoff; Multivariable analysis OS
Lau et al. 2021 Singapore 88 53(22–89) 47/41 T1-T4 margin cutoff; 5 mm

RFS - Univariate analysis

OS - Multivariate analyses

 OS, RFS
Sambasivan et al. 2021 England 172 62(25–90) 108/64 T1-T4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS
Choi et al. 2022 South Korea 407 — 261/146 T1-4 margin cutoff; 5 mm margin cutoff; Multivariable analysis OS, DFS
Kohler et al. 2022 Brazil 772 59.2 ± 13.5 516/256 T(p)1-4a

1)margin cutoff; continious ;

2)margin cutoff; 1.7 mm;

1)continious; Multivariable analysis

2))margin cutoff; Univariate analysis

 RFS
Cho et al. 2022 South Korea 45 60.2 19/26 T1-4 margin cutoff; 3.5 mm margin cutoff; Multivariable analysis RFS
Fowler et al. 2022 England 412 63.7 ± 12.3 280/132 T1-4 margin cutoff; 5 mm margin cutoff; Kaplan–Meier OS, RFS
Pei et al. 2022 South Korea 235 60 (23–91) 131/104 T1-4 margin cutoff; 5 mm margin cutoff; Kaplan–Meier OS, RFS
Bera et al. 2023 India 260 61.63 ± 7.34 189/71 T(c)3-4b margin cutoff; 5 mm margin cutoff; Univariate analysis OS, DFS
Sultania et al. 2023 India 123 52 ± 12.8 100/23 T1-4

1)margin cutoff; 5 mm

2)MDR

1)margin cutoff; Univariate analysis

2)MDR; Multivariate analyses

 OS, DFS
Aziz et al. 2024 Pakistan 158 18–75 96/62 T1-2 margin cutoff; 1.0 mm margin cutoff; Kaplan–Meier OS, RFS
Dudkiewicz et al. 2024 Israel 266 17–94 128/138 T1-4

1)margin cutoff; continious ;

2)margin cutoff; 5 mm;

 Multivariate analyses OS, RFS
Hung et al. 2024 China 911 55.5 767/144 T1-2 MDR; 0.6 MDR; Kaplan–Meier+Multivariate analyses OS, DFS
Kamalakkannan et al. 2024 India 167 50–66 100 /67 T(p)1–4 margin cutoff; 5 mm margin cutoff; Univariate analysis RFS
Klibngern et al. 2024 China 18,324 55.44 ± 10.53 16,678/1646 T(p)1–4

1)margin cutoff; 5 mm

2)MDR; 0.5

1)margin cutoff; Kaplan–Meier

2)MDR; 0.5

 OS
Hung et al. 2025 China 7420 57.4 ± 10.8 6739/681 T(p)1–4

1)margin cutoff; 5 mm

2)MDR;0.5

margin cutoff; Multivariable analysis

MDR; Multivariable analysis

 OS, DFS
Tsai et al. 2025 China 422 31–74 394/28 T(p)3–4

1)margin cutoff; 5 mm

2)MDR;0.5

margin cutoff; Multivariable analysis

MDR; Multivariable analysis

 OS, DFS
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Quality assessment

Regarding the selection of study subjects, the participants of the included studies were highly representative, primarily consisting of patients with OSCC undergoing surgical treatment. The source of the study subjects was clearly reported, and the inclusion and exclusion criteria were clearly defined, with no significant selection bias. Regarding the control of confounding factors, most studies used SM status or depth of invasion as the primary study variable and performed multivariate regression analysis to control for confounding factors such as age, sex, and tumor stage, resulting in a low risk of bias. Only a few studies used univariate analysis without adequately controlling for confounding factors, indicating a moderate risk of bias. Regarding outcome measurement, studies generally used reliable clinical records, pathology reports, or follow-up data to determine prognostic outcomes. Follow-up time was sufficient and follow-up integrity was good (follow-up rate > 90%), suggesting a low risk of bias in outcome measurement. Furthermore, all included studies performed well in the areas of loss to follow-up, measurement of prognostic factors, and statistical analysis and reporting, indicating a low risk of bias. The overall assessment results show that all included studies had a low to moderate risk of bias, indicating high quality of the included studies (Table S3).

Meta-analysis results

SM

OS

A total of 28 studies involving 57,474 individuals analyzed OS. Given that significant heterogeneity (I² = 93.7%, p < 0.001) was observed, a random-effects model was employed. As illustrated in Fig. 2, the pooled results indicated a significant association between poor SM and worse OS (HR = 1.56, 95% CI 1.37–1.77, p < 0.001).

Fig. 2.

