Objective: Preoperative diagnosis of phyllodes tumor (PT) is challenging, core-needle biopsy (CNB) has a significant rate of understaging, resulting in suboptimal surgical planification. We hypothesized that the association of imaging data to CNB would improve preoperative diagnostic accuracy compared to biopsy alone.
Methods: In this retrospective pilot study, we included 59 phyllodes tumor with available preoperative imaging, CNB and surgical specimen pathology.
Results: Two ultrasound features: tumor heterogeneity and tumor shape were associated with tumor grade, independently of CNB results. Using a machine learning classifier, the association of ultrasound features with CNB results improved accuracy of preoperative tumor classification up to 84%.
Conclusion: An integrative approach of preoperative diagnosis, associating ultrasound features and CNB, improves preoperative diagnosis and could thus optimize surgical planification.
Keywords: Phyllodes tumor; Ultrasound; machine learning classifier; preoperative diagnosis.