Background Using prior mammograms from patients with delays in their breast cancer diagnoses, we sought to describe in-vivo growth kinetics of untreated breast cancer to determine if the time they… Click to show full abstract
Background Using prior mammograms from patients with delays in their breast cancer diagnoses, we sought to describe in-vivo growth kinetics of untreated breast cancer to determine if the time they became clinically apparent can be predicted. Methods Patient and tumor characteristics were collected from those who presented with “missed,” untreated breast cancer to a breast center in a single institution. Only patients whose biopsied masses revealed estrogen receptor-positive, Her2-negative (ER+/Her2−) invasive cancers were included. Two attending radiologists reviewed images from prior mammograms. Rates of change in volume were calculated in mm 3 /day, and a logarithmic equation was used to calculate tumor volume doubling time (TVDT). A Spearman's Rho correlation was performed for the continuous variables, and the Mann–Whitney U and Kruskal–Wallis tests were used to compare categorical data. A p value < 0.05 was considered statistically significant. Logistic regression was performed to determine if patient or tumor characteristics were correlated to tumor growth velocity. Results Of the 36 ER+/Her2− invasive breast cancers included in the analysis, 13 (36%) were at least cT2 (of TNM), 7 (19%) were grade 3, and 7 (19%) were node positive at diagnosis. Grade ( p = 0.043) and pathologic invasive tumor size ( p = 0.001) were positively correlated to tumor growth velocity. Median TVDT was 385 days (23–1897). Age, nodal positivity, Oncotype Dx® Recurrence Score, time of diagnostic delay, and spheroid-ellipsoid discrepancy (SED) were not related to tumor growth velocity in this sample. Conclusion In this cohort of patients with untreated ER+/Her2− invasive breast cancers, grade and pathologic tumor size were found to be positively correlated to growth velocity. The growth rates in a homogeneous group of tumors varied widely and could not be predicted. One possible explanation for this finding is that other difficult-to-measure biologic factors such as tumor microenvironment may play a greater role in tumor progression than traditional clinicopathologic characteristics.
               
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