Fig. 2

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Association between surgical margins and overall survival (OS)

DFS

A total of 14 studies involving 12,401 individuals reported DFS. Given that substantial heterogeneity (I² = 90.1%, p < 0.001) was found, a random-effects model was used. As shown in Fig. 3, the pooled results demonstrated a significant association between poor SM and unfavorable DFS (HR = 1.72, 95% CI 1.29–2.28, p < 0.001).

Fig. 3.

Fig. 3

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Association between surgical margins and disease-free survival (DFS)

RFS

A total of 21 studies involving 7,029 individuals analyzed RFS. As substantial heterogeneity (I² = 88.8%, p < 0.001) was found, a random-effects model was employed. As illustrated in Fig. 4, the pooled results demonstrated a significant association between poor SM and unfavorable RFS (HR = 1.99, 95% CI 1.53–2.58, p < 0.001).

Fig. 4.

Fig. 4

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Association between surgical margins and recurrence-free survival (RFS)

MDR

OS

A total of 4 studies involving 8,195 patients reported this outcome. Due to heterogeneity analysis (I² = 0.9%, p = 0.387), a fixed-effects model was used. Pooled results showed that lower MDR was significantly associated with worse OS (HR = 1.36, 95% CI 1.21–1.54, p < 0.001) (Fig. 5).

Fig. 5.

Fig. 5

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Association between margin-to-depth of invasion ratio (MDR) and overall survival (OS)

DFS

Three studies involving 7,773 patients reported DFS. Given that significant heterogeneity (I² = 52.6%, p = 0.121) was observed, a random-effects model was applied. As illustrated in Fig. 6, the pooled results demonstrated a significant association between low MDR and worse DFS (HR = 1.40, 95% CI 1.12–1.74, p = 0.003) (Fig. 6).

Fig. 6.

Fig. 6

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Association between margin-to-depth of invasion ratio (MDR) and disease-free survival (DFS)

Subgroup analyses

SM

Due to significant heterogeneity, subgroup analyses were performed based on data source, region, and different cutoff values. Subgroup analysis of SM for OS showed that the performance of SM in predicting OS was not affected by region or cutoff values. However, subgroup analysis based on data source revealed that in multivariate analysis, low SM was significantly associated with worse OS (HR = 1.36, 95% CI 1.19–1.55, p < 0.001). In Kaplan-Meier analysis, low SM was also significantly associated with worse OS (HR = 1.80, 95% CI 1.45–2.25, p < 0.001). In univariate analysis, SM had no prognostic effect (Table 2).

Table 2.

