Distribution and Associated Factors of Canine Mammary Tumors within the Canine Tumor Spectrum in Mainland China

Distribution and Associated Factors of Canine Mammary Tumors within the Canine Tumor Spectrum in Mainland China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distribution and Associated Factors of Canine Mammary Tumors within the Canine Tumor Spectrum in Mainland China Xinyuan Liu, Bohan Xu, Huiling Zhang, Hongyu Zhou, Bing Li, Tangjie Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9105294/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Canine mammary tumors (CMTs) are among the most frequently diagnosed neoplasms in female dogs and represent an important health concern in companion animal medicine. However, their overall contribution to the canine tumor spectrum in mainland China has not been systematically quantified. Results A systematic search of CNKI, Wanfang, VIP, PubMed, and Web of Science Core Collection was conducted from database inception to March 2025 to identify studies reporting the number of CMTs and total canine tumors in mainland China. Pooled proportions were estimated using a random-effects model with Freeman–Tukey double arcsine transformation. Thirty-one studies involving 5,947 canine tumor cases were included. The pooled proportion of CMTs among all canine tumors was 34.0% (95% CI: 29.6%–45.8%), with substantial heterogeneity (I² = 91.0%). Higher proportions were observed in female dogs, intact dogs, small-sized breeds, and dogs aged ≥ 6 years. Regional variation was evident, with the highest proportion reported in Northeastern China, and temporal differences were also observed across study periods. No significant publication bias was detected, and sensitivity analysis confirmed the robustness of the results. Conclusions CMTs represent a substantial component of the canine tumor spectrum in mainland China. The observed demographic patterns support the relevance of spontaneous CMTs as a comparative model for human breast cancer research. Canine mammary tumor Tumor proportion Stratified analysis Epidemiology Mainland China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Dogs are among the most important companion animals worldwide, and their health status directly affects both animal welfare and the physical and psychological well-being of their owners. With the rapid expansion of the pet industry and the increasing urban dog population in China, neoplastic diseases have become an increasingly significant clinical concern. Among these, CMTs are recognized as one of the most frequently diagnosed neoplasms in female dogs, accounting for a substantial proportion of all canine tumors [ 1 , 2 ]. Globally, mammary tumors represent approximately 40–50% of all tumors in intact female dogs, of which nearly half are malignant, resulting in considerable heterogeneity in biological behavior and prognosis [ 1 , 3 ]. Tumor development is closely associated with cumulative exposure to ovarian hormones. Classical epidemiological studies have demonstrated that ovariectomy performed before the first estrus reduces the risk of mammary tumor development by more than 99%, and this protective effect progressively diminishes with successive estrous cycles [ 4 ]. Breed predisposition and age are also well-established risk factors. Small and toy breeds exhibit increased susceptibility, and the peak incidence typically occurs in middle-aged to older dogs [ 2 , 5 ]. In China, several hospital-based investigations have reported that mammary tumors constitute a predominant proportion of surgically excised canine neoplasms, with more than half of the cases confirmed as malignant [ 6 – 8 ]. Regional studies conducted in major metropolitan areas, including Wuhan, further indicate that the majority of affected dogs are intact females and that most cases occur in animals older than 8 years [ 7 ]. However, these findings are largely derived from single institutions or geographically restricted populations. In the absence of a nationwide veterinary tumor registry, the overall distribution pattern of CMTs in mainland China remains poorly defined. Marked regional differences exist among central-eastern, northeastern, southwestern, and northwestern China region with respect to economic development, implementation of neutering practices, and breeding patterns. Such disparities may contribute to substantial heterogeneity in reported proportions, yet no quantitative synthesis has been performed to date. Given the large and geographically dispersed canine population in China, conducting population-based epidemiological surveys to estimate incidence is logistically challenging. Accordingly, calculating the proportion of mammary tumors among clinically diagnosed neoplasms in companion animal practice represents a more feasible surrogate measure of disease burden. Nevertheless, existing epidemiological data from different regions have not been systematically integrated, and a pooled national estimate is lacking. Potential influencing factors have also not been comprehensively evaluated [ 6 ]. In this study, a systematic search of the literature on CMTs in mainland China was conducted, and quantitative data from independent studies were synthesized using a random-effects model to estimate the pooled proportion of mammary tumors among all canine neoplasms. Stratified analyses were performed according to geographic region, sampling year, age, breed, sex, and neutering status to explore potential sources of heterogeneity. By providing a quantitative description of the national distribution pattern and associated factors, this study seeks to clarify the characteristics of disease burden in companion animal oncology in China and to provide an evidence base for preventive strategies and the rational allocation of clinical resources. In parallel with advances in comparative oncology, canine mammary carcinoma has been increasingly recognized as a valuable spontaneous animal model for human breast cancer. Owing to its natural occurrence and its substantial similarities to human breast cancer in terms of molecular subtypes, hormone receptor expression, and patterns of tumor progression, it offers important translational relevance [ 9 , 10 ]. A systematic synthesis of epidemiological evidence on CMTs in mainland China will therefore not only contribute to improved management of companion animal diseases, but also provide population-level data to support cross-species translational research in breast cancer, thereby expanding its application in comparative medicine. Results Study Selection According to the predefined search strategy, a total of 630 records were retrieved from five databases (search period: January 1, 2000 to March 31, 2025). After removal of duplicates and screening of titles and abstracts, 567 records were excluded. Following full-text assessment, 33 irrelevant studies, 32 studies with non-extractable data, and 1 study with incomplete reporting were excluded. Ultimately, 31 studies were included in the quantitative synthesis. The study selection process is presented in Figure. 1. Although the search period covered 2000 to 2025, the actual data collection of the included studies was primarily conducted between 2010 and 2025, and the articles were published between 2015 and 2025 [ 6 ][ 11 – 40 ]. A total of 5,947 CMTs were included, with individual study sample sizes ranging from 15 to 1,079. All included studies were based on histopathological examination or clearly defined clinical diagnostic criteria. The main characteristics of the included studies are summarized in Table 1 . Table 1 Baseline characteristics of the included studies Authors Sampling Year Sampling Region No. of CMTs / Total Tumors Proportion (%) Bai et al., 2019 [ 11 ] 2019–2020 Weifang(Central-Eastern China) 23/70 32.80% Chang et al., 2018 [ 12 ] 2016–2017 Harbin(Northeastern China) 29/71 40.80% Chang, 2019 [ 13 ] 2014–2017 Harbin(Northeastern China) 138/271 50.90% Chen, 2017 [ 14 ] 2009–2014 Kunming(Southern China) 96/378 5.40% Chen, 2019 [ 15 ] 2017–2018 Henan(Eastern China) 17/105 16.19% He, 2022 [ 16 ] 2019 Beijing(Northern China) 13/115 11.30% Huang, 2014 [ 17 ] 2010–2012 Shenzhen(Southern China) 9/38 23.68% Huang, 2023 [ 18 ] 2013–2019 Guangdong(Southern China) 187/439 42.60% Li et al., 2021 [ 19 ] 2018–2019 Yunnan(Southern China) 8/15 53.30% Liu et al., 2013 [ 20 ] 2009–2011 Xi’an(Northwestern China) 10/57 17.54% Liu et al., 2013 [ 21 ] 2010–2011 Beijing(Northern China) 74/156 47.44% Lu et al., 2016 [ 22 ] 2014–2015 Tianjin(Northern China) 9/29 31.03% Ma, 2024 [ 23 ] 2015–2021 Xi’an(Northwestern China) 90/295 30.51% Ni, 2024 [ 24 ] 2018–2022 Shandong(Eastern China) 205/632 32.43% Qu et al., 2019 [ 25 ] 2017–2018 Jilin(Northeastern China) 12/32 37.50% Shao et al., 2019 [ 26 ] 2018 Xinjiang(Northwestern China) 4/15 26.70% Shao, 2022 [ 27 ] 2017–2019 Xinjiang(Northwestern China) 11/36 28.21% Shen et al., 2017[ 28 ] 2015 Nanjing(Eastern China) 11/35 31.43% Shen, 2019 [ 29 ] 2015–2016 Nanjing(Eastern China) 30/66 45.50% Shi, 2020 [ 30 ] 2017–2019 Beijing(Northern China) 113/247 45.70% Wang et al., 2017 [ 31 ] 2015–2016 Beijing(Northern China) 4/44 9.00% Wang, 2024 [ 32 ] 2021–2022 Beijing(Northern China) 24/206 11.65% Xian, 2021 [ 33 ] 2018–2020 Shenyang(Northeastern China) 94/196 47.96% Yan, 2014 [ 34 ] 2012 Hefei(Eastern China) 45/104 43.00% Yang, 2025 [ 35 ] 2021–2023 Changchun(Northeastern China) 186/448 41.80% Ye, 2024 [ 36 ] 2022–2023 Ürümqi(Northwestern China) 29/66 43.93% Yu, 2013 [ 37 ] 2010–2013 Changsha(Eastern China) 11/46 23.91% Zhang et al., 2021 [ 38 ] 2018–2020 Jilin(Northeastern China) 50/98 51.02% Zheng et al., 2022 [ 6 ] 2017–2021 Mainland China 504/1079 46.71% Zhu et al., 2015 [ 39 ] 2012–2014 Hangzhou(Eastern China) 56/133 42.11% Zou, 2024 [ 40 ] 2019–2022 Wuhan(Eastern China) 140/425 32.94% * Data missing for Tibet due to lack of available literature Quality Assessment of Included Studies Diagnostic criteria, sample size, and data completeness were carefully reviewed to ensure accurate extraction of the number of mammary tumors and total tumor cases. Overall, reporting quality was acceptable, and no apparent source