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Feline Invasive Mammary Carcinomas: Prognostic Value of Histological Grading.

Elie DagherJérôme AbadieDelphine LoussouarnMario CamponeFrédérique Nguyen
Published in: Veterinary pathology (2019)
Feline mammary carcinomas are highly malignant tumors usually associated with poor outcome. Nevertheless, survival times can differ significantly according to various prognostic factors. The Elston and Ellis (EE) histologic grading system, originally developed for human breast cancer, is commonly used to grade feline mammary carcinomas, although it is not really adapted for this species, hence the need of a more relevant grading system. Although few veterinary studies attempted to validate previously published results in an independent cohort, the aim of our study was to evaluate the prognostic value of different histologic grading systems in feline invasive mammary carcinomas, including the EE grading system applicable to human breast cancers and the modified and newly designed histologic grading systems recently proposed by Mills et al. Survey data and histologic features of 342 feline invasive mammary carcinomas were analyzed with respect to overall and cancer-specific survival. The histological grading system with best prognostic value was the mitotic-modified Elston and Ellis (MMEE) grading system: grade III carcinomas (P = .04, hazard ratio [HR] = 1.46, 95% CI, 1.01-2.11), grade II (P = .03, HR = 1.39, 95% CI, 1.03-1.88), and grade I carcinomas (HR = 1.00, reference), with decreasing hazard ratios significantly were associated with a worse overall survival, independently from the pathologic tumor size (pT ≥ 20 mm: P = .002, HR = 1.45, 95% CI, 1.15-1.83) and positive nodal stage (P = .001, HR = 1.51, 95% CI, 1.18-1.94). This retrospective study validates Mills et al's proposal to adapt the thresholds for mitotic counts to better assess the histological grade of the highly proliferative mammary carcinomas encountered in the cat.
Keyphrases
  • high grade
  • prognostic factors
  • endothelial cells
  • squamous cell carcinoma
  • lymph node
  • neoadjuvant chemotherapy
  • machine learning
  • big data
  • young adults
  • electronic health record
  • pluripotent stem cells