Affiliations: [a] Women’s Imaging Unit, University Hospital of Strasbourg, Hautepierre Hospital, Strasbourg Cedex, France | [b] Radiology Department, Saint Catherine Hospital of Saverne, Saverne, France | [c] Pathology Department, University Hospital of Strasbourg, Hautepierre Hospital, Strasbourg Cedex, France | [d] ICube - IMAGeS, UMR 7357, Illkirch, France | [e] Fédération de Médecine Translationnelle de Strasbourg (FMTS), Strasbourg, France | [f] Surgery Department, ICANS (Strasbourg Europe), Strasbourg, France | [g] Institute of Genetics and Molecular and Cellular Biology CNRS UMR 7104, INSERM U964, University of Strasbourg, Illkirch, France | [h] Breast and Thyroid Imaging Unit, ICANS (Strasbourg Europe), Strasbourg, France
Correspondence:
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Corresponding author: Sébastien Molière. Women’s Imaging Unit, University Hospital of Strasbourg, Hautepierre Hospital, BP 23025 67033, Strasbourg Cedex, France. E-mail: moliere.seb@gmail.com
Abstract: 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.