of systematic bias was identified. Data Synthesis and Heterogeneity Analysis A pooled analysis of studies conducted between 2010 and 2025 was performed (Fig. 2 ). The reported proportion of CMTs among all canine neoplasms ranged from 5.4% to 53.3%. Significant heterogeneity was observed across studies (χ² = 333.58, P < 0.0001; I² = 91.0%). Therefore, a random-effects model was applied, yielding a pooled proportion of 34.0% (95% CI: 29.6%–45.8%) for mammary tumors among all canine tumors in mainland China. Stratified Analysis Given the substantial heterogeneity identified in the overall analysis (I² > 50%), stratified analyses were conducted to explore potential sources of variation. Subgroup analyses were performed according to geographic region, age distribution, sex, body size, study period, and neutering status. As heterogeneity remained considerable within subgroups, random-effects models were consistently applied to estimate pooled proportions for each stratum. The results are presented in Table 2 . Table 2 Stratified analysis of pooled CMT proportions Variable No. Studies Total Tumor Cases CMT Cases Pooled Proportion Heterogeneity P -value Estimates 95%CI Q(x²) PQ I²(%) Age(years) 9 < 6 234 76 32.48% 0.15,0.45 36.78 < 0.000 83.69% 0.01 6–13 544 238 44.00% 0.25,0.52 82.86 13 504 214 42.46% 0.29,0.49 31.21 < 0.000 77.57% Sex 20 Female 1974 1155 58.51% 0.48,0.63 221.53 < 0.000 91.42% 0.000 Male 1279 33 0.26% 0.01,0.05 16.94 0.02 58.68% Neutering Status 12 Neutered 653 253 38.74% 0.31,0.49 46.13 < 0.000 76.15% 0.000 Intact 1315 615 46.77% 0.37,0.49 39.15 < 0.000 74.69% Body Size Small 11 1062 494 46.52% 0.33,0.53 95.52 < 0.000 89.53% 0.000 Medium 10 565 159 28.14% 0.20,0.41 51.98 < 0.000 86.53% Large 11 161 50 31.06%% 0.18,0.56 59.03 < 0.000 86.45% Region Northeastern China 6 1116 509 45.61% 0.43,0.49 9.30 0.16 35.45% 0.000 Northern China 6 797 237 29.74% 0.15,0.38 153.97 < 0.000 96.10% Central-Eastern China 9 1621 538 33.19% 0.29,0.37 37.22 < 0.000 75.82% Southern China 4 870 300 34.48% 0.26,0.42 32.60 < 0.000 87.73% Northwestern China 5 469 144 30.70% 0.25,0.35 11.54 0.04 56.66% Study Period 2010–2014 7 563 214 38.01% 0.24,0.43 33.09 < 0.000 81.87% 0.000 2015–2020 15 2224 923 41.50% 0.26,0.43 222.64 < 0.000 93.71% 2021–2025 4 1145 379 33.10% 0.17,0.47 98.12 < 0.000 96.94% In the body size stratification, significant differences were observed among size categories ( P < 0.001). Based on domestic classification standards, dogs were categorized as small, medium, or large. The highest proportion of mammary tumors was observed in small-sized dogs (46.52%, 95% CI: 33%–53%), followed by large-sized dogs (31.06%, 95% CI: 18%–56%), while medium-sized dogs showed a comparatively lower proportion (28.14%, 95% CI: 20%–41%). Age-stratified analysis also revealed significant differences among age groups (P = 0.01). The proportion of mammary tumors was 32.48% (95% CI: 15%–45%) in dogs younger than 6 years, 44.00% (95% CI: 25%–52%) in dogs aged 6–13years, and 42.46% (95% CI: 29%–49%) in dogs older than 13 years. These findings indicate that mammary tumors predominantly occur in middle-aged and older dogs aged 6 years and above. Sex-stratified analysis demonstrated a marked difference in proportions (P < 0.001). Mammary tumors occurred predominantly in female dogs, accounting for 58.51% (95% CI: 48%–63%) of tumors among affected females. Neutering status was also significantly associated with tumor proportion. Intact dogs exhibited a higher proportion of mammary tumors (46.77%, 95% CI: 37%–49%). Regional stratification revealed significant variation across geographic areas (P < 0.001). The highest proportion was observed in Northeastern China region(45.61%, 95% CI: 43%–49%). Relatively lower proportions were found in Northern China region(29.74%, 95% CI: 15%–38%) and Northwestern China region (30.70%, 95% CI: 25%–35%). Central-Eastern and Southern China region showed intermediate levels, at 33.19% (95% CI: 29%–37%) and 34.48% (95% CI: 26%–42%), respectively (Fig. 3 ). No data were available from the Tibet region. Stratification by study period also indicated significant differences (P < 0.001). The highest pooled proportion was observed during 2015–2020 (41.50%, 95% CI: 26%–43%), followed by 2010–2014 (38.01%, 95% CI: 24%–43%), whereas a slight decrease was noted in 2021–2025 (33.10%, 95% CI: 17%–47%). Publication Bias and Sensitivity Analysis Egger’s regression test was applied to assess potential publication bias in the reported proportions of CMTs. The regression intercept test yielded a P value > 0.05, indicating no statistically significant evidence of publication bias (Fig. 4 ). Sensitivity analysis was subsequently conducted using a leave-one-out approach. Sequential exclusion of individual studies did not materially alter the pooled estimate. All recalculated effect sizes remained within the 95% confidence interval of the overall pooled proportion, supporting the robustness and stability of the findings (Fig. 5 ). Discussion Based on a comprehensive search of multiple databases and quantitative synthesis of available evidence, this study provides a pooled estimate of the structural distribution of CMTs within the overall canine tumor spectrum in mainland China. The combined proportion was 34.0% (95% CI: 29.6%–45.8%), indicating that approximately one-third of tumor cases presented in clinical settings are of mammary origin. This figure falls at the higher end of the 20%–40% range reported internationally and is consistent with tumor registry data from Europe and Australia, where mammary tumors have long ranked among the most common or leading neoplasms in dogs [ 41 ][ 42 ]. These findings underscore the central role of mammary tumors within the companion animal oncology spectrum in China. It should be emphasized that the present analysis estimates the proportion of mammary tumors among diagnosed tumor cases, rather than the true incidence in the general canine population. In the absence of a nationwide canine tumor registry system in China, proportion-based analysis represents a pragmatic surrogate indicator for assessing clinical disease burden and informing resource allocation. The results provide quantitative support for the development of a national canine tumor database and the formulation of stratified prevention and control strategies. With respect to age distribution, the highest proportion of mammary tumors was observed in dogs aged 6–13 years (44.00%), and a relatively high level was maintained in the older age group (42.46%), whereas dogs younger than 6 years showed the lowest proportion (32.48%). This pattern aligns with the prevailing clinical observation in China that mammary tumors predominantly affect middle-aged and older dogs, and is consistent with international reports documenting increased risk in aged females [ 1 ][ 42 ]. Advancing age may promote tumor development through cumulative hormonal exposure, age-related decline in immune surveillance, and reduced tissue repair capacity [ 43 ]. Sex-stratified analysis demonstrated a marked predominance in females (58.51%) compared with males (0.26%), confirming at the population level the pivotal role of sex hormone exposure in tumorigenesis. Estrogen and progesterone stimulate proliferation of mammary epithelium, and repeated cyclic hormonal stimulation increases the likelihood of genetic alterations and dysregulated cell growth[ 3 ]. In contrast, the rarity of mammary tumors in male dogs is attributable to limited mammary gland development and lower endogenous hormone levels. This pronounced sex dependence, together with the spontaneous nature of the disease, reinforces the value of the dog as a comparative model for studying hormone-dependent tumors and tumor biology relevant to human breast cancer [ 3 ][ 9 ]. Body size stratification revealed that small-sized dogs had the highest proportion of mammary tumors (46.52%), significantly exceeding that observed in medium-sized (28.14%) and large-sized dogs (31.06%). This trend is consistent with domestic reports describing increased susceptibility in small breeds such as Poodles and Dachshunds, as well as findings from international studies. The elevated proportion in small dogs may be attributable to their longer life expectancy, which extends cumulative exposure of mammary epithelium to endogenous hormones and increases the probability of genetic mutations over time [ 9 ]. Differences in growth-axis hormone profiles, including insulin-like growth factor 1 (IGF-1), may also enhance hormonal responsiveness of mammary tissue in smaller dogs and promote aberrant cellular proliferation[ 3 ]. In addition, certain small breeds carry higher genetic susceptibility, and variants in tumor suppressor genes may lower the threshold for tumor development under relatively modest environmental pressures [ 43 ]. Analysis by neutering status indicated that intact dogs exhibited a higher proportion of mammary tumors (46.77%) than neutered dogs (38.74%), supporting the protective effect of ovariectomy against hormone-dependent tumors [ 4 ]. By eliminating ovarian sources of estrogen and progesterone, neutering interrupts the physiological stimulus that sustains mammary epithelial proliferation and potential malignant transformation [ 43 ]. Previous studies have emphasized the critical importance of timing, demonstrating that ovariectomy performed before the first estrus reduces the lifetime risk to below 0.5%, whereas the protective effect declines substantially with successive estrous cycles [ 3 , 4 ]. This pronounced hormonal sensitivity further substantiates the relevance of CMTs as a comparative model for human breast cancer. Regional stratification demonstrated significant variation in the proportion of CMTs across different areas of China. The highest proportion was observed in Northeastern China region, followed by Southern China region and Eastern China region, whereas Northwestern and Northern China region showed comparatively lower levels. Previous retrospective clinical studies from the Northeastern China region have likewise reported a relatively high contribution of mammary tumors within the local canine tumor