Signalment of dogs and histopathological features of subcutaneous and cutaneous mast cell tumors

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In the past, the distinction between cutaneous MCTs (cMCTs), originating from the dermis, and subcutaneous MCTs (scMCTs), originating from the subcutaneous tissue, was not made. Histopathological differentiation, including grading, is important for prognostication. However, the Patnaik and Kiupel grading systems were proposed for cMCTs only. The objective of our study was to describe and compare the signalment of dogs with scMCTs and cMCTs and histopathological features, anticipating similarities in both groups. Data of dogs histologically diagnosed with scMCTs or cMCTs between September 2020 and July 2023 were retrospectively analyzed. Signalment, tumor location, histopathological features, completeness of removal and lymph node status were recorded. Results Data on 305 scMCTs and 1291 cMCTs were collected. Mitotic count (MC) was not different between scMCTs (1.63) and cMCTs (1.58) (P = 0.8490). Compared to cMCTs, scMCTs more often had anisokaryosis, bizarre nuclei and multinucleation. Kiupel high grade was more often assigned to scMCTs (51/292, 17.5%) than cMCTs (154/1291, 11.9%) (P = 0.009). The odds of MCTs being assigned a high grade in scMCT was 1.578 higher than in cMCTs (95% confidence interval [1.116–2.232]). Conclusions Breed distribution was different for scMCTs and cMCTs. Histopathological differences between scMCTs and cMCTs were observed. A Kiupel high grade was more often assigned to scMCTs than cMCTs. Whether these histopathological findings correlate with clinical outcome has to be established in additional studies. Canine grading system Kiupel neoplasm Patnaik Figures Figure 1 Background Mast cell tumors (MCTs) comprise up to one fifth of skin tumors in dogs, rendering them the most common malignant skin neoplasm in this species [ 1 – 2 ]. Cutaneous MCTs originate from the dermis and can extent into the underlying subcutis and muscles [ 3 ]. Literature since then adapted the term cMCTs. It took until 2007, when a separate subset of MCTs originating from subcutaneous tissues has first been described [ 4 ]. Therapeutic decisions in canine cMCTs are based on the clinical condition of the dog, anatomic location of the tumor, staging, and histopathological differentiation, including grading, with the latter being one of the most crucial prognostic predictors [ 3 – 7 ]. A recent consensus proposal regarding diagnostic criteria and classification of MCTs has emphasized the importance of reporting the origin (cutaneous versus subcutaneous) and, for prognostication, to grade both tumor types [ 8 ]. The grading systems that are currently used to grade MCTs (3-tier Patnaik and 2-tier Kiupel) were both designed for grading cMCTs [ 3 , 6 ]. However, since scMCTs were historically regarded as a subcutaneous variant of cMCTs, there are concerns whether some scMCTs were not inadvertently included when those grading systems were developed. In the absence of a grading system for scMCTs, negative prognostic factors that have been used to assess these tumors on histopathology are mitotic count (MC), multinucleation and infiltrative growth pattern [ 7 , 9 , 10 ]. The decision whether or not to grade scMCTs according to one of the existing grading systems was at the discretion of the pathologist. However, Sabattini and colleagues very recently studied the prognostic value of the Kiupel 2-tier grading in scMCT in dogs and concluded that it enables identification of aggressive biological behavior in scMCT cases, similar to cMCT cases [ 11 ]. Earlier, in terms of prognosis, it was believed that the majority of scMCTs exhibited a favorable prognosis compared to cMCTs, with extended survival times and low metastatic rates and recurrence rates (4% and 8%, respectively) [ 7 ]. Later studies solely focusing on scMCTs, however, reported that a larger proportion of the scMCT cases might exhibit an aggressive biologic behavior [ 10 , 12 – 14 ]. Our study aimed to describe signalment of dogs and histopathological features of scMCTs and cMCTs across a large dataset of canine MCTs of the skin. Our hypothesis centered on the comparability of histopathological features between scMCTs and cMCTs, anticipating similar characteristics in both groups. Methods Anonymized pathology databases from 2 board-certified pathologists (A and B) from a single laboratory were screened and included reports of canine tissue samples from primary, secondary, and tertiary veterinary centers. Pathology reports that mentioned “skin” and “mast cells” between September 2020 and July 2023 were reviewed. Each report lacking the diagnosis of MCT or specific information on tumor origin (cutaneous versus subcutaneous) were excluded. Reports of dogs with more than one MCT, reports that mentioned incisional biopsy or reports that mentioned mast cells without histopathological diagnosis of MCT were excluded. Data retrieved from the database included information on signalment (breed, gender, and age), tumor dimension (in mm), and tumor origin (cutaneous or subcutaneous). For the purpose of the study, 9 categories for location were established: extremity, flank, perineal and genital region, back, head and neck, mammary gland, thorax, tail region, or buttock area). Histopathological features retrieved from the database were MC, bizarre nuclei and multinucleation, cytoplasmatic granules, eosinophil count, anisokaryosis, completeness of removal, histopathological grade (2-tier Patnaik and 3-tier Kiupel), and potential lymph node involvement. The MC was assessed in areas with the highest mitotic activity and reported as an absolute value, defined as the number of mitotic figures per 10 high-power fields (HPF) (x400, 2.37 mm²). For the purpose of the study, the presence of bizarre nuclei and multinucleation (in 10 HPF) was categorized in 4 categories (none or one bizarre nuclei/10 HPF, less than 3 bizarre nuclei/10 HPF, 3 or more bizarre nuclei/10 HPF, present but undefined). Presence of cytoplasmic granules had been categorized as a ‘small,’ ‘moderate,’ or ‘large’ number by each pathologist. Similarly, the number of eosinophils was categorized as ‘low,’ ‘moderate,’ or ‘high’. Pathologist A reported the presence and degree of anisokaryosis as a % of neoplastic cells of the total neoplastic population, that exhibits a 2-fold variation in nuclear size. Pathologist B reported the presence and degree of anisokaryosis as none/mild/moderate or marked. Only data of pathologist A were used for statistical analysis regarding the presence of anisokaryosis to compare scMCTs versus cMCTs. The completeness of removal was described with the deep and horizontal margins taken into assessment. Margins were categorized as 'incomplete' if neoplastic cells extended to the surgeon-cut edge of the tissue in at least one plane of section. When information on lymph node metastasis was available, it was reported as either ‘present’ or ‘absent’ or classified as HN0 (non-metastatic), HN1 (pre-metastatic/suspected metastasis), HN2 (early metastasis), and HN3 (overt metastasis) when classification information was available [ 15 ]. Statistical analysis For categorical variables, subcutaneous MCTs and cMCTs were compared using the Cochrane Mantel Haenszel test with pathologists as stratification factor. The Breslow-Day Test for homogeneity of odds ratios was used to assess whether the comparison differed between the two pathologists. Analysis for numeric variables was based on the fixed effects model with pathologist as block factor. All analyses were performed at a significance level of 5%. Results A total of 1685 histopathology records were reviewed, of which 1596 records in 1596 dogs contained information on tumor origin: 305/1596 (19.1%) scMCTs and 1291/1596 (80.9%) cMCTs. Pathologist A provided 1008 records of which 193/1008 (19.2%) scMCTs and 815/1008 (80.8%) cMCTs. Pathologist B provided a total of 588 records with 112/588 (19.1%) scMCTs and 476/588 (80.9%) cMCTs. Signalment Information regarding the age of dogs was available in 1533/1596 cases with a mean age of 7.63 ± 0.16 (SE) years for dogs diagnosed with scMCT and 7.57 ± 0.08 (SE) years for dogs diagnosed with cMCT (P = 0.7478). Gender was known for 1541/1596 dogs; 829/1541 (53.8%) were female and 712/1541 (46.2%) were male. Whereas female neutered dogs more often had scMCTs than cMCT (94/299, 31.4% versus 301/1241, 24.2%) male intact dogs more often had cMCTs (503/1541, 32.6% versus 84/299, 28.1%) but the difference was only borderline significant (P = 0.0495). A total of 105 different breeds were identified; breed was not mentioned in 103/1596 (6.5%) records. The 18 most common breeds made up 1172/1493 (78.5%) of all breeds and the breed distribution differed significantly between scMCTs and cMCTs (P < 0.0001) (Fig. 1 ). Tumor size, location, and completeness of removal Information on tumor size of fixed tissue was available in 1071/1596 (67.2%) of cases: 220/305 (72.1%) scMCTs and 851/1291 (65.9%) cMCTs. On average, scMCTs were larger (17.87 cm³, [range 32–224.0 cm³]), compared to cMCTs (7.41 cm³, [range 8–266.32 cm³]) (P < 0.0001). Tumor location on the body was known for 1507/1596 (94.4%) MCTs: 290/305 (95.1%) scMCTs and 1217/1291(94.3%) cMCTs and differed significantly (P = 0.0010) (Table 1 ). Information regarding completeness of excision was available in all cases, with scMCTs being more often (130/305; 42.6%) incompletely excised than cMCTs (233/1291; 18%) (P < 0.0001). Table 1 – Tumor location of mast cell tumors in dogs. Tumor location scMCTs cMCTs Total (P = 0.0010) N % N % N % 290 100 1217 100 1507 100 Head and neck 32 11.0 194 15.9 226 15.0 Thoracic region 32 11,0 120 9.9 152 10.1 Extremity 111 38.3 390 32.0 501 33.2 Flank 47 16.2 172 14.1 219 14.5 Back 7 2.4 35 2.9 42 2.8 Mammary gland 20 6.9 51 4.2 71 4.7 Buttock 29 10.0 113 9.3 142 9.4 Perineal and genital region 8 2.8 123 10.1 131 8.7 Tail 4 1.4 19 1.6 23 1.5 Tumor location of canine subcutaneous mast cell tumors (scMCTs), cutaneous mast cell tumors (cMCTs) and of the total group of mast cell tumors, displayed in absolute number (N) and relative percentage (%). A significant difference in tumor location on the body was observed between scMCTs and cMCTs (P = 0.0010). Histopathological features Information on MC was available in all but 2 cases (303/305 scMCTs and 1291/1291 cMCTs) and was not different between scMCTs (1.63 ± 0.24 (SE)) and cMCTs (1.58 ± 0.12 (SE)) (P = 0.8490) (Table 2 ). Granule count did not differ between both tumor types (P = 0.0644). Subcutaneous MCTs more often had bizarre nuclei and multinucleation than cMCTs (36/283; 12.7% versus 12/1555; 7.7%) (P < 0.0001). Subcutaneous MCTs more often had a higher amount of eosinophils compared to cMCTs (89/289; 30.8% versus 218/1269; 17.2%) whereas cMCTs more often had a moderate amount of eosinophils compared to scMCTs (520/1269; 41.0% versus 61/289; 21.1%) (P < 0.0001). Subcutaneous MCTs more often had more than 10% anisokaryosis compared to cMCTs (25/188; 13.3% versus 76/811; 9.4% respectively) (P = 0.0269) (Table 3 ). Table 2 – Mitotic count of mast cell tumors in dogs. Mitotic count scMCTs cMCTs Total (P = 0.8490) N % N % N % 303 100 1291 100 1594 100 0 231 76.2 1031 79.9 1262 79.2 1 12 4.0 55 4.3 67 4.2 2 12 4.0 18 1.4 30 1.9 3 5 1.7 18 1.4 23 1.4 4 5 1.7 19 1.5 24 1.5 5 2 0.7 6 0.5 8 0.5 6 4 1.3 12 0.9 16 1.0 7 7 2.3 10 0.8 17 1.1 8 2 0.7 16 1.2 18 1.1 9 5 1.7 15 1.2 20 1.3 10 1 0.3 12 0.9 13 0.8 11 4 1.3 15 1.2 19 1.2 12 3 1.0 15 1.2 18 1.1 13 2 0.7 5 0.4 7 0.4 14 1 0.3 3 0.2 4 0.3 15 0 0.0 7 0.5 7 0.4 16 1 0.3 3 0.2 4 0.3 17 0 0.0 1 0.1 1 0.1 18 0 0.0 0 0.0 0 0.0 19 0 0.0 8 0.6 8 0.5 20 0 0.0 1 0.1 1 0.1 20+ 6 2.0 21 1.6 27 1.7 Mitotic count of canine subcutaneous mast cell tumors (scMCTs), cutaneous mast cell tumors (cMCTs) and of the total group of mast cell tumors, displayed in absolute number (N) and relative percentage (%). No significant difference in mitotic count was observed between scMCTs and cMCTs (P = 0.8490). Table 3 – Histopathological features of mast cell tumors in dogs. Variables ScMCTs cMCTs Total P-value N % N % N % Bizarre nuclei and multinucleation 283 100 1272 100 1555 100 < 0.0001 None or 1/10 HPF 247 87.3 1188 93.4 1435 92.3 Yes, less than 3/10 HPF 0 0.0 10 0.8 10 0.6 Yes, more than 3/10 HPF 1 0.4 60 4.7 61 3.9 Yes, unspecified 35 12.4 14 1.1 49 3.2 Eosinophil amount 289 100 1269 100 1558 100 < 0.0001 High 89 30.8 218 17.2 307 19.7 Moderate 61 21.1 520 41.0 581 37.3 Low 139 48.1 531 41.8 670 43.0 Anisokaryosis a 188 100 811 100 999 100 = 0.0269 Less than 5% 163 86.7 735 90.6 898 89.9 Between 5–10% 0 0 0 0 0 0 More than 10% 25 13.3 76 9.4 101 10.1 Histopathological features of canine subcutaneous mast cell tumors (scMCTs), cutaneous mast cell tumors (cMCTs) and of the total group of mast cell tumors, displayed in absolute number (N) and relative percentage (%). Significant differences were observed between scMCTs and cMCTs. a As % of neoplastic cells of the total neoplastic cell population, that exhibits a 2-fold variation in nuclear size (pathologist A). Grading The Kiupel grading system was applied to 292/305 (95.7%) scMCTs, and to all 1291/1291 (100%) cMCTs with scMCTs significantly more often assigned a high grade than cMCTs (51/292; 17.5% versus 154/1291; 11.9%) (P = 0.0095). The odds of being assigned a high grade in scMCTs was 1.578 higher than in cMCTs (95% confidence interval [1.116–2.232]) and was not different between both pathologists (P = 0.623). The Patnaik grading system was only applied to 3/305 (0.9%) scMCTs and to all 1291/1291 (100%) cMCTs. Lymph node metastasis Information on lymph node status was available in 62/1596 (3.8%) reports. Of the lymph nodes that were submitted for histopathological evaluation, 23/62 (37.1%) were from scMCTs and 39/62 (62.9%) from cMCTs. Metastasis was absent in 7/23 (30.4%) of scMCTs and 20/39 (51.3%) of cMCTs (P = 0.1097) (Table 4 ). Table 4 –Lymph node classification of mast cell tumors in dogs. Metastasis scMCTs cMCTs Total (P = 0.1097) N % N % N % 23 100 39 100 62 100 HN1 2 8.7 1 2,6 3 4.8 HN2 1 4.3 1 2,6 2 3.2 HN3 3 13.0 4 10,3 7 11.3 Yes, but unspecified 10 43.5 13 33,3 23 37.1 Absent 7 30.4 20 51,3 27 43.5 Lymph node classification of canine subcutaneous mast cell tumors (scMCTs), cutaneous mast cell tumors (cMCTs) and of the total group of mast cell tumors, displayed in absolute number (N) and relative percentage (%), including classification according to Weishaar [ 15 ] when this was mentioned in the histopathology report. No significant differences were observed between scMCTs and cMCTs regarding metastasis (P = 0.1097). Discussion Our study provides insights regarding the signalment and histopathological features of MCTs of the skin across a large canine population, and of all examined MCTs, nearly one fifth was of subcutaneous origin. The results of our retrospective study confirm that, based on their histopathological features, the origin of the MCT, being subcutaneous or cutaneous, does matter. Differences between both tumor types were observed and when scMCTs were graded according to the Kiupel grading system, they were more often assigned a Kiupel high-grade than cMCTs. Boxer, French Bulldog, Weimaraner, Labrador Retriever, and Golden Retriever are well-known predisposed breeds for developing MCTs [ 16 – 17 ]; these breeds were also among the 18 most prevalent breeds in our study. Breed distribution, however, was different for both tumor types when the 18 most prevalent breeds were compared. Subcutaneous MCTs were more often diagnosed in the Labrador Retriever, Maltese, Beagle, Bernese