Background: Invasive breast cancer is a frequently diagnosed disease that now comes with an ever expanding array of therapeutic management options. We assessed the effects of 20 prognostic factors in a multivariate context.
Methods: We accrued clinical data for 156 consecutive patients with stage 1-3 primary invasive breast cancer who were diagnosed in 1989-1990 at the Henrietta Banting Breast Center, and followed to 1995. There is complete follow-up for 91% of patients (median follow-up of 4.9 years). The event of interest was distant recurrence (for distant disease-free survival, DFS). We used Cox and log-normal step-wise regression to assess the multivariate effects of the following factors on DFS: age, tumor size, nodal status, histology, tumor and nuclear grade, lymphovascular and perineural invasion (LVPI), ductal carcinoma-in-situ (DCIS) type, DCIS extent, DCIS at edge of tumor, ER and PgR, ERICA, adjuvant systemic therapy, ki67, S-phase, DNA index, neu oncogene, and pRb.
Results: There was strong evidence against the Cox assumption of proportional hazards for nodal status, and nodal status was not in the Cox step-wise model. With step-wise log-normal regression, a large tumor size (P < .001), positive nodes (P = .002), high nuclear grade (P = .01), presence of LVPI (P = .03), and infiltrating duct carcinoma not otherwise specified (P = .05) were associated with a reduction in DFS.
Conclusions: For nodal status, there was strong evidence against the Cox assumption of proportional hazards, and it was not included in the Cox model although it was in the log-normal model. Only traditional factors were included in the step-wise models. Thus, this statistical management of prognostic markers in breast cancer appears to be very important.