spectrum [ 6 ], suggesting a comparatively greater burden in that region. However, such geographic variation does not necessarily reflect true differences in underlying risk. Economically developed Eastern and Southern China regions generally have a higher density of specialized veterinary hospitals and more accessible histopathological services, which may increase tumor detection rates. In contrast, limited veterinary resources in parts of Northwestern and Northern China region may contribute to underdiagnosis or reporting bias. At present, China lacks a unified national registry system for companion animal tumors, and most regional data are derived from single-center or hospital-based case series [ 44 ]. Stratification by study period also revealed significant differences in tumor proportion ( P < 0.001). The highest proportion was observed during 2015–2020, followed by 2010–2014, with a slight decline in 2021–2025. This temporal pattern may be related to improvements in companion animal medical services and shifts in case-reporting structures. Around 2015, the rapid development of specialized pet healthcare, along with broader implementation of pathological diagnosis and screening, may have increased the detection of mammary tumors. A multicenter retrospective study covering 2017–2021 in mainland China similarly reported fluctuations in the proportion of mammary tumors across years, indicating that temporal factors may influence the tumor spectrum structure [ 6 ]. In addition, the growing promotion of neutering and increasing awareness of preventive healthcare in recent years may have contributed to a relative reduction in the proportion of mammary tumors within the overall tumor spectrum. International studies have likewise noted a declining trend in mammary tumor proportion in association with increased neutering rates [ 41 ]. It should be emphasized that the present analysis reflects structural differences in tumor composition across time periods rather than true changes in population-level incidence, which would require long-term registry data for confirmation. Overall, CMTs in China do not yet demonstrate a highly uniform nationwide distribution pattern. Observed spatial variation is likely influenced by multiple interacting factors, including regional dog population density, neutering practices, access to veterinary care, and owner health-seeking behavior. Establishment of standardized tumor registry data and the implementation of multicenter, prospective regional comparative studies are warranted. The pooled measure in this study represents the proportion of mammary tumors among all confirmed canine neoplasms and constitutes a descriptive epidemiological parameter rather than an interventional effect size. Compared with intervention studies that may be influenced by publication preference for statistically significant or positive findings, proportion-based outcomes are theoretically less susceptible to selective publication. Visual inspection of the funnel plot did not reveal substantial asymmetry, and statistical testing did not detect a significant small-study effect. Taken together, no clear evidence of publication bias was identified, although the possibility of residual bias cannot be entirely excluded. The age concentration in middle-aged and older dogs, the marked female predominance, and the hormone-related characteristics observed in this study closely parallel the epidemiological pattern of human breast cancer. Such population-level similarities under natural disease conditions provide real-world epidemiological support for the dog as a spontaneous comparative model of female breast cancer. Compared with experimentally induced models, CMTs more faithfully reflect tumorigenesis arising from long-term hormonal exposure and environmental influences, thereby offering distinctive value in translational research. At present, surgical excision remains the mainstay of treatment for CMTs, with chemotherapy or endocrine therapy applied in selected cases. In recent years, localized drug delivery systems such as hydrogels have shown translational potential in canine oncology, and spontaneous canine tumors continue to be explored as comparative models for human cancer research. However, these approaches remain investigational and have not yet been standardized. In contrast, the establishment of a comprehensive canine tumor registry system and strengthened epidemiological surveillance represent fundamental priorities for disease control and prevention. Several limitations should be acknowledged. Most included studies were retrospective and hospital-based, which may not fully represent the general canine population and could introduce selection bias. Regional differences, variability in diagnostic criteria, and disparities in sample size may have contributed to residual heterogeneity. Incomplete reporting of potential confounders, including neutering status, breed, and age in some studies, limited the scope of stratified analyses. Although no significant publication bias was detected, the potential influence of unpublished data cannot be entirely excluded. The findings should therefore be interpreted with appropriate caution. Conclusion CMTs represent a major component of the canine tumor spectrum in most regions of Mainland China, predominantly affecting middle-aged and older female dogs. Differences observed across regions and study periods may reflect variations in neutering practices, pet population structure, and access to veterinary diagnostics. These findings highlight the need for standardized tumor registration and continuous epidemiological monitoring. The demographic pattern observed further supports the relevance of spontaneous CMTs as a comparative model for human breast cancer research. Methods Search Strategy A systematic literature search was conducted in the China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Database, PubMed, and the Web of Science Core Collection from database inception to March 2025. The search strategy combined controlled vocabulary (e.g., MeSH terms) and free-text terms and was adapted as appropriate for each database. Chinese search terms included “dog,” “canine,” “mammary tumor,” “mammary cancer,” “epidemiology,” “prevalence,” and “proportion.” English search terms included “dog,” “canine,” “mammary tumor,” “mammary neoplasm,” “prevalence,” “proportion,” and “epidemiology.” Boolean operators (“AND” and “OR”) were applied to construct the search expressions. All search strategies were independently developed by two investigators and cross-checked for consistency. In addition, the reference lists of eligible articles were manually screened to identify potentially relevant studies not captured in the electronic search. Inclusion and Exclusion Criteria The inclusion criteria were as follows: (1) studies conducted in mainland China; (2) study populations comprising canine tumor cases; (3) reporting the number of CMTs and the total number of canine tumors, or providing sufficient data for calculation; (4) clearly describing the diagnostic methods (e.g., histopathological confirmation or defined clinical diagnostic criteria); and (5) sample size ≥ 10. The exclusion criteria were as follows: (1) duplicate publications or repeated reporting of the same dataset; (2) reviews, case reports, or studies with insufficient sample size; and (3) studies with incomplete data or from which relevant information could not be extracted. Literature screening was performed independently by two investigators. The following information was extracted from each included study: first author, year of publication, study region, sampling year, diagnostic method, total number of canine tumors, and number of canine CMTs. Discrepancies were resolved through discussion or adjudication by a third reviewer. Statistical Analysis Data management and statistical analyses were performed using Stata version 15.1 (StataCorp, College Station, TX, USA) [ 45 ]. Although the inverse variance method is widely applied and performs adequately when the proportion of CMTs approximates 50%, studies reporting extreme proportions (0–30% or 70–100%) may receive disproportionate weights, potentially introducing bias. To minimize the influence of extreme values on the pooled estimate and to approximate normality of the distribution [ 46 ], the Freeman–Tukey double arcsine transformation was applied prior to pooling the proportions [ 47 , 48 ]. Between-study heterogeneity was assessed using Cochran’s Q test and the I² statistic. A P value < 0.05 in Cochran’s Q test was considered indicative of significant heterogeneity. The I² statistic was used to quantify the magnitude of heterogeneity, with values of 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively, and corresponding 95% confidence intervals (CIs) were calculated [ 49 , 50 ]. When I²≥50%, substantial heterogeneity was assumed and a random-effects model (DerSimonian–Laird method) was applied; otherwise, a fixed-effects model was used [ 51 ]. Stratified analyses were conducted to explore the potential effects of geographic region, sampling year, age, breed, and neutering status on the pooled estimates. Publication Bias and Sensitivity Analysis Publication bias was visually assessed using funnel plots and statistically evaluated by Egger’s regression test. A P value ≥ 0.05 was considered to indicate no significant publication bias, whereas P < 0.05 suggested potential bias [ 52 ]. Sensitivity analysis was performed using a leave-one-out approach, in which each study was sequentially excluded to assess its influence on the overall pooled estimate. Declarations Acknowledgments Not applicable. Ethics approval and consent to participate None required. Consent to Publish declaration Not applicable. This study did not involve identifiable individual data or personal information requiring consent for publication. Availability of data and materials Data available on request from the authors. Competing interests The authors declare that they have no competing interests. Funding Supported by the 111 Project D18007; A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Authors' contributions Conceptualization, X.L. and B.X.; methodology, X.L. and T.Z.; software, X.L. and B.L.; validation, Hongyu Zhou, X.L. and T.Z.; formal analysis, X.L. Huiling Zhang and B.X.; investigation, X.L. B.X. and B.L.; resources, X.L.; data curation, X.L., T.Z. and B.L.; writing—original draft preparation, X.L. and Hongyu Zhou; writing—review and editing, Huiling Zhang and T.Z.; visualization, X.L.; supervision, T.Z.; project administration, Huiling Zhang and T.Z.; funding acquisition, T.Z. All authors have read and agreed to the published version of the manuscript. References Sorenmo K, Kristiansen V, Cofone M, Shofer F, Breen A, Langeland M, Mongil CM, Grondahl AM, Teige J, Goldschmidt M. Canine mammary gland tumours: a histological continuum from benign to malignant; clinical and histopathological evidence. Vet Comp Oncol. 