Subgroup analysis results

Outcomes Number of studies Heterogeneity test I²,P value HR,95%CI P Egger, P
MARGIN: OS
overall 28 93.7%,P < 0.001 1.56(1.37,1.77) < 0.001 0.053
Area
Asia 16 96.4%,P < 0.001 1.61(1.37,1.90) < 0.001
North America 6 7%,P = 0.372 1.39(1.17,1.64) < 0.001
Europe 5 53.9%,P = 0.070 1.41(1.05,1.88) < 0.023
Oceania 1 0%,P < 0.001 1.48(1.07,2.05) 0.018
Analysis source
Multivariable analysis 16 89.8%,P < 0.001 1.36(1.19,1.55) < 0.001
Kaplan–Meier 9 80.6%,P < 0.001 1.80(1.45,2.25) < 0.001
Univariate analysis 3 90.6%,P = 0.001 1.80(0.41,7.98) 0.439
CUTOFF
5 mm 26 83%,P < 0.001 1.59(1.42,1.78) < 0.001
7 mm 1 0,P < 0.001 1.04(1.01,1.07) 0.019
3 mm 1 0,P < 0.001 1.78(1.18,2.69) 0.006
MARGIN: DFS
overall 14 90.1%,P < 0.001 1.72(1.29,2.28) < 0.001 0.719
Area
Asia 10 92.8%,P < 0.001 1.68(1.21,2.34) 0.002
North America 3 59.7%,P = 0.084 2.24(0.71,7.12) 0.17
Europe 1 0,P < 0.001 1.46(1.06,2.01) 0.02
Analysis source
Multivariable analysis 7 76.8%,P < 0.001 1.55(1.11,2.16) 0.001
Kaplan–Meier 5 92.5%,P < 0.001 1.70(0.97,2.97) 0.065
Univariate analysis 2 93.1%,P < 0.001 1.81(0.41,8.04) 0.433
CUTOFF
5 mm 14 90.1%,P < 0.001 1.72(1.29,2.28) < 0.001
MARGIN: RFS
overall 21 88.8%,P < 0.001 1.99(1.53,2.58) < 0.001 0.002
Area
Asia 14 90.1%,P < 0.001 1.93(1.33,2.79) 0.001
North America 2 79.2%,P = 0.029 5.79(0.5,67.06) 0.16
Europe 3 66.4%,P = 0.051 1.51(0.85,2.66) 0.158
Oceania 1 0,P < 0.001 5.35(2.28,12.55) < 0.001
South America 1 0,P < 0.001 1.68(1.38,2.05) < 0.001
Analysis source
Multivariable analysis 9 87.1%,P < 0.001 1.91(1.22,3.00) 0.005
Kaplan–Meier 8 84.2%,P < 0.001 2.05(1.25,3.36) 0.005
Univariate analysis 4 39%,P = 0.178 1.99(1.50,2.64) < 0.001
CUTOFF
7 mm 1 0,P < 0.001 1.11(1.05,1.16) < 0.001
5 mm 14 82.3%,P < 0.001 2.11(1.48,3.01) < 0.001
1 mm 3 39.1%,P = 0.193 3.01(1.55,5.85) 0.001
5.5 mm 1 0,P < 0.001 2.23(1.41,3.52) 0.001
1.7 mm 1 0,P < 0.001 1.68(1.38,2.05) <0.001
3.5 mm 1 0,P < 0.001 0.81(0.33,1.97) 0.643
MDR: OS
overall 4 0.9%,P = 0.387 1.36(1.21,1.54) < 0.001
Area
Asia 4 0.9%,P = 0.387 1.36(1.21,1.54) < 0.001
Analysis source
Multivariable analysis 3 0,P = 0.458 1.39(1.23,1.57) < 0.001
Kaplan–Meier 1 0,P < 0.001 0.95(0.52,1.73) 0.867
MDR
0.5 3 0,P = 0.378 1.33(1.17,1.51) < 0.001
0.35 1 0,P < 0.001 1.61(1.15,2.26) 0.005
MDR: DFS
overall 3 52.6%,P = 0.121 1.40(1.12,1.74) 0.003
Area
Asia 3 52.6%,P = 0.121 1.40(1.12,1.74) 0.142
Analysis source
Multivariable analysis 3 52.6%,P = 0.121 1.40(1.12,1.74) 0.142
MDR
0.5 3 52.6%,P = 0.121 1.40(1.12,1.74) 0.142
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Subgroup analysis of SM for DFS showed that SM was not associated with prognosis in subgroups of North America, univariate analysis, and Kaplan-Meier analysis. Other subgroup analyses showed that low SM was significantly associated with worse DFS. Regarding RFS, the results indicated that SM was not significantly linked to prognosis in studies conducted in North America and Europe or those using a cutoff value of 3.5 mm. In contrast, poor SM was significantly associated with worse DFS in the other subgroups (Table 2).

MDR

To ensure robustness of the results, we conducted subgroup analyses based on data source, region, and different cutoff values. The subgroup analysis of MDR for OS showed that when the data source was a survival curve, MDR was not significantly associated with prognosis. Other subgroup analyses showed that low MDR was significantly associated with worse OS. The subgroup analysis of MDR for DFS showed that low MDR was significantly associated with worse DFS (Table 2).

Sensitivity analysis and publication bias

In this study, sensitivity analysis was performed for all outcome indicators of SM and MDR using the leave-one-out method to assess the stability of the results. After excluding the study by Klibngern et al. (2024) [47], a new pooled analysis was performed, and the results showed that the pooled effect size did not change significantly and the effect direction remained consistent, indicating that the results of this study were relatively robust (Figures S1-S5).

Egger’s test was performed on SM for OS, DFS, and RFS. The results showed that there was publication bias for RFS(p = 0.002), but no publication bias was identified for OS (p = 0.053) and DFS (p = 0.719). No new articles were added after the trim and fill method, indicating that the existing meta-analysis results were relatively robust and unlikely to be significantly affected by publication bias (Figures S6).

Discussion

This meta-analysis synthesized the most recent evidence from 41 eligible cohort studies. The findings suggested that poor SM was significantly associated with unfavorable OS, DFS, and RFS, while low MDR was significantly associated with poor OS and DFS. Since only one article on MDR for RFS was included, and the data were limited, no meta-analysis for this outcome was performed. Subgroup analyses indicated that these conclusions may be influenced by cutoff values, geographical regions, and data sources.