Mountain Dog, Boxer, and Nova Scotia Duck Tolling Retriever whereas the French Bulldog, Golden Retriever and American Staffordshire Terrier more often had cMCTs. Our study is the first to offer insights into the occurrence and breed distribution of scMCTs. In veterinary oncology, traditional histopathology still plays a major role. It does not only provide information about the type of tumor and the adequacy of excision, but it also assists in grading. Cell morphology, nuclear morphology, anisokaryosis, architecture, cellularity, stromal reaction, location, mitotic figures, edema, and necrosis are histopathological features evaluated in the Patnaik and/or Kiupel grading system [ 3 , 6 ]. Mitotic count, bizarre nuclei and multinucleation, number of granules, number of eosinophils and anisokaryosis were histopathological features assessed in our retrospective study. Mitotic count and number of granules were not different between both tumor types whereas bizarre nuclei and multinucleation, number of eosinophils, and anisokaryosis were significantly different. The original Patnaik and Kiupel grading systems were specifically developed for cMCTs [ 3 ], [ 6 ] and both were not validated for grading scMCTs. In scMCT, the decision to whether or not apply the cMCT grading systems was left to the discretion of the pathologist; many of them would attribute a grade to scMCT and the majority would grade according to Kiupel. The 2 pathologists evaluating the scMCTs in this study graded all but 13 scMCTs according to Kiupel and only 3 according to Patnaik. If the Patnaik system is used to grade scMCTs, they would often be assigned grade II or higher because grade I tumors are confined to dermis and interfollicular spaces [ 3 ]. Unfortunately, the recent consensus that emphasizes the importance of reporting both Patnaik and Kiupel grade, was specifically proposed for cMCTs [ 18 ]. The original study validating the Kiupel 2-tier grading system in MCTs refers to an older study of the same research group that investigated the significance of tumor depth and tumor location for prognostic evaluation of cMCTs [ 6 ], [ 19 ]. In that study, however, scMCTs were considered as cMCTs isolated in the subcutis, thus being a subgroup of cMCT, and not as a distinct entity, as they are nowadays [ 8 ]. It is therefore not clear whether only true cMCTs were incorporated in the 95 cases on which the Kiupel grading system was established [ 6 ]. The question arises whether it would be interesting to reevaluate the 2-tier grading system in order to incorporate scMCTs, or to establish an independent grading system for both tumor types separately. After all, a recent consensus proposal emphasized the importance of grading both cMCTs and scMCTs for prognostication [ 8 ]. Another recent consensus regarding grading of MCTs solely focused on cMCTs [ 18 ]. Fortunately, a very recent study examined the prognostic utility of the Kiupel histologic grading system in 91 MCTs of the skin with different 6 growth model categories and explored the prognostic impact with emphasis on the growth model itself [ 11 ]. The authors demonstrated that the Kiupel grade had indeed a relevant prognostic value and that the Kiupel system could accurately identify any type of MCT with aggressive biologic behavior, including scMCTs [ 11 ]. In our study, both pathologists categorized a significantly higher percentage of scMCTs as Kiupel high-grade compared to cMCTs (17.4% versus 11.9%). This grading system solely relies on cell morphology, in contrast to the Patnaik system which also considers growth pattern and infiltration in surrounding tissues [ 3 , 6 ]. Mast cell tumors are assigned a Kiupel high-grade when any of the following criteria is present: ≥7 mitoses/10 HPFs, ≥ 3 multinucleated cells/10 HPFs, ≥ 3 bizarre nuclei/10 HPFs, and karyomegaly [ 6 ]. The results of our study show that around 10% of all MCTs exhibited a MC ≥ 7 (10.6% of scMCTs and 10.2% of cMCTs, respectively) with no difference between both types of tumors. Subcutaneous MCTs more often had anisokaryosis, and bizarre nuclei and multinucleation than cMCTs; unfortunately, the specific number /10 HPF was not always specified. However, the fact that anisokaryosis and bizarre nuclei and multinucleation were more often observed in scMCTs, may have contributed to the fact that scMCTs were more often assigned a Kiupel high grade in our study. Whether these histopathological features also correlated with a more aggressive behavior and worse clinical outcome could not be evaluated since information regarding clinical outcome was lacking. It is well known that MCTs can metastasize to lymph nodes [ 20 ]. Unfortunately, in our study only 3.9% of all MCTs had lymph nodes extirpated, and although there was no difference in metastatic rate between scMCTs and cMCTs, this number might be insufficient as a representative sample to draw definitive conclusions. Moreover, in the dataset it was not mentioned whether the excised lymph node was the locoregional (LRN) or sentinel lymph node (SLN). It emphasizes however the urgency for veterinarians to adopt a systematic approach to resecting LRN, and ideally, the SLN given the improved outcome when performing lymphadenectomy in dogs with biologically aggressive cMCTs [ 21 ]. Based on the histopathology reports, scMCTs were more often incompletely excised than cMCTs. This could be attributed to several factors. Firstly, the subcutaneous location could have made it harder to accurately determine surgical margins. Secondly, achieving deep margins during excision might have been more challenging with scMCTs. Moreover, surgeons might have based their surgical margins on previous literature with scMCTs exhibiting a more favorable prognosis. Finally, their bigger size might have risen concerns about subsequent closure, potentially prompting surgeons to opt for smaller margins during excision. The question arises whether incomplete excision had implications regarding the disease-free interval. This study has several limitations. The retrospective nature of our study resulted in challenges related to standardization of histopathological data (bizarre nuclei and multinucleation, granule count, eosinophil count, anisokaryosis, metastasis and immunohistochemistry). Quantification using a range of values, and standardization would reduce observational bias in future studies. Functional outcome data on recurrence, disease-free interval, and survival time lacked, limiting the ability to establish direct correlations between histopathological findings and clinical prognosis. Obviously, such data are needed in a sufficient number of cases before the prognostic value of histopathological grading could be assessed. Conclusions Subcutaneous MCTs represented a notable portion of the examined mast cell population, rendering them potentially more prevalent than previously recognized. They exhibited distinct histopathological characteristics including more anisokaryosis, bizarre nuclei, and multinucleation compared to cMCTs. In line, scMCTs were more frequently assigned a high grade than cMCTs when they were graded according to the Kiupel grading system. However, further studies are needed to determine if these histopathological differences correlate with clinical outcomes. Declarations Authors’ contributions SM initiated and wrote with HdR. RF provided the subject proposal. SB made contributions to the acquisition of a substantial amount of data. LD performed the statistical analyses. SD, SDV and MH revised the manuscript. All authors have read and approved the final version of the manuscript. Acknowledgements The authors would like to thank the team of clinicians and assistants in Anicura Randstad for the support. We also thank Timothy Scase for the substantial amount of data that he provided. Competing interests The authors declare that they have no competing interests. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Consent for publication Not applicable. Ethics approval This study did not require official or institutional ethical approval. Prior publication Data have not been published previously. Funding This study was funded by Anicura Randstad and Ghent University. 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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-4395941","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307175404,"identity":"10bbe71f-9c1f-4e54-b992-75a5fc6ff1e6","order_by":0,"name":"Stella Minnoye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYHACxgMPGBh4IOyKA2AKJIIbsAEVJMC1nDkAZoFECGqBWtgG0cKAT4v8/OYDBxJqtsmYSzcfk/g4746cvdjhh0BD7OR0G7BrMTjGlnAg4dhtHss5x9IkZ257ZswjnWYA1JJsbHYAhxY2HqACtts8BjdyzKR5tx1O7JFOAGk5kLgNhxb5Nv4PBxL+QbX8nQPSkv4BrxaGYzxA2TaoFsYGkJYc/LYYHAO6PLEPpCUt2bLn2GFjnts5BQcSDHD7Rb758MMHH77dtje4kXzwxo+aw3Lss9M3f/hQYSeHSwsyYJFAsp2wchBg/kCculEwCkbBKBhpAAAz3GkeLgPPEAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5535-2618","institution":"AniCura Group","correspondingAuthor":true,"prefix":"","firstName":"Stella","middleName":"","lastName":"Minnoye","suffix":""},{"id":307175405,"identity":"650cb804-2321-42c6-9641-745386b4d3ed","order_by":1,"name":"Shana De Vos","email":"","orcid":"","institution":"Ghent University: Universiteit Gent","correspondingAuthor":false,"prefix":"","firstName":"Shana","middleName":"","lastName":"De Vos","suffix":""},{"id":307175406,"identity":"d2557fe2-4753-4184-8208-bdb0dc411efd","order_by":2,"name":"Samuel Beck","email":"","orcid":"","institution":"Independent Anatomic Pathology","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Beck","suffix":""},{"id":307175407,"identity":"232375cc-7023-4483-958d-c988cadbbe9a","order_by":3,"name":"Luc Duchateau","email":"","orcid":"","institution":"Ghent University: Universiteit Gent","correspondingAuthor":false,"prefix":"","firstName":"Luc","middleName":"","lastName":"Duchateau","suffix":""},{"id":307175408,"identity":"fa9b25a2-c11e-42fc-999e-5fd45f6ed40d","order_by":4,"name":"Mike Hubers","email":"","orcid":"","institution":"AniCura Group","correspondingAuthor":false,"prefix":"","firstName":"Mike","middleName":"","lastName":"Hubers","suffix":""},{"id":307175409,"identity":"f626e2eb-cbe9-400b-9c75-7987f3927aea","order_by":5,"name":"Sieglinde David","email":"","orcid":"","institution":"AniCura Group","correspondingAuthor":false,"prefix":"","firstName":"Sieglinde","middleName":"","lastName":"David","suffix":""},{"id":307175410,"identity":"b2a3bdfd-7823-42a0-9b69-de960c2eed9d","order_by":6,"name":"Ruth Fortrie","email":"","orcid":"","institution":"AniCura Group","correspondingAuthor":false,"prefix":"","firstName":"Ruth","middleName":"","lastName":"Fortrie","suffix":""},{"id":307175411,"identity":"06bcfbc7-b9a6-4b95-b4ff-03a60d28bc92","order_by":7,"name":"Hilde de Rooster","email":"","orcid":"","institution":"Ghent University: Universiteit Gent","correspondingAuthor":false,"prefix":"","firstName":"Hilde","middleName":"","lastName":"de Rooster","suffix":""}],"badges":[],"createdAt":"2024-05-09 14:57:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4395941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4395941/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13028-024-00775-5","type":"published","date":"2024-10-01T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57939087,"identity":"5821112a-422c-4af8-847b-be24aaa3f222","added_by":"auto","created_at":"2024-06-07 18:09:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1831355,"visible":true,"origin":"","legend":"\u003cp\u003eMost prevalent dog breeds (n=18) with mast cell tumors (n=1172 dogs).\u003c/p\u003e\n\u003cp\u003eLegend: The 18 most prevalent dog breeds in the study that were diagnosed with a mast cell tumor, subdivided in subcutaneous mast cell tumors (scMCTs) (n=221 dogs) versus cutaneous mast cell tumors (cMCTs) (n = 951 dogs).\u003c/p\u003e","description":"","filename":"Figure.png","url":"https://assets-eu.researchsquare.com/files/rs-4395941/v1/60edf28ad843d89c9f876951.png"},{"id":66097041,"identity":"2c2dbd6c-b3ff-493b-9dde-207e971369ce","added_by":"auto","created_at":"2024-10-07 16:12:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1827390,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4395941/v1/4754195c-baad-4e6c-adc6-4e089e5f36ae.pdf"}],"financialInterests":"","formattedTitle":"Signalment of dogs and histopathological features of subcutaneous and cutaneous mast cell tumors","fulltext":[{"header":"Background","content":"\u003cp\u003eMast cell tumors (MCTs) comprise up to one fifth of skin tumors in dogs, rendering them the most common malignant skin neoplasm in this species [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Cutaneous MCTs originate from the dermis and can extent into the underlying subcutis and muscles [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Literature since then adapted the term cMCTs. It took until 2007, when a separate subset of MCTs originating from subcutaneous tissues has first been described [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therapeutic decisions in canine cMCTs are based on the clinical condition of the dog, anatomic location of the tumor, staging, and histopathological differentiation, including grading, with the latter being one of the most crucial prognostic predictors [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A recent consensus proposal regarding diagnostic criteria and classification of MCTs has emphasized the importance of reporting the origin (cutaneous versus subcutaneous) and, for prognostication, to grade both tumor types [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The grading systems that are currently used to grade MCTs (3-tier Patnaik and 2-tier Kiupel) were both designed for grading cMCTs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, since scMCTs were historically regarded as a subcutaneous variant of cMCTs, there are concerns whether some scMCTs were not inadvertently included when those grading systems were developed. In the absence of a grading system for scMCTs, negative prognostic factors that have been used to assess these tumors on histopathology are mitotic count (MC), multinucleation and infiltrative growth pattern [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The decision whether or not to grade scMCTs according to one of the existing grading systems was at the discretion of the pathologist. However, Sabattini and colleagues very recently studied the prognostic value of the Kiupel 2-tier grading in scMCT in dogs and concluded that it enables identification of aggressive biological behavior in scMCT cases, similar to cMCT cases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Earlier, in terms of prognosis, it was believed that the majority of scMCTs exhibited a favorable prognosis compared to cMCTs, with extended survival times and low metastatic rates and recurrence rates (4% and 8%, respectively) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Later studies solely focusing on scMCTs, however, reported that a larger proportion of the scMCT cases might exhibit an aggressive biologic behavior [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our study aimed to describe signalment of dogs and histopathological features of scMCTs and cMCTs across a large dataset of canine MCTs of the skin. Our hypothesis centered on the comparability of histopathological features between scMCTs and cMCTs, anticipating similar characteristics in both groups.