2009;7:162–72. https://doi.org/10.1111/j.1476-5829.2009.00184.x . Goldschmidt M, Peña L, Rasotto R, Zappulli V. Classification and grading of canine mammary tumors. Vet Pathol. 2011;48:117–31. https://doi.org/10.1177/0300985810393258 . Salas Y, Márquez A, Diaz D, Romero L. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9105294","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625130024,"identity":"a61cfe1d-1ebe-431e-9b28-f39679f216a3","order_by":0,"name":"Xinyuan Liu","email":"","orcid":"","institution":"Institute of Comparative Medicine, College of Veterinary Medicine, Yangzhou University, Yangzhou, Jiangsu 225009, China","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Liu","suffix":""},{"id":625130025,"identity":"c9544585-9d8a-47ac-ab01-182a6cc48c74","order_by":1,"name":"Bohan 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proportion of CMTs among all canine tumors in mainland China\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9105294/v1/f354dd3a4cf9abd42aebd258.jpg"},{"id":107618509,"identity":"dc9cd4d2-e806-4727-8627-c9fe047fb97a","added_by":"auto","created_at":"2026-04-23 09:25:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59211,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of the proportion of CMTs across different regions in mainland China\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9105294/v1/e3d13099ac86c06313e2875e.jpg"},{"id":107618844,"identity":"f1af59ad-e32a-4130-8ba9-c00ff7b916af","added_by":"auto","created_at":"2026-04-23 09:26:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":258876,"visible":true,"origin":"","legend":"\u003cp\u003eEgger’s test for publication bias of the pooled proportion of CMTs\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9105294/v1/6ac1d603235c70d1e5a1dccb.png"},{"id":107618559,"identity":"925792e8-f927-4930-a969-473e9f6a1421","added_by":"auto","created_at":"2026-04-23 09:25:46","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123795,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out sensitivity analysis of the pooled proportion of CMTs\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9105294/v1/6cad683e8fbb550c774d52f5.jpg"},{"id":107619048,"identity":"f409ded2-090b-4130-8339-6a6dd583996b","added_by":"auto","created_at":"2026-04-23 09:27:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1219492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9105294/v1/4b3a8bd1-edb4-4b51-84f3-b3417a59cceb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distribution and Associated Factors of Canine Mammary Tumors within the Canine Tumor Spectrum in Mainland China","fulltext":[{"header":"Background","content":"\u003cp\u003eDogs are among the most important companion animals worldwide, and their health status directly affects both animal welfare and the physical and psychological well-being of their owners. With the rapid expansion of the pet industry and the increasing urban dog population in China, neoplastic diseases have become an increasingly significant clinical concern. Among these, CMTs are recognized as one of the most frequently diagnosed neoplasms in female dogs, accounting for a substantial proportion of all canine tumors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, mammary tumors represent approximately 40\u0026ndash;50% of all tumors in intact female dogs, of which nearly half are malignant, resulting in considerable heterogeneity in biological behavior and prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Tumor development is closely associated with cumulative exposure to ovarian hormones. Classical epidemiological studies have demonstrated that ovariectomy performed before the first estrus reduces the risk of mammary tumor development by more than 99%, and this protective effect progressively diminishes with successive estrous cycles [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Breed predisposition and age are also well-established risk factors. Small and toy breeds exhibit increased susceptibility, and the peak incidence typically occurs in middle-aged to older dogs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn China, several hospital-based investigations have reported that mammary tumors constitute a predominant proportion of surgically excised canine neoplasms, with more than half of the cases confirmed as malignant [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Regional studies conducted in major metropolitan areas, including Wuhan, further indicate that the majority of affected dogs are intact females and that most cases occur in animals older than 8 years [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, these findings are largely derived from single institutions or geographically restricted populations. In the absence of a nationwide veterinary tumor registry, the overall distribution pattern of CMTs in mainland China remains poorly defined. Marked regional differences exist among central-eastern, northeastern, southwestern, and northwestern China region with respect to economic development, implementation of neutering practices, and breeding patterns. Such disparities may contribute to substantial heterogeneity in reported proportions, yet no quantitative synthesis has been performed to date.\u003c/p\u003e \u003cp\u003eGiven the large and geographically dispersed canine population in China, conducting population-based epidemiological surveys to estimate incidence is logistically challenging. Accordingly, calculating the proportion of mammary tumors among clinically diagnosed neoplasms in companion animal practice represents a more feasible surrogate measure of disease burden. Nevertheless, existing epidemiological data from different regions have not been systematically integrated, and a pooled national estimate is lacking. Potential influencing factors have also not been comprehensively evaluated [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, a systematic search of the literature on CMTs in mainland China was conducted, and quantitative data from independent studies were synthesized using a random-effects model to estimate the pooled proportion of mammary tumors among all canine neoplasms. Stratified analyses were performed according to geographic region, sampling year, age, breed, sex, and neutering status to explore potential sources of heterogeneity. By providing a quantitative description of the national distribution pattern and associated factors, this study seeks to clarify the characteristics of disease burden in companion animal oncology in China and to provide an evidence base for preventive strategies and the rational allocation of clinical resources.\u003c/p\u003e \u003cp\u003eIn parallel with advances in comparative oncology, canine mammary carcinoma has been increasingly recognized as a valuable spontaneous animal model for human breast cancer. Owing to its natural occurrence and its substantial similarities to human breast cancer in terms of molecular subtypes, hormone receptor expression, and patterns of tumor progression, it offers important translational relevance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A systematic synthesis of epidemiological evidence on CMTs in mainland China will therefore not only contribute to improved management of companion animal diseases, but also provide population-level data to support cross-species translational research in breast cancer, thereby expanding its application in comparative medicine.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Selection\u003c/h2\u003e\n \u003cp\u003eAccording to the predefined search strategy, a total of 630 records were retrieved from five databases (search period: January 1, 2000 to March 31, 2025). After removal of duplicates and screening of titles and abstracts, 567 records were excluded. Following full-text assessment, 33 irrelevant studies, 32 studies with non-extractable data, and 1 study with incomplete reporting were excluded. Ultimately, 31 studies were included in the quantitative synthesis. The study selection process is presented in Figure. 1.\u003c/p\u003e\n \u003cp\u003eAlthough the search period covered 2000 to 2025, the actual data collection of the included studies was primarily conducted between 2010 and 2025, and the articles were published between 2015 and 2025 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A total of 5,947 CMTs were included, with individual study sample sizes ranging from 15 to 1,079. All included studies were based on histopathological examination or clearly defined clinical diagnostic criteria. The main characteristics of the included studies are summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of the included studies\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSampling Year\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSampling Region\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNo. of CMTs / Total Tumors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBai et al., 2019 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWeifang(Central-Eastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e23/70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e32.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChang et al., 2018 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2016\u0026ndash;2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHarbin(Northeastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e29/71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e40.