Comprehensive Histologic Scoring to Maximize the Predictability of Pathology-generated Equation of Breast Cancer
Thaer Khoury1, Xiao Huang, Xiwei Chen
1Departments of *Pathology †Biostatistics & Bioinformatics ‡Oncology, Roswell Park Cancer Institute, Buffalo, NY.
Background:
Pathology-generated equations have been introduced to predict Oncotype DX recurrence score (ORS) in breast cancer. The purpose of the study is to improve these equations.
Materials And Methods:
Slides from 416 (test set) consecutive breast cancers with available Oncotype DX were reviewed. A validation set (n=91) was prospectively scored using the generated formulas from the test set. The following histopathologic features were graded: Nottingham grade (designated as current Nottingham grade), necrosis, and degree of tumor-infiltrating lymphocytes. The following data were extracted from the pathology report: Nottingham grade (designated as reported Nottingham grade), tumor size, ER/PR Allred scores, HER2 status, and ORS. Equations were calculated, one included the reported Nottingham grade, one included the current Nottingham grade, and one included the current Nottingham grade with the other significant histopathologic variables.
Results:
In the equation that included the reported Nottingham grade, ER, PR, and HER2, the overall concordance with the ORS was 64.86%. After excluding the intermediate category detected by the formula, the concordance rate was 95.28%. When the current Nottingham grade was included, the concordance rate became 69.61% and 98.62%, respectively. When necrosis and the degree of tumor-infiltrating lymphocytes were added to the previous equation, these rates became 70.1% and 98.63%, respectively.
Conclusions:
Our equation has better correlation with ORS than previously published results.
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