ERS3055. Variability Between Different Breast Cancer Risk Models and Effect on Risk of Various Parameters
Authors * Denotes Presenting Author
  1. Brady Young *; Northeast Ohio Medical University
  2. Hannah Girgis; Northeast Ohio Medical University
  3. Kajal Madan; Northeast Ohio Medical University
Breast cancer is the second most common cancer among women in the U.S. The 2022 American Cancer Society estimates 287,850 new cases of breast cancer will be diagnosed this year. From 2013 to 2018, the breast cancer death rate fell by 1%, attributing this decline to earlier screening and cancer awareness. Early diagnosis of breast cancer can reduce morbidity and intense treatment plans (such as mastectomies and chemotherapy). There are many screening tests used to predict the 5-year and lifetime risk of breast cancer. These screening tests and software programs have large variabilities in their risk prediction and can lead to overdiagnosis. Recent publications show a 5% to 19% overdiagnosis of breast cancer in women. In our study we predicted the 5-year risk and lifetime risk of breast cancer for Southwood Imaging (Youngstown, OH) patients using 4 different breast cancer risk models. We then compared the variability in the risk models using t-test statistical analysis. The breast cancer risk models compared in our study were Mammorisk, Tyer Cuzick 2013, Tyer Cuzick 2017, and Gail.

Materials and Methods:
Our sample size consisted of 242 participants from Southwood Imaging who came in for their annual mammogram. Most women were between ages 40-70 years old. While Southwood patients awaited their mammogram, they were asked to fill out a questionnaire. The questionnaire contained 19 questions about the patient’s personal health, prior breast biopsies, prior cancer therapies, and family history of breast cancer. Afterwards, we collected the 242 surveys and each patients' Mammorisk results. 209 surveys gave a substantial amount of information to perform the risk assessment. We then put the patient information into the additional screening tests Tyer Cuzick 2013, Tyer Cuzick 2017, and Gail. We calculated the 5 year and lifetime risk factor of each patient. We measured the variability of the 5 year, 10 year, and lifetime risk factor between models using t-test statistical analysis.

The results showed statistically significant differences between lifetime risk for breast cancer risk models. Mammorisk and TC2017 lifetime risks differed by 0.544 ± 4.995 (-9.247, 10.334). The difference in lifetime risk between the Mammorisk and TC2013 models was -2.666 ± 6.883 (-16.157, 10.824). Mammorisk and Gail lifetime risks varied by 0.104 ± 4.147 (-8.553, 8.761). The difference between TC2017 and TC2013 lifetime risks was -3.363 ± 4.278 (-11.747, 5.022). TC2017 and Gail lifetime risks differed by -0.677 ± 3.676 (-7.883, 6.528). Lastly, the difference between TC2013 and Gail lifetime breast cancer risk was 2.668 ± 5.505 (-8.121, 13.457).

Prediction of lifetime risk of breast cancer varies significantly between different risk models. Lifetime risk predicted by the Mammorisk, TC2013, TC2017, and Gail models significantly differs. This creates variability which is difficult for physicians to interpret, and hence will affect clinical decision making for the patient. Creating a standardized approach would better serve physicians and patients to provide a more accurate risk model.