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAnonymized pathology databases from 2 board-certified pathologists (A and B) from a single laboratory were screened and included reports of canine tissue samples from primary, secondary, and tertiary veterinary centers. Pathology reports that mentioned \u0026ldquo;skin\u0026rdquo; and \u0026ldquo;mast cells\u0026rdquo; between September 2020 and July 2023 were reviewed. Each report lacking the diagnosis of MCT or specific information on tumor origin (cutaneous versus subcutaneous) were excluded. Reports of dogs with more than one MCT, reports that mentioned incisional biopsy or reports that mentioned mast cells without histopathological diagnosis of MCT were excluded. Data retrieved from the database included information on signalment (breed, gender, and age), tumor dimension (in mm), and tumor origin (cutaneous or subcutaneous). For the purpose of the study, 9 categories for location were established: extremity, flank, perineal and genital region, back, head and neck, mammary gland, thorax, tail region, or buttock area). Histopathological features retrieved from the database were MC, bizarre nuclei and multinucleation, cytoplasmatic granules, eosinophil count, anisokaryosis, completeness of removal, histopathological grade (2-tier Patnaik and 3-tier Kiupel), and potential lymph node involvement. The MC was assessed in areas with the highest mitotic activity and reported as an absolute value, defined as the number of mitotic figures per 10 high-power fields (HPF) (x400, 2.37 mm\u0026sup2;). For the purpose of the study, the presence of bizarre nuclei and multinucleation (in 10 HPF) was categorized in 4 categories (none or one bizarre nuclei/10 HPF, less than 3 bizarre nuclei/10 HPF, 3 or more bizarre nuclei/10 HPF, present but undefined). Presence of cytoplasmic granules had been categorized as a \u0026lsquo;small,\u0026rsquo; \u0026lsquo;moderate,\u0026rsquo; or \u0026lsquo;large\u0026rsquo; number by each pathologist. Similarly, the number of eosinophils was categorized as \u0026lsquo;low,\u0026rsquo; \u0026lsquo;moderate,\u0026rsquo; or \u0026lsquo;high\u0026rsquo;. Pathologist A reported the presence and degree of anisokaryosis as a % of neoplastic cells of the total neoplastic population, that exhibits a 2-fold variation in nuclear size. Pathologist B reported the presence and degree of anisokaryosis as none/mild/moderate or marked. Only data of pathologist A were used for statistical analysis regarding the presence of anisokaryosis to compare scMCTs versus cMCTs. The completeness of removal was described with the deep and horizontal margins taken into assessment. Margins were categorized as 'incomplete' if neoplastic cells extended to the surgeon-cut edge of the tissue in at least one plane of section. When information on lymph node metastasis was available, it was reported as either \u0026lsquo;present\u0026rsquo; or \u0026lsquo;absent\u0026rsquo; or classified as HN0 (non-metastatic), HN1 (pre-metastatic/suspected metastasis), HN2 (early metastasis), and HN3 (overt metastasis) when classification information was available [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor categorical variables, subcutaneous MCTs and cMCTs were compared using the Cochrane Mantel Haenszel test with pathologists as stratification factor. The Breslow-Day Test for homogeneity of odds ratios was used to assess whether the comparison differed between the two pathologists. Analysis for numeric variables was based on the fixed effects model with pathologist as block factor. All analyses were performed at a significance level of 5%.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1685 histopathology records were reviewed, of which 1596 records in 1596 dogs contained information on tumor origin: 305/1596 (19.1%) scMCTs and 1291/1596 (80.9%) cMCTs. Pathologist A provided 1008 records of which 193/1008 (19.2%) scMCTs and 815/1008 (80.8%) cMCTs. Pathologist B provided a total of 588 records with 112/588 (19.1%) scMCTs and 476/588 (80.9%) cMCTs.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSignalment\u003c/h2\u003e \u003cp\u003eInformation regarding the age of dogs was available in 1533/1596 cases with a mean age of 7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 (SE) years for dogs diagnosed with scMCT and 7.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 (SE) years for dogs diagnosed with cMCT (P\u0026thinsp;=\u0026thinsp;0.7478). Gender was known for 1541/1596 dogs; 829/1541 (53.8%) were female and 712/1541 (46.2%) were male. Whereas female neutered dogs more often had scMCTs than cMCT (94/299, 31.4% versus 301/1241, 24.2%) male intact dogs more often had cMCTs (503/1541, 32.6% versus 84/299, 28.1%) but the difference was only borderline significant (P\u0026thinsp;=\u0026thinsp;0.0495). A total of 105 different breeds were identified; breed was not mentioned in 103/1596 (6.5%) records. The 18 most common breeds made up 1172/1493 (78.5%) of all breeds and the breed distribution differed significantly between scMCTs and cMCTs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTumor size, location, and completeness of removal\u003c/h2\u003e \u003cp\u003eInformation on tumor size of fixed tissue was available in 1071/1596 (67.2%) of cases: 220/305 (72.1%) scMCTs and 851/1291 (65.9%) cMCTs. On average, scMCTs were larger (17.87 cm\u0026sup3;, [range 32\u0026ndash;224.0 cm\u0026sup3;]), compared to cMCTs (7.41 cm\u0026sup3;, [range 8\u0026ndash;266.32 cm\u0026sup3;]) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Tumor location on the body was known for 1507/1596 (94.4%) MCTs: 290/305 (95.1%) scMCTs and 1217/1291(94.3%) cMCTs and differed significantly (P\u0026thinsp;=\u0026thinsp;0.0010) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Information regarding completeness of excision was available in all cases, with scMCTs being more often (130/305; 42.6%) incompletely excised than cMCTs (233/1291; 18%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Tumor location of mast cell tumors in dogs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003escMCTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ecMCTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(P\u0026thinsp;=\u0026thinsp;0.0010)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1217\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1507\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead and neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThoracic region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMammary gland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButtock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerineal and genital region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eTumor location of canine subcutaneous mast cell tumors (scMCTs), cutaneous mast cell tumors (cMCTs) and of the total group of mast cell tumors, displayed in absolute number (N) and relative percentage (%). A significant difference in tumor location on the body was observed between scMCTs and cMCTs (P\u0026thinsp;=\u0026thinsp;0.0010).