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChang, 2019 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2014\u0026ndash;2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHarbin(Northeastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e138/271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e50.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChen, 2017 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2009\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eKunming(Southern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e96/378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e5.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChen, 2019 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2017\u0026ndash;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHenan(Eastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e17/105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e16.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHe, 2022 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBeijing(Northern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e13/115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e11.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHuang, 2014 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2010\u0026ndash;2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eShenzhen(Southern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9/38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e23.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHuang, 2023 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2013\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eGuangdong(Southern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e187/439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e42.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLi et al., 2021 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eYunnan(Southern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e53.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLiu et al., 2013 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2009\u0026ndash;2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eXi\u0026rsquo;an(Northwestern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e10/57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e17.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLiu et al., 2013 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2010\u0026ndash;2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBeijing(Northern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e74/156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e47.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLu et al., 2016 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2014\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTianjin(Northern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e31.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMa, 2024 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2015\u0026ndash;2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eXi\u0026rsquo;an(Northwestern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e90/295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e30.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNi, 2024 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eShandong(Eastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e205/632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e32.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eQu et al., 2019 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2017\u0026ndash;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eJilin(Northeastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12/32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e37.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShao et al., 2019 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eXinjiang(Northwestern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e26.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShao, 2022 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eXinjiang(Northwestern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e28.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShen et al., 2017[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNanjing(Eastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11/35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e31.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShen, 2019 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2015\u0026ndash;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNanjing(Eastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e30/66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e45.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eShi, 2020 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBeijing(Northern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e113/247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e45.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWang et al., 2017 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2015\u0026ndash;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBeijing(Northern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4/44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e9.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWang, 2024 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2021\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBeijing(Northern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e24/206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e11.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eXian, 2021 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2018\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eShenyang(Northeastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94/196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e47.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYan, 2014 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHefei(Eastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e45/104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e43.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYang, 2025 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2021\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eChangchun(Northeastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e186/448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYe, 2024 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026Uuml;r\u0026uuml;mqi(Northwestern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e29/66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e43.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYu, 2013 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2010\u0026ndash;2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eChangsha(Eastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11/46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e23.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZhang et al., 2021 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2018\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eJilin(Northeastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e50/98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e51.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZheng et al., 2022 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2017\u0026ndash;2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMainland China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e504/1079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e46.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZhu et al., 2015 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2012\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHangzhou(Eastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e56/133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e42.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZou, 2024 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2019\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWuhan(Eastern China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e140/425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e32.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e* Data missing for Tibet due to lack of available literature\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eQuality Assessment of Included Studies\u003c/h3\u003e\n\u003cp\u003eDiagnostic criteria, sample size, and data completeness were carefully reviewed to ensure accurate extraction of the number of mammary tumors and total tumor cases. Overall, reporting quality was acceptable, and no apparent source of systematic bias was identified.\u003c/p\u003e\n\u003ch3\u003eData Synthesis and Heterogeneity Analysis\u003c/h3\u003e\n\u003cp\u003eA pooled analysis of studies conducted between 2010 and 2025 was performed (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The reported proportion of CMTs among all canine neoplasms ranged from 5.4% to 53.3%. Significant heterogeneity was observed across studies (\u0026chi;\u0026sup2; = 333.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; I\u0026sup2; = 91.0%). Therefore, a random-effects model was applied, yielding a pooled proportion of 34.0% (95% CI: 29.6%\u0026ndash;45.8%) for mammary tumors among all canine tumors in mainland China.