Evaluation of the international Ki67 working group cut point recommendations for early breast cancer: comparison with 21-gene assay results in a large integrated health care system.

PURPOSE: The International Ki67 Working Group (IKWG) has developed training for immunohistochemistry (IHC) scoring reproducibility and recommends cut points of ≤ 5% and ≥ 30% for prognosis in ER+, HER2-, stage I/II breast cancer. We examined scoring reproducibility following IKWG training and evaluated these cut points for selecting patients for further testing with the 21-gene Recurrence Score (RS) assay. METHODS: We included 307 women aged 50+ years with node-negative, ER+PR+HER2- breast cancer and with available RS results. Slides from the diagnostic biopsy were stained for Ki67 and scored using digital image analysis (IA). Two IHC pathologists underwent IKWG training and visually scored slides, blinded to each other and IA readings. Interobserver reproducibility was examined using intraclass correlation (ICC) and Kappa statistics. RESULTS: Depending on reader, 8.8-16.0% of our cohort had Ki67 ≤ 5% and 11.4-22.5% had scores ≥ 30%. The ICC for Ki67 scores by the two pathologists was 0.82 (95% CI 0.78-0.85); it was 0.79 (95% CI 0.74-0.83) for pathologist 1 and IA and 0.76 (95% CI 0.71-0.80) for pathologist 2 and IA. For Ki67 scores ≤ 5%, the percentages with RS < 26 were 92.6%, 91.8%, and 90.9% for pathologist 1, pathologist 2, and IA, respectively. For Ki67 scores ≥ 30%, the percentages with RS ≥ 26 were 41.5%, 51.4%, and 27.5%, respectively. CONCLUSION: The IKWG's Ki67 training resulted in moderate to strong reproducibility across readers but cut points had only moderate overlap with RS cut points, especially for Ki67 ≥ 30% and RS ≥ 26; thus, their clinical utility for a 21-gene assay testing pathway remains unclear.

Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women.

BACKGROUND: Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding. METHODS: We conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40-74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view. RESULTS: The adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 (95% confidence interval, 1.18-1.57), 0.85 (0.77-0.93) and 1.44 (1.26-1.66) for LIBRA and 1.44 (1.33-1.55), 0.81 (0.74-0.89) and 1.54 (1.34-1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2-5 years and 5-10 years after the baseline mammogram. CONCLUSION: Associations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk.