Preoperative Clinical Predictors of Histologic Malignancy and Carcinoma Grade in 286 Canine Mammary Nodules from 92 Bitches: A Retrospective Study

TL;DR

This study identifies clinical signs that can predict whether canine mammary nodules are malignant and their aggressiveness before surgery, helping veterinarians make better decisions.

Contribution

The study introduces prediction models using preoperative clinical data to assess malignancy and tumor grade in canine mammary nodules.

Findings

Malignant nodules were more likely to be larger, grow quickly, or have longer detection-to-surgery intervals.

Higher-grade carcinomas were associated with prior mammary tumors, bloody discharge, and fewer synchronous nodules.

Prediction models showed good discrimination for malignancy (AUC 0.805) and tumor grade (AUC 0.859).

Abstract

Canine mammary tumours are common in female dogs, and many have more than one nodule. During the initial consultation, veterinarians must determine which tests are necessary to evaluate whether the disease has spread (staging) and the extent of the required surgery. However, the malignancy of a nodule and its histological aggressiveness can only be determined after its removal and pathological examination. We reviewed the records of 92 client-owned female dogs with no evidence of distant metastasis at the time of presentation, including 286 surgically removed mammary nodules. Using statistical methods that accounted for multiple nodules from the same dog, we found that malignant nodules were more prevalent among those that were larger, had reportedly grown quickly, or for which a longer interval had been observed between initial detection and surgery. However, it should be noted that small nodules can still be malignant. Among mammary carcinomas, higher-grade tumours were more prevalent in dogs with a history of mammary tumours, bloody nipple discharge and fewer nodules detected at the same time. These everyday clinical findings may help clinicians prioritise staging tests, support surgical planning and counsel owners while awaiting the definitive pathology report. However, the prediction models should be confirmed in other hospital populations before they can be used more widely.

Canine mammary tumours often present as multiple synchronous nodules, necessitating decisions regarding staging intensity and surgical planning prior to histology. We developed two preoperative nodule-level prediction models using only the medical history and physical examination of client-owned bitches with mammary disease, which were staged using the WHO-modified TNM system with a M0 classification (no distant metastasis) at the time of presentation. This retrospective study analysed 286 surgically excised mammary nodules from 92 dogs managed under a standardised mammary oncology protocol; those with inflammatory mammary carcinoma or distant metastasis were excluded. The outcomes were (i) malignant versus benign/non-neoplastic histology (for all nodules) and (ii) intermediate/high histologic grade (II–III versus I) among carcinomas. Separate multivariable Firth penalised logistic regression models accounted for within-dog clustering with dog-level bootstrap internal validation. Multiple imputation was used in a sensitivity analysis for missingness in the detection-to-surgery interval. Malignancy was confirmed in 87/286 (30.4%) of the nodules (86 carcinomas), including 35/87 (40.2%) that measured less than 1 cm. Among complete cases (153 nodules), malignancy was associated with age at neutering, maximum tumour diameter, owner-reported rapid growth and a detection-to-surgery interval of more than 3.5 months (an exploratory ROC-derived threshold) with good discrimination (area under the curve (AUC) 0.805; optimism-corrected 0.799) and acceptable calibration. Among carcinomas (83 specimen), previous mammary tumours, bloody nipple discharge and fewer synchronous nodules were associated with intermediate/high malignancy grade (AUC 0.859). Sensitivity analyses yielded directionally consistent estimates. Routinely available clinical information may provide interpretable preoperative risk stratification to support staging and surgical planning, pending external validation.