Our results align with earlier studies, confirming that SM is a critical prognostic indicator affecting local control after surgery and long-term survival in OSCC patients. SM holds clinical significance in three key areas. First, in terms of tumor residue and recurrence risk, a positive margin or a margin too close to the tumor (< 5 mm) may result in residual tumor tissue, markedly elevating the risk of local recurrence. Multiple studies demonstrated that SM may be an independent prognostic indicator for OSCC recurrence, especially when tumor cell infiltration is present or when the SM is too close to the tumor edge. In such cases, postoperative recurrence rates can increase by 2–3 times [52, 53]. Second, molecular markers provide novel approaches for assessing SM. Key regulatory factors such as Snail and Axin2 typically exhibit abnormally high expression in SM tissues and are significantly linked to elevated tumor invasiveness and recurrence risk. This finding suggests that molecular analysis of SM could enhance the accuracy of prognostic assessments [48]. Furthermore, the SM width should be evaluated alongside the biological features of the tumor. Clinical recommendations demonstrate that a safe SM of ≥ 5 mm can effectively eliminate microinvasive lesions, improving the 5-year SR by 15%-20%. Nevertheless, it is worth noting that when the tumor exhibits high invasiveness, such as nerve or vascular infiltration, close follow-up is required even if the conventional margin standard is met [54].

When exploring the relationship between SM and prognosis, this study found that some results differed from traditional conclusions. For example, Dudkiewicz et al. (2024) [45] employed Cox regression analysis to assess the prognostic value of traditional survival classifications, but found no statistically significant result (p = 0.54). This result may be affected by information loss and a lack of finer differentiation, as traditional classification methods are overly simplistic and rely on categorical variables. The true relationship between SM width and survival may be continuous rather than exhibiting a threshold effect. Therefore, traditional classifications cannot capture the true impact of SM width on SRs. A study by Choi et al. (2022) [37] demonstrated that SM had an HR of 0.83 (95% CI 0.54–1.27, p = 0.39) in multivariate analysis, whereas the HR for SM was 1.56 (95% CI 1.13–2.14, p = 0.005) in univariate analysis. This inconsistency may be related to the varying degree of adjustment for confounding factors in different studies. For instance, studies may have controlled for factors including TNM stage, smoking history, age, and alcohol consumption to varying degrees. In addition, some studies only made basic adjustments, thus resulting in an underestimation of the HR. A study by Lien et al. (2020) [33] demonstrated that the HR for SM in multivariate analysis was 1.19 (95% CI 0.77–1.83), with weak statistical significance. This may be attributed to the characteristics of the patient population in their study, which had higher TNM staging and poorer overall health. The independent impact of SM on prognosis may have been overshadowed by other stronger prognostic factors (such as tumor stage and general health), thus leading to a lack of statistical significance in multivariate analysis. The study by Ding et al. (2019) [31] was limited by a small sample size (n = 149). Although a HR of 1.7 (95% CI 0.96–3.01) was observed for SM status, the wide CI suggested insufficient statistical power. According to statistical principles, at least 200 samples are required to achieve 80% test power (α = 0.05). Moreover, the limited sample size may elevate the risk of Type II errors, thereby affecting the reliability of the results.

Subgroup analysis showed that positive SM had a generally stable adverse effect on OS, DFS, and RFS in patients with OSCC, but differences existed in some subgroups. The results of North America, univariate analysis, and some cutoff value subgroups were not statistically significant, suggesting that geographic location, confounding factors, and cutoff values may affect the results. MDR had a stable association with OS, but its association with DFS was no longer significant in subgroups. In addition to tumor and surgical factors, postoperative oral care and infection control may also indirectly affect the prognostic effect of SM by influencing wound healing and local recurrence. These inconsistencies suggest that the prognostic role of SM and MDR is moderated by multiple clinical and methodological factors. The divergence in conclusions under different cutoff values may stem from differences in tumor aggressiveness, which could influence the prognostic effect of SM thresholds. For highly aggressive tumors, recurrence may still occur via microscopic infiltration or distant metastasis despite negative margins, thereby diminishing the protective role of wider SM [55]. Variations in surgical quality and margin assessment approaches may also lead to inconsistency between the actual resection extent and pathological reports, particularly when intraoperative frozen sections and postoperative pathology differ in evaluation standards. As a result, the association between margin cutoff and prognosis could be affected [56]. Additionally, primary tumor location, including the hard palate, floor of the mouth, or tongue border, which are anatomically complex, may result in differences in local control and survival even under the same SM width [57]. Inconsistent findings based on the same cutoff value could also be due to variations in the immune microenvironment. In patients with impaired immune function, recurrence may still occur despite meeting margin adequacy [58]. Furthermore, inconsistencies in margin measurement, including whether tissue shrinkage was corrected for, could result in recorded margin widths that do not accurately reflect the actual resection range, thus introducing bias in prognostic comparisons [59]. Heterogeneity in tumor molecular characteristics, such as HPV status or p16 expression, may also lead to distinct biological behavior and survival outcomes even under identical pathological SM widths [60].

