An integrative ultrasound-pathology approach to improve preoperative phyllodes tumor classification: A pilot study

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.

In this retrospective pilot study, we included 59 phyllodes tumor with available preoperative imaging, CNB and surgical specimen pathology.

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%.

An integrative approach of preoperative diagnosis, associating ultrasound features and CNB, improves preoperative diagnosis and could thus optimize surgical planification.