\u003c/p\u003e\n\u003ch3\u003eStratified Analysis\u003c/h3\u003e\n\u003cp\u003eGiven the substantial heterogeneity identified in the overall analysis (I\u0026sup2; \u0026gt; 50%), stratified analyses were conducted to explore potential sources of variation. Subgroup analyses were performed according to geographic region, age distribution, sex, body size, study period, and neutering status. As heterogeneity remained considerable within subgroups, random-effects models were consistently applied to estimate pooled proportions for each stratum. The results are presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStratified analysis of pooled CMT proportions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eNo. Studies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal Tumor Cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eCMT Cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003ePooled Proportion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\n \u003cp\u003eHeterogeneity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eEstimates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eQ(x\u0026sup2;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003ePQ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eI\u0026sup2;(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e32.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.15,0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e36.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e83.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6\u0026ndash;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e44.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.25,0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e82.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e90.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e42.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.29,0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e31.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e77.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e58.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.48,0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e221.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e91.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.01,0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e16.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e58.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutering Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNeutered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.31,0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e46.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e76.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIntact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e46.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.37,0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e39.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e74.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e46.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.33,0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e95.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e89.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e28.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.20,0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e51.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e86.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e31.06%%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.18,0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e59.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e86.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNortheastern China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e45.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.43,0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e9.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e35.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"4\" rowspan=\"5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNorthern China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e29.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.15,0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e153.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e96.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCentral-Eastern China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e33.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.29,0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e37.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e75.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSouthern China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e34.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.26,0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e32.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e87.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNorthwestern China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e30.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.25,0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e11.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e56.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e38.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.24,0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e33.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e81.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2015\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e41.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.26,0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e222.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e93.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2021\u0026ndash;2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e33.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.17,0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e98.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e96.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the body size stratification, significant differences were observed among size categories (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Based on domestic classification standards, dogs were categorized as small, medium, or large. The highest proportion of mammary tumors was observed in small-sized dogs (46.52%, 95% CI: 33%\u0026ndash;53%), followed by large-sized dogs (31.06%, 95% CI: 18%\u0026ndash;56%), while medium-sized dogs showed a comparatively lower proportion (28.14%, 95% CI: 20%\u0026ndash;41%).\u003c/p\u003e\n\u003cp\u003eAge-stratified analysis also revealed significant differences among age groups (P\u0026thinsp;=\u0026thinsp;0.01). The proportion of mammary tumors was 32.48% (95% CI: 15%\u0026ndash;45%) in dogs younger than 6 years, 44.00% (95% CI: 25%\u0026ndash;52%) in dogs aged 6\u0026ndash;13years, and 42.46% (95% CI: 29%\u0026ndash;49%) in dogs older than 13 years. These findings indicate that mammary tumors predominantly occur in middle-aged and older dogs aged 6 years and above.\u003c/p\u003e\n\u003cp\u003eSex-stratified analysis demonstrated a marked difference in proportions (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mammary tumors occurred predominantly in female dogs, accounting for 58.51% (95% CI: 48%\u0026ndash;63%) of tumors among affected females. Neutering status was also significantly associated with tumor proportion. Intact dogs exhibited a higher proportion of mammary tumors (46.77%, 95% CI: 37%\u0026ndash;49%).\u003c/p\u003e\n\u003cp\u003eRegional stratification revealed significant variation across geographic areas (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest proportion was observed in Northeastern China region(45.61%, 95% CI: 43%\u0026ndash;49%). Relatively lower proportions were found in Northern China region(29.74%, 95% CI: 15%\u0026ndash;38%) and Northwestern China region (30.70%, 95% CI: 25%\u0026ndash;35%). Central-Eastern and Southern China region showed intermediate levels, at 33.19% (95% CI: 29%\u0026ndash;37%) and 34.48% (95% CI: 26%\u0026ndash;42%), respectively (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No data were available from the Tibet region.\u003c/p\u003e\n\u003cp\u003eStratification by study period also indicated significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest pooled proportion was observed during 2015\u0026ndash;2020 (41.50%, 95% CI: 26%\u0026ndash;43%), followed by 2010\u0026ndash;2014 (38.01%, 95% CI: 24%\u0026ndash;43%), whereas a slight decrease was noted in 2021\u0026ndash;2025 (33.10%, 95% CI: 17%\u0026ndash;47%).\u003c/p\u003e\n\u003ch3\u003ePublication Bias and Sensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eEgger\u0026rsquo;s regression test was applied to assess potential publication bias in the reported proportions of CMTs. The regression intercept test yielded a P value\u0026thinsp;\u0026gt;\u0026thinsp;0.05, indicating no statistically significant evidence of publication bias (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Sensitivity analysis was subsequently conducted using a leave-one-out approach. Sequential exclusion of individual studies did not materially alter the pooled estimate. All recalculated effect sizes remained within the 95% confidence interval of the overall pooled proportion, supporting the robustness and stability of the findings (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on a comprehensive search of multiple databases and quantitative synthesis of available evidence, this study provides a pooled estimate of the structural distribution of CMTs within the overall canine tumor spectrum in mainland China. The combined proportion was 34.0% (95% CI: 29.6%\u0026ndash;45.8%), indicating that approximately one-third of tumor cases presented in clinical settings are of mammary origin. This figure falls at the higher end of the 20%\u0026ndash;40% range reported internationally and is consistent with tumor registry data from Europe and Australia, where mammary tumors have long ranked among the most common or leading neoplasms in dogs [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e][\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These findings underscore the central role of mammary tumors within the companion animal oncology spectrum in China.\u003c/p\u003e \u003cp\u003eIt should be emphasized that the present analysis estimates the proportion of mammary tumors among diagnosed tumor cases, rather than the true incidence in the general canine population. In the absence of a nationwide canine tumor registry system in China, proportion-based analysis represents a pragmatic surrogate indicator for assessing clinical disease burden and informing resource allocation. The results provide quantitative support for the development of a national canine tumor database and the formulation of stratified prevention and control strategies.