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHistopathological features\u003c/h2\u003e \u003cp\u003eInformation on MC was available in all but 2 cases (303/305 scMCTs and 1291/1291 cMCTs) and was not different between scMCTs (1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 (SE)) and cMCTs (1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 (SE)) (P\u0026thinsp;=\u0026thinsp;0.8490) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Granule count did not differ between both tumor types (P\u0026thinsp;=\u0026thinsp;0.0644). Subcutaneous MCTs more often had bizarre nuclei and multinucleation than cMCTs (36/283; 12.7% versus 12/1555; 7.7%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Subcutaneous MCTs more often had a higher amount of eosinophils compared to cMCTs (89/289; 30.8% versus 218/1269; 17.2%) whereas cMCTs more often had a moderate amount of eosinophils compared to scMCTs (520/1269; 41.0% versus 61/289; 21.1%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Subcutaneous MCTs more often had more than 10% anisokaryosis compared to cMCTs (25/188; 13.3% versus 76/811; 9.4% respectively) (P\u0026thinsp;=\u0026thinsp;0.0269) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Mitotic count of mast cell tumors in dogs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitotic count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003escMCTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ecMCTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(P\u0026thinsp;=\u0026thinsp;0.8490)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1291\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1594\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMitotic count of canine subcutaneous mast cell tumors (scMCTs), cutaneous mast cell tumors (cMCTs) and of the total group of mast cell tumors, displayed in absolute number (N) and relative percentage (%). No significant difference in mitotic count was observed between scMCTs and cMCTs (P\u0026thinsp;=\u0026thinsp;0.8490).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Histopathological features of mast cell tumors in dogs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eScMCTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ecMCTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBizarre nuclei and multinucleation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1272\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1555\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone or 1/10 HPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, less than 3/10 HPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, more than 3/10 HPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, unspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEosinophil amount\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e289\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1269\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1558\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnisokaryosis\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e188\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e811\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e=\u0026thinsp;\u003cb\u003e0.0269\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween 5\u0026ndash;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eHistopathological features of canine subcutaneous mast cell tumors (scMCTs), cutaneous mast cell tumors (cMCTs) and of the total group of mast cell tumors, displayed in absolute number (N) and relative percentage (%). Significant differences were observed between scMCTs and cMCTs.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eAs % of neoplastic cells of the total neoplastic cell population, that exhibits a 2-fold variation in nuclear size (pathologist A).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGrading\u003c/h2\u003e \u003cp\u003eThe Kiupel grading system was applied to 292/305 (95.7%) scMCTs, and to all 1291/1291 (100%) cMCTs with scMCTs significantly more often assigned a high grade than cMCTs (51/292; 17.5% versus 154/1291; 11.9%) (P\u0026thinsp;=\u0026thinsp;0.0095). The odds of being assigned a high grade in scMCTs was 1.578 higher than in cMCTs (95% confidence interval [1.116\u0026ndash;2.232]) and was not different between both pathologists (P\u0026thinsp;=\u0026thinsp;0.623). The Patnaik grading system was only applied to 3/305 (0.9%) scMCTs and to all 1291/1291 (100%) cMCTs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLymph node metastasis\u003c/h2\u003e \u003cp\u003eInformation on lymph node status was available in 62/1596 (3.8%) reports. Of the lymph nodes that were submitted for histopathological evaluation, 23/62 (37.1%) were from scMCTs and 39/62 (62.9%) from cMCTs. Metastasis was absent in 7/23 (30.4%) of scMCTs and 20/39 (51.3%) of cMCTs (P\u0026thinsp;=\u0026thinsp;0.1097) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash;Lymph node classification of mast cell tumors in dogs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003escMCTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ecMCTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(P\u0026thinsp;=\u0026thinsp;0.1097)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, but unspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eLymph node classification of canine subcutaneous mast cell tumors (scMCTs), cutaneous mast cell tumors (cMCTs) and of the total group of mast cell tumors, displayed in absolute number (N) and relative percentage (%), including classification according to Weishaar [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] when this was mentioned in the histopathology report. No significant differences were observed between scMCTs and cMCTs regarding metastasis (P\u0026thinsp;=\u0026thinsp;0.1097).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides insights regarding the signalment and histopathological features of MCTs of the skin across a large canine population, and of all examined MCTs, nearly one fifth was of subcutaneous origin. The results of our retrospective study confirm that, based on their histopathological features, the origin of the MCT, being subcutaneous or cutaneous, does matter. Differences between both tumor types were observed and when scMCTs were graded according to the Kiupel grading system, they were more often assigned a Kiupel high-grade than cMCTs.\u003c/p\u003e \u003cp\u003eBoxer, French Bulldog, Weimaraner, Labrador Retriever, and Golden Retriever are well-known predisposed breeds for developing MCTs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; these breeds were also among the 18 most prevalent breeds in our study. Breed distribution, however, was different for both tumor types when the 18 most prevalent breeds were compared. Subcutaneous MCTs were more often diagnosed in the Labrador Retriever, Maltese, Beagle, Bernese Mountain Dog, Boxer, and Nova Scotia Duck Tolling Retriever whereas the French Bulldog, Golden Retriever and American Staffordshire Terrier more often had cMCTs. Our study is the first to offer insights into the occurrence and breed distribution of scMCTs.\u003c/p\u003e \u003cp\u003eIn veterinary oncology, traditional histopathology still plays a major role. It does not only provide information about the type of tumor and the adequacy of excision, but it also assists in grading. Cell morphology, nuclear morphology, anisokaryosis, architecture, cellularity, stromal reaction, location, mitotic figures, edema, and necrosis are histopathological features evaluated in the Patnaik and/or Kiupel grading system [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Mitotic count, bizarre nuclei and multinucleation, number of granules, number of eosinophils and anisokaryosis were histopathological features assessed in our retrospective study. Mitotic count and number of granules were not different between both tumor types whereas bizarre nuclei and multinucleation, number of eosinophils, and anisokaryosis were significantly different. The original Patnaik and Kiupel grading systems were specifically developed for cMCTs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and both were not validated for grading scMCTs. In scMCT, the decision to whether or not apply the cMCT grading systems was left to the discretion of the pathologist; many of them would attribute a grade to scMCT and the majority would grade according to Kiupel. The 2 pathologists evaluating the scMCTs in this study graded all but 13 scMCTs according to Kiupel and only 3 according to Patnaik. If the Patnaik system is used to grade scMCTs, they would often be assigned grade II or higher because grade I tumors are confined to dermis and interfollicular spaces [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Unfortunately, the recent