The impact of breast cancer biological subtyping on tumor size assessment by ultrasound and mammography - a retrospective multicenter cohort study of 6543 primary breast cancer patients

BACKGROUND: Mammography and ultrasound are the gold standard imaging techniques for preoperative assessment and for monitoring the efficacy of neoadjuvant chemotherapy in breast cancer. Maximum accuracy in predicting pathological tumor size non-invasively is critical for individualized therapy and surgical planning. We therefore aimed to assess the accuracy of tumor size measurement by ultrasound and mammography in a multicentered health services research study. METHODS: We retrospectively analyzed data from 6543 patients with unifocal, unilateral primary breast cancer. The maximum tumor diameter was measured by ultrasound and/or mammographic imaging. All measurements were compared to final tumor diameter determined by postoperative histopathological examination. We compared the precision of each imaging method across different patient subgroups as well as the method-specific accuracy in each patient subgroup. RESULTS: Overall, the correlation with histology was 0.61 for mammography and 0.60 for ultrasound. Both correlations were higher in pT2 cancers than in pT1 and pT3. Ultrasound as well as mammography revealed a significantly higher correlation with histology in invasive ductal compared to lobular cancers (p < 0.01). For invasive lobular cancers, the mammography showed better correlation with histology than ultrasound (p = 0.01), whereas there was no such advantage for invasive ductal cancers. Ultrasound was significantly superior for HR negative cancers (p < 0.001). HER2/neu positive cancers were also more precisely assessed by ultrasound (p < 0.001). The size of HER2/neu negative cancers could be more accurately predicted by mammography (p < 0.001). CONCLUSION: This multicentered health services research approach demonstrates that predicting tumor size by mammography and ultrasound provides accurate results. Biological tumor features do, however, affect the diagnostic precision.