\u003c/p\u003e \u003cp\u003eWith respect to age distribution, the highest proportion of mammary tumors was observed in dogs aged 6\u0026ndash;13 years (44.00%), and a relatively high level was maintained in the older age group (42.46%), whereas dogs younger than 6 years showed the lowest proportion (32.48%). This pattern aligns with the prevailing clinical observation in China that mammary tumors predominantly affect middle-aged and older dogs, and is consistent with international reports documenting increased risk in aged females [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Advancing age may promote tumor development through cumulative hormonal exposure, age-related decline in immune surveillance, and reduced tissue repair capacity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSex-stratified analysis demonstrated a marked predominance in females (58.51%) compared with males (0.26%), confirming at the population level the pivotal role of sex hormone exposure in tumorigenesis. Estrogen and progesterone stimulate proliferation of mammary epithelium, and repeated cyclic hormonal stimulation increases the likelihood of genetic alterations and dysregulated cell growth[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In contrast, the rarity of mammary tumors in male dogs is attributable to limited mammary gland development and lower endogenous hormone levels. This pronounced sex dependence, together with the spontaneous nature of the disease, reinforces the value of the dog as a comparative model for studying hormone-dependent tumors and tumor biology relevant to human breast cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBody size stratification revealed that small-sized dogs had the highest proportion of mammary tumors (46.52%), significantly exceeding that observed in medium-sized (28.14%) and large-sized dogs (31.06%). This trend is consistent with domestic reports describing increased susceptibility in small breeds such as Poodles and Dachshunds, as well as findings from international studies. The elevated proportion in small dogs may be attributable to their longer life expectancy, which extends cumulative exposure of mammary epithelium to endogenous hormones and increases the probability of genetic mutations over time [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Differences in growth-axis hormone profiles, including insulin-like growth factor 1 (IGF-1), may also enhance hormonal responsiveness of mammary tissue in smaller dogs and promote aberrant cellular proliferation[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, certain small breeds carry higher genetic susceptibility, and variants in tumor suppressor genes may lower the threshold for tumor development under relatively modest environmental pressures [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnalysis by neutering status indicated that intact dogs exhibited a higher proportion of mammary tumors (46.77%) than neutered dogs (38.74%), supporting the protective effect of ovariectomy against hormone-dependent tumors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. By eliminating ovarian sources of estrogen and progesterone, neutering interrupts the physiological stimulus that sustains mammary epithelial proliferation and potential malignant transformation [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Previous studies have emphasized the critical importance of timing, demonstrating that ovariectomy performed before the first estrus reduces the lifetime risk to below 0.5%, whereas the protective effect declines substantially with successive estrous cycles [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This pronounced hormonal sensitivity further substantiates the relevance of CMTs as a comparative model for human breast cancer.\u003c/p\u003e \u003cp\u003eRegional stratification demonstrated significant variation in the proportion of CMTs across different areas of China. The highest proportion was observed in Northeastern China region, followed by Southern China region and Eastern China region, whereas Northwestern and Northern China region showed comparatively lower levels. Previous retrospective clinical studies from the Northeastern China region have likewise reported a relatively high contribution of mammary tumors within the local canine tumor spectrum [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], suggesting a comparatively greater burden in that region. However, such geographic variation does not necessarily reflect true differences in underlying risk. Economically developed Eastern and Southern China regions generally have a higher density of specialized veterinary hospitals and more accessible histopathological services, which may increase tumor detection rates. In contrast, limited veterinary resources in parts of Northwestern and Northern China region may contribute to underdiagnosis or reporting bias. At present, China lacks a unified national registry system for companion animal tumors, and most regional data are derived from single-center or hospital-based case series [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStratification by study period also revealed significant differences in tumor proportion (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest proportion was observed during 2015\u0026ndash;2020, followed by 2010\u0026ndash;2014, with a slight decline in 2021\u0026ndash;2025. This temporal pattern may be related to improvements in companion animal medical services and shifts in case-reporting structures. Around 2015, the rapid development of specialized pet healthcare, along with broader implementation of pathological diagnosis and screening, may have increased the detection of mammary tumors. A multicenter retrospective study covering 2017\u0026ndash;2021 in mainland China similarly reported fluctuations in the proportion of mammary tumors across years, indicating that temporal factors may influence the tumor spectrum structure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, the growing promotion of neutering and increasing awareness of preventive healthcare in recent years may have contributed to a relative reduction in the proportion of mammary tumors within the overall tumor spectrum. International studies have likewise noted a declining trend in mammary tumor proportion in association with increased neutering rates [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. It should be emphasized that the present analysis reflects structural differences in tumor composition across time periods rather than true changes in population-level incidence, which would require long-term registry data for confirmation.\u003c/p\u003e \u003cp\u003eOverall, CMTs in China do not yet demonstrate a highly uniform nationwide distribution pattern. Observed spatial variation is likely influenced by multiple interacting factors, including regional dog population density, neutering practices, access to veterinary care, and owner health-seeking behavior. Establishment of standardized tumor registry data and the implementation of multicenter, prospective regional comparative studies are warranted.\u003c/p\u003e \u003cp\u003eThe pooled measure in this study represents the proportion of mammary tumors among all confirmed canine neoplasms and constitutes a descriptive epidemiological parameter rather than an interventional effect size. Compared with intervention studies that may be influenced by publication preference for statistically significant or positive findings, proportion-based outcomes are theoretically less susceptible to selective publication. Visual inspection of the funnel plot did not reveal substantial asymmetry, and statistical testing did not detect a significant small-study effect. Taken together, no clear evidence of publication bias was identified, although the possibility of residual bias cannot be entirely excluded.\u003c/p\u003e \u003cp\u003eThe age concentration in middle-aged and older dogs, the marked female predominance, and the hormone-related characteristics observed in this study closely parallel the epidemiological pattern of human breast cancer. Such population-level similarities under natural disease conditions provide real-world epidemiological support for the dog as a spontaneous comparative model of female breast cancer. Compared with experimentally induced models, CMTs more faithfully reflect tumorigenesis arising from long-term hormonal exposure and environmental influences, thereby offering distinctive value in translational research.\u003c/p\u003e \u003cp\u003eAt present, surgical excision remains the mainstay of treatment for CMTs, with chemotherapy or endocrine therapy applied in selected cases. In recent years, localized drug delivery systems such as hydrogels have shown translational potential in canine oncology, and spontaneous canine tumors continue to be explored as comparative models for human cancer research. However, these approaches remain investigational and have not yet been standardized. In contrast, the establishment of a comprehensive canine tumor registry system and strengthened epidemiological surveillance represent fundamental priorities for disease control and prevention.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. Most included studies were retrospective and hospital-based, which may not fully represent the general canine population and could introduce selection bias. Regional differences, variability in diagnostic criteria, and disparities in sample size may have contributed to residual heterogeneity. Incomplete reporting of potential confounders, including neutering status, breed, and age in some studies, limited the scope of stratified analyses. Although no significant publication bias was detected, the potential influence of unpublished data cannot be entirely excluded. The findings should therefore be interpreted with appropriate caution.