consensus that emphasizes the importance of reporting both Patnaik and Kiupel grade, was specifically proposed for cMCTs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe original study validating the Kiupel 2-tier grading system in MCTs refers to an older study of the same research group that investigated the significance of tumor depth and tumor location for prognostic evaluation of cMCTs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In that study, however, scMCTs were considered as cMCTs isolated in the subcutis, thus being a subgroup of cMCT, and not as a distinct entity, as they are nowadays [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is therefore not clear whether only true cMCTs were incorporated in the 95 cases on which the Kiupel grading system was established [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The question arises whether it would be interesting to reevaluate the 2-tier grading system in order to incorporate scMCTs, or to establish an independent grading system for both tumor types separately. After all, a recent consensus proposal emphasized the importance of grading both cMCTs and scMCTs for prognostication [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Another recent consensus regarding grading of MCTs solely focused on cMCTs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Fortunately, a very recent study examined the prognostic utility of the Kiupel histologic grading system in 91 MCTs of the skin with different 6 growth model categories and explored the prognostic impact with emphasis on the growth model itself [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The authors demonstrated that the Kiupel grade had indeed a relevant prognostic value and that the Kiupel system could accurately identify any type of MCT with aggressive biologic behavior, including scMCTs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In our study, both pathologists categorized a significantly higher percentage of scMCTs as Kiupel high-grade compared to cMCTs (17.4% versus 11.9%). This grading system solely relies on cell morphology, in contrast to the Patnaik system which also considers growth pattern and infiltration in surrounding tissues [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Mast cell tumors are assigned a Kiupel high-grade when any of the following criteria is present: \u0026ge;7 mitoses/10 HPFs, \u0026ge;\u0026thinsp;3 multinucleated cells/10 HPFs, \u0026ge;\u0026thinsp;3 bizarre nuclei/10 HPFs, and karyomegaly [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The results of our study show that around 10% of all MCTs exhibited a MC\u0026thinsp;\u0026ge;\u0026thinsp;7 (10.6% of scMCTs and 10.2% of cMCTs, respectively) with no difference between both types of tumors. Subcutaneous MCTs more often had anisokaryosis, and bizarre nuclei and multinucleation than cMCTs; unfortunately, the specific number /10 HPF was not always specified. However, the fact that anisokaryosis and bizarre nuclei and multinucleation were more often observed in scMCTs, may have contributed to the fact that scMCTs were more often assigned a Kiupel high grade in our study. Whether these histopathological features also correlated with a more aggressive behavior and worse clinical outcome could not be evaluated since information regarding clinical outcome was lacking.\u003c/p\u003e \u003cp\u003eIt is well known that MCTs can metastasize to lymph nodes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Unfortunately, in our study only 3.9% of all MCTs had lymph nodes extirpated, and although there was no difference in metastatic rate between scMCTs and cMCTs, this number might be insufficient as a representative sample to draw definitive conclusions. Moreover, in the dataset it was not mentioned whether the excised lymph node was the locoregional (LRN) or sentinel lymph node (SLN). It emphasizes however the urgency for veterinarians to adopt a systematic approach to resecting LRN, and ideally, the SLN given the improved outcome when performing lymphadenectomy in dogs with biologically aggressive cMCTs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the histopathology reports, scMCTs were more often incompletely excised than cMCTs. This could be attributed to several factors. Firstly, the subcutaneous location could have made it harder to accurately determine surgical margins. Secondly, achieving deep margins during excision might have been more challenging with scMCTs. Moreover, surgeons might have based their surgical margins on previous literature with scMCTs exhibiting a more favorable prognosis. Finally, their bigger size might have risen concerns about subsequent closure, potentially prompting surgeons to opt for smaller margins during excision. The question arises whether incomplete excision had implications regarding the disease-free interval.\u003c/p\u003e \u003cp\u003eThis study has several limitations. The retrospective nature of our study resulted in challenges related to standardization of histopathological data (bizarre nuclei and multinucleation, granule count, eosinophil count, anisokaryosis, metastasis and immunohistochemistry). Quantification using a range of values, and standardization would reduce observational bias in future studies. Functional outcome data on recurrence, disease-free interval, and survival time lacked, limiting the ability to establish direct correlations between histopathological findings and clinical prognosis. Obviously, such data are needed in a sufficient number of cases before the prognostic value of histopathological grading could be assessed.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSubcutaneous MCTs represented a notable portion of the examined mast cell population, rendering them potentially more prevalent than previously recognized. They exhibited distinct histopathological characteristics including more anisokaryosis, bizarre nuclei, and multinucleation compared to cMCTs. In line, scMCTs were more frequently assigned a high grade than cMCTs when they were graded according to the Kiupel grading system. However, further studies are needed to determine if these histopathological differences correlate with clinical outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eSM initiated and wrote with HdR. RF provided the subject proposal. SB made contributions to the acquisition of a substantial amount of data. LD performed the statistical analyses. SD, SDV and MH revised the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe authors would like to thank the team of clinicians and assistants in Anicura Randstad for the support. We also thank Timothy Scase for the substantial amount of data that he provided.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eEthics approval\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThis study did not require official or institutional ethical approval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrior publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData have not been published previously.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Anicura Randstad and Ghent University. Open access funding provided by Anicura Belgium.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGarrett L. Canine mast cell tumors: diagnosis, treatment, and prognosis. Vet Med Res. 2014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/vmrr.s41005\u003c/span\u003e\u003cspan address=\"10.2147/vmrr.s41005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacy DW. Canine mast cell tumors. Vet Clin North Am Small Anim Pract. 1985;15:783\u0026ndash;803.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatnaik AK, Ehler WJ, MacEwen EG. Canine cutaneous mast cell tumor: morphologic grading and survival time in 83 dogs. Vet Pathol. 