Epidemiological Investigation of Canine Mammary Tumors in Mainland China Between 2017 and 2021

Abstract

Epidemiological studies enable us to analyze disease behavior, define risk factors, and establish fundamental prognostic criteria. This study aimed to determine the epidemiological and clinical characteristics of canine tumors diagnosed during the years 2017-2021. The results showed that canine mammary tumors were the most common tumors, and their relative incidence for 5-years-total was 46.71% (504/1,079), with 48.41% (244/504) of benign, and 51.59% (260/504) of malignant. Pure breeds accounted for 84.13% (424/504) of submissions, and adult female dogs (9-12 years old) were most frequently involved, followed by 5-8-year-old females. Remarkably, 2.58% (13/504) occurred in the male dogs. In addition, a high prevalence of mammary tumors (77.38%, 390/504) was diagnosed in unneutered dogs, and different incidence rates were observed in different regions (Northeast, Southeast, Northwest and Southwest China). For clinical factors, the tumor size ranged from 0.5 to 28 cm, with the 0-5 cm being the most common tumor size (47.82%, 241/504), and malignant tumors (4.33 ± 2.88 cm, mean ± SD) were bigger than benign ones (3.06 ± 1.67 cm, mean ± SD) (p < 0.001). The incidence of single tumor (55.36%, 279/504) was higher than that of multiple tumors in dogs, while the latter had a higher incidence of malignant tumors (74.67%, 168/225). According to this study, we also found that canine mammary tumors were more common in the last two pairs of mammary glands. In addition, multiple linear regression analysis showed that there was linear significant relationship between three independent variables (age, tumor size, and tumor number) and histological properties of canine mammary tumor [(p>|t|) < 0.05]. This is the first retrospective statistical analysis of such a large dataset in China to reveal the link between epidemiological clinical risks and histological diagnosis. It aids in the improvement of the host's knowledge of canine tumor disorders and the early prevention of canine mammary tumors.

Keywords: benign; canine mammary tumors; clinical; epidemiology; malignant.

Figure 1

Regional distribution in China. The mainland China is consists of four parts: Northeast China, Southeast China, Northwest China, and Southwest China.

Figure 2

Clinical appearance of canine mammary tumors. (A) Preoperative appearance of dog: diseased dogs were mainly characterized by localized swelling, hard texture, painless, and itchless of mammary gland and surrounding subcutaneous tissue. (B) Postoperative picture of tumor: the tumor size is variable.

Figure 3

Annual cases and pathological characteristics of canine mammary tumors. (A) The number of dogs suffered from CMT during 2017 to 2021. (B) Relative incidence of canine mammary tumors according to biological behavior at five-years-total. (C) The 5-year-total prevalence of three histological malignant categories.

Figure 4

Statistical analysis of pathogenic factors of canine mammary tumors. Dogs diagnosed with benign or malignant neoplasm. (A) Percentage of dogs showing canine mammary tumors and classified according to their breed group. (B) Age group distribution. (C) Distribution of sterilization status of diseased dogs. (D) Gender group distribution. (E) Distribution of regions of diseased dogs.

Figure 5

Multiple linear regression analysis and machine learning model. F test and t-test were assessed whether these general variables from the whole and itself had the significant impact on the random variable y, respectively, and the value of goodness of fit, R² were between 0 and 1, which was directly proportional to the regression fitting effect. The factors (breed, body shape, age, gender, spayed status, geographical region, tumor location, tumor size, and number) are represented by independent variables ×1, ×2, ×3, ×4, ×5, ×6, ×7, ×8, ×9, respectively. The linear regression equation y = 2.4289+0.210* ×1+0.0093* ×2-0.0805* ×3 + 0.0554* ×4+0.0343* ×5-0.0014* ×6-0.0169* ×7-0.0323* ×8-0.0380* ×9. Note: * represents “coef” of corresponding variables.