SM width remains a key indicator for assessing OSCC prognosis, and its limitations can be mitigated by combining it with MDR. By balancing tumor aggressiveness and the extent of surgical resection, MDR offers a more refined tool for risk stratification. Future efforts should focus on standardizing the technical implementation and exploring the molecular mechanisms underlying the complementary use of both metrics, ultimately advancing individualized precision therapy. However, this study still has the following unavoidable limitations. First, as a retrospective meta-analysis, this study may be subject to selection and information bias due to differences in the quality and design of the original studies. Second, we only included English-language studies and did not search for grey-literature, which could introduce language bias and potential publication bias. Third, although literature with significant database overlap was removed, some studies originated from large global multicenter cancer registries, and potential patient overlap that cannot be completely ruled out may still exist, potentially affecting the robustness of the results. Finally, due to the limited number of included studies on MDR, it is infeasible to systematically assess its prognostic value.

Conclusion

This meta-analysis suggests that SM and MDR may serve as potential reference indicators for assessing the prognosis of patients with OSCC. These associations may be influenced by the cutoff thresholds. Further prospective studies of high quality are required to validate the value of SM and MDR for predicting OSCC under varying cutoff values, as well as to refine optimal SM thresholds, thereby providing insights for enhancing evidence-based clinical decision-making.

Supplementary Information

.

Supplementary Material 1. (29KB, docx)
12903_2026_9103_MOESM2_ESM.docx (439.2KB, docx)

Supplementary Material 2. Table S1: Detailed Search Strategy. Table S2. Reasons for exclusion of full-text articles. Table S3: Risk of Bias Assessment of Included Studies Using the QUIPS Tool. Figure S1: Sensitivity Analysis of Surgical Margins and OS. Figure S2: Sensitivity Analysis of Surgical Margins and DFS. Figure S3: Sensitivity Analysis of Surgical Margins and RFS. Figure S4: Sensitivity Analysis of MDR and OS. Figure S5: Sensitivity Analysis of MDR and DFS. Figure S6: Trim-and-fill Analysis for Evaluating the Impact of Publication Bias on the Meta-analysis of RFS.

Acknowledgements

Not applicable.

Clinical trial number

Not applicable.

Abbreviations

DFS

disease-free survival

MDR

margin to depth of invasion ratio

OS

overall survival

OSCC

Oral squamous cell carcinoma

RFS

recurrence-free survival

SM

Surgical margin

Authors' contributions

All authors contributed to the study conception and design. Writing – original draft preparation: Jin Xu, Yinqi Chen, Qian Cui; Writing – review and editing: Wenyuan Yu, Yong Xu, Yu Shao, Zhonghao Liu, Zhenggang Chen; Conceptualization: Jin Xu, Yinqi Chen, Qian Cui, Zhonghao Liu, Zhenggang Chen; Methodology: Jin Xu, Yinqi Chen, Qian Cui, Wenyuan Yu; Formal analysis and investigation: Jin Xu, Yinqi Chen, Qian Cui, Wenyuan Yu; Funding acquisition: Zhenggang Chen; Resources: Yong Xu, Yu Shao, Zhonghao Liu; Supervision: Zhonghao Liu, Zhenggang Chen. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by Shandong Medical & Health Science & Technological Project (Health Commission of Shandong Province 202308020420).

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

As this is a meta-analysis, ethical approval is not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jin Xu, Qian Cui and Yinqi Chen contributed equally to this work and should be considered as co-first authors.

Contributor Information

Zhonghao Liu, Email: lzhao_bzmc@163.com.

Zhenggang Chen, Email: chenzhg1973@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (29KB, docx)
12903_2026_9103_MOESM2_ESM.docx (439.2KB, docx)

Supplementary Material 2. Table S1: Detailed Search Strategy. Table S2. Reasons for exclusion of full-text articles. Table S3: Risk of Bias Assessment of Included Studies Using the QUIPS Tool. Figure S1: Sensitivity Analysis of Surgical Margins and OS. Figure S2: Sensitivity Analysis of Surgical Margins and DFS. Figure S3: Sensitivity Analysis of Surgical Margins and RFS. Figure S4: Sensitivity Analysis of MDR and OS. Figure S5: Sensitivity Analysis of MDR and DFS. Figure S6: Trim-and-fill Analysis for Evaluating the Impact of Publication Bias on the Meta-analysis of RFS.

Data Availability Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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