1984;21;469\u0026thinsp;\u0026ndash;\u0026thinsp;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman SJ, Mrkonjich L, Walker KK, Rohrbach BW. Canine subcutaneous mast cell tumour: diagnosis and prognosis. J Comp Pathol. 2007;136:231\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBostock DE. Neoplasms of the skin and subcutaneous tissues in dogs and cats. Br Vet J. 1986;142:1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiupel M, Webster J, Bailey K, Best S, Delay J, Detrisac C, et al. Proposal of a 2-tier histologic grading system for canine cutaneous mast cell tumors to more accurately predict biological behavior. Vet Pathol. 2011;48:147\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson JJ, Pearl DL, Yager JA, Best SJ, Coomber BL, Foster RA. Canine subcutaneous mast cell tumor: characterization and prognostic indices. Vet Pathol. 2011;48:156\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillmann M, Yuzbasiyan-Gurkan V, Marconato L, Dacasto M, Hadzijusufovic E, Hermine O et al. Proposed diagnostic criteria and classification of canine mast cell neoplasms: a consensus proposal. Front Vet Sci. 2021;8;755258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson JJ, Yager J, Best S, Pearl D, Coomber B, Torres R, et al. Canine subcutaneous mast cell tumors: cellular proliferation and kit expression as prognostic indices. Vet Pathol. 2011;48:169\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTreggiari E, Valenti P, Porcellato I, Maresca G, Romanelli G. Retrospective analysis of outcome and prognostic factors of subcutaneous mast cell tumours in dogs undergoing surgery with or without adjuvant treatment. Vet Comp Oncol. 2023;21:437\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabattini S, Brocanelli A, Zaccone R, Faroni E, Renzi A, Ciammaichella L, et al. The 2-tier grading system identifies canine cutaneous and/or subcutaneous mast cell tumors with aggressive biological behavior regardless of growth model. Vet Pathol. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/03009858241240443\u003c/span\u003e\u003cspan address=\"10.1177/03009858241240443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCherzan NL, Fryer K, Burke B, Farrelly J. Factors affecting prognosis in canine subcutaneous mast cell tumors: 45 cases. Vet Surg. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/vsu.13944\u003c/span\u003e\u003cspan address=\"10.1111/vsu.13944\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGill V, Leibman N, Monette S, Craft DM, Bergman PJ. Prognostic indicators and clinical outcome in dogs with subcutaneous mast cell tumors treated with surgery alone: 43 Cases. J Am Anim Hosp Assoc. 2020;56:215\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarconato L, Stefanello D, Solari Basano F, Faroni E, Dacasto M, Giantin M et al. Subcutaneous mast cell tumours: A prospective multi-institutional clinicopathological and prognostic study of 43 dogs. Vet Rec. 2023;193.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeishaar KM, Thamm DH, Worley DR, Kamstock DA. Correlation of nodal mast cells with clinical outcome in dogs with mast cell tumour and a proposed classification system for the evaluation of node metastasis. J Comp Pathol. 2014;151:329\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierini A, Lubas, Gori E, Binanti D, Millanta F, Marchetti V. Epidemiology of breed-related mast cell tumour occurrence and prognostic significance of clinical features in a defined population of dogs in west-central Italy. Vet Sci. 2019;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShoop SJ, Church D, English K, McGreevy P, Stell A, Thomson P et al. Prevalence and risk factors for mast cell tumours in dogs in England. Canine Genet Epidemiol. 2015;2;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerlato D, Bulman-Fleming J, Clifford C, Garrett L, Intile J, Jones P, et al. Value, limitations, and recommendations for grading of canine cutaneous mast cell tumors: a consensus of the oncology-pathology working group. Vet Pathol. 2021;58:858\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiupel M, Webster JD, Miller RA, Kaneene JB. Impact of tumour depth, tumour location and multiple synchronous masses on the prognosis of canine cutaneous mast cell tumours. J Vet Med J. 2005;52:280\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlackwood L, Murphy S, Buracco P, De Vos JP, De Fornel-Thibaud P, Hirschberger J, et al. European consensus document on mast cell tumors in dogs and cats. Vet Comp Oncol. 2012;10:1\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalfon C, Sabattini S, Finotello R, Faroni E, Guerra D, Pisoni L, et al. Lymphadenectomy improves outcome in dogs with resected Kiupel high-grade cutaneous mast cell tumours and overtly metastatic regional lymph nodes. J Small Anim Pract. 2022;63:661\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Canine, grading system, Kiupel, neoplasm, Patnaik","lastPublishedDoi":"10.21203/rs.3.rs-4395941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4395941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMast cell tumors (MCTs) are the most common malignant skin neoplasms in dogs. In the past, the distinction between cutaneous MCTs (cMCTs), originating from the dermis, and subcutaneous MCTs (scMCTs), originating from the subcutaneous tissue, was not made. Histopathological differentiation, including grading, is important for prognostication. However, the Patnaik and Kiupel grading systems were proposed for cMCTs only. The objective of our study was to describe and compare the signalment of dogs with scMCTs and cMCTs and histopathological features, anticipating similarities in both groups. Data of dogs histologically diagnosed with scMCTs or cMCTs between September 2020 and July 2023 were retrospectively analyzed. Signalment, tumor location, histopathological features, completeness of removal and lymph node status were recorded.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eData on 305 scMCTs and 1291 cMCTs were collected. Mitotic count (MC) was not different between scMCTs (1.63) and cMCTs (1.58) (P\u0026thinsp;=\u0026thinsp;0.8490). Compared to cMCTs, scMCTs more often had anisokaryosis, bizarre nuclei and multinucleation. Kiupel high grade was more often assigned to scMCTs (51/292, 17.5%) than cMCTs (154/1291, 11.9%) (P\u0026thinsp;=\u0026thinsp;0.009). The odds of MCTs being assigned a high grade in scMCT was 1.578 higher than in cMCTs (95% confidence interval [1.116\u0026ndash;2.232]).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBreed distribution was different for scMCTs and cMCTs. Histopathological differences between scMCTs and cMCTs were observed. A Kiupel high grade was more often assigned to scMCTs than cMCTs. Whether these histopathological findings correlate with clinical outcome has to be established in additional studies.\u003c/p\u003e","manuscriptTitle":"Signalment of dogs and histopathological features of subcutaneous and cutaneous mast cell tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 18:09:44","doi":"10.21203/rs.3.rs-4395941/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ecf0e0da-50ef-4e19-b0f0-893c23af40d8","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-07T16:06:18+00:00","versionOfRecord":{"articleIdentity":"rs-4395941","link":"https://doi.org/10.1186/s13028-024-00775-5","journal":{"identity":"acta-veterinaria-scandinavica","isVorOnly":false,"title":"Acta Veterinaria Scandinavica"},"publishedOn":"2024-10-01 15:58:27","publishedOnDateReadable":"October 1st, 2024"},"versionCreatedAt":"2024-06-07 18:09:44","video":"","vorDoi":"10.1186/s13028-024-00775-5","vorDoiUrl":"https://doi.org/10.1186/s13028-024-00775-5","workflowStages":[]},"version":"v1","identity":"rs-4395941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4395941","identity":"rs-4395941","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}