Abstract B080: Advancing Breast Cancer Risk Prediction: Introducing the Gabbi Risk Assessment Model (GRAM)

Abstract

Abstract Introduction: Advanced breast cancer risk models hold promise in implementing more effective and accurate screening strategies, facilitating early detection while minimizing screening-related harm compared to current guidelines. However, integrating machine learning risk models into clinical practice requires essential steps to be taken, including continual refinement to enhance their accuracy, validation across diverse populations, and demonstrating their ability to optimize clinical workflows. Gabbi Risk Assessment Model (GRAM), an innovative machine learning model, is a binary classifier based on medical claims developed to provide personalized breast cancer risk prediction. GRAM efficiently estimates risk and utilizes potentially missing risk factor information. Furthermore, it offers easy integration into EMR systems, thus optimizing its practical application. Methods: Construction of the GRAM utilized a medical claims dataset of 500k