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCMTs represent a major component of the canine tumor spectrum in most regions of Mainland China, predominantly affecting middle-aged and older female dogs. Differences observed across regions and study periods may reflect variations in neutering practices, pet population structure, and access to veterinary diagnostics. These findings highlight the need for standardized tumor registration and continuous epidemiological monitoring. The demographic pattern observed further supports the relevance of spontaneous CMTs as a comparative model for human breast cancer research.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSearch Strategy\u003c/h2\u003e \u003cp\u003eA systematic literature search was conducted in the China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Database, PubMed, and the Web of Science Core Collection from database inception to March 2025. The search strategy combined controlled vocabulary (e.g., MeSH terms) and free-text terms and was adapted as appropriate for each database. Chinese search terms included \u0026ldquo;dog,\u0026rdquo; \u0026ldquo;canine,\u0026rdquo; \u0026ldquo;mammary tumor,\u0026rdquo; \u0026ldquo;mammary cancer,\u0026rdquo; \u0026ldquo;epidemiology,\u0026rdquo; \u0026ldquo;prevalence,\u0026rdquo; and \u0026ldquo;proportion.\u0026rdquo; English search terms included \u0026ldquo;dog,\u0026rdquo; \u0026ldquo;canine,\u0026rdquo; \u0026ldquo;mammary tumor,\u0026rdquo; \u0026ldquo;mammary neoplasm,\u0026rdquo; \u0026ldquo;prevalence,\u0026rdquo; \u0026ldquo;proportion,\u0026rdquo; and \u0026ldquo;epidemiology.\u0026rdquo;\u003c/p\u003e \u003cp\u003eBoolean operators (\u0026ldquo;AND\u0026rdquo; and \u0026ldquo;OR\u0026rdquo;) were applied to construct the search expressions. All search strategies were independently developed by two investigators and cross-checked for consistency. In addition, the reference lists of eligible articles were manually screened to identify potentially relevant studies not captured in the electronic search.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eThe inclusion criteria were as follows: (1) studies conducted in mainland China; (2) study populations comprising canine tumor cases; (3) reporting the number of CMTs and the total number of canine tumors, or providing sufficient data for calculation; (4) clearly describing the diagnostic methods (e.g., histopathological confirmation or defined clinical diagnostic criteria); and (5) sample size\u0026thinsp;\u0026ge;\u0026thinsp;10.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: (1) duplicate publications or repeated reporting of the same dataset; (2) reviews, case reports, or studies with insufficient sample size; and (3) studies with incomplete data or from which relevant information could not be extracted.\u003c/p\u003e \u003cp\u003eLiterature screening was performed independently by two investigators. The following information was extracted from each included study: first author, year of publication, study region, sampling year, diagnostic method, total number of canine tumors, and number of canine CMTs. Discrepancies were resolved through discussion or adjudication by a third reviewer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData management and statistical analyses were performed using Stata version 15.1 (StataCorp, College Station, TX, USA) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Although the inverse variance method is widely applied and performs adequately when the proportion of CMTs approximates 50%, studies reporting extreme proportions (0\u0026ndash;30% or 70\u0026ndash;100%) may receive disproportionate weights, potentially introducing bias. To minimize the influence of extreme values on the pooled estimate and to approximate normality of the distribution [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], the Freeman\u0026ndash;Tukey double arcsine transformation was applied prior to pooling the proportions [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBetween-study heterogeneity was assessed using Cochran\u0026rsquo;s Q test and the I\u0026sup2; statistic. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in Cochran\u0026rsquo;s Q test was considered indicative of significant heterogeneity. The I\u0026sup2; statistic was used to quantify the magnitude of heterogeneity, with values of 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively, and corresponding 95% confidence intervals (CIs) were calculated [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. When I\u0026sup2;\u0026ge;50%, substantial heterogeneity was assumed and a random-effects model (DerSimonian\u0026ndash;Laird method) was applied; otherwise, a fixed-effects model was used [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStratified analyses were conducted to explore the potential effects of geographic region, sampling year, age, breed, and neutering status on the pooled estimates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePublication Bias and Sensitivity Analysis\u003c/h2\u003e \u003cp\u003ePublication bias was visually assessed using funnel plots and statistically evaluated by Egger\u0026rsquo;s regression test. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026ge;\u0026thinsp;0.05 was considered to indicate no significant publication bias, whereas \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 suggested potential bias [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSensitivity analysis was performed using a leave-one-out approach, in which each study was sequentially excluded to assess its influence on the overall pooled estimate.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study did not involve identifiable individual data or personal information requiring consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available on request from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by the 111 Project D18007;\u003c/p\u003e\n\u003cp\u003eA Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, X.L. and B.X.; methodology, X.L. and T.Z.; software, X.L. and B.L.; validation, Hongyu Zhou, X.L. and T.Z.; formal analysis, X.L. Huiling Zhang and B.X.; investigation, X.L. B.X. and B.L.; resources, X.L.; data curation, X.L., T.Z. and B.L.; writing\u0026mdash;original draft preparation, X.L. and Hongyu Zhou; writing\u0026mdash;review and editing, Huiling Zhang and T.Z.; visualization, X.L.; supervision, T.Z.; project administration, Huiling Zhang and T.Z.; funding acquisition, T.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSorenmo K, Kristiansen V, Cofone M, Shofer F, Breen A, Langeland M, Mongil CM, Grondahl AM, Teige J, Goldschmidt M. Canine mammary gland tumours: a histological continuum from benign to malignant; clinical and histopathological evidence. 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Assessing heterogeneity in meta-analysis: Q statistic or I\u0026sup2;. index? Psychol Methods. 2006;11:193\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/1082-989X.11.2.193\u003c/span\u003e\u003cspan address=\"10.1037/1082-989X.11.2.193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBegg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2533446\u003c/span\u003e\u003cspan address=\"10.2307/2533446\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Canine mammary tumor, Tumor proportion, Stratified analysis, Epidemiology, Mainland China","lastPublishedDoi":"10.21203/rs.3.rs-9105294/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9105294/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCanine mammary tumors (CMTs) are among the most frequently diagnosed neoplasms in female dogs and represent an important health concern in companion animal medicine. However, their overall contribution to the canine tumor spectrum in mainland China has not been systematically quantified.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA systematic search of CNKI, Wanfang, VIP, PubMed, and Web of Science Core Collection was conducted from database inception to March 2025 to identify studies reporting the number of CMTs and total canine tumors in mainland China. Pooled proportions were estimated using a random-effects model with Freeman\u0026ndash;Tukey double arcsine transformation. Thirty-one studies involving 5,947 canine tumor cases were included. The pooled proportion of CMTs among all canine tumors was 34.0% (95% CI: 29.6%\u0026ndash;45.8%), with substantial heterogeneity (I\u0026sup2; = 91.0%). Higher proportions were observed in female dogs, intact dogs, small-sized breeds, and dogs aged\u0026thinsp;\u0026ge;\u0026thinsp;6 years. Regional variation was evident, with the highest proportion reported in Northeastern China, and temporal differences were also observed across study periods. No significant publication bias was detected, and sensitivity analysis confirmed the robustness of the results.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCMTs represent a substantial component of the canine tumor spectrum in mainland China. The observed demographic patterns support the relevance of spontaneous CMTs as a comparative model for human breast cancer research.\u003c/p\u003e","manuscriptTitle":"Distribution and Associated Factors of Canine Mammary Tumors within the Canine Tumor Spectrum in Mainland China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:23:29","doi":"10.21203/rs.3.rs-9105294/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"300257590851107743659967934134287783462","date":"2026-04-27T07:34:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T08:09:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-17T11:02:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T12:25:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-16T12:25:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2026-03-12T13:19:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5af6f01-62f8-4864-80f7-de4a9fbb2d96","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:23:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9105294","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9105294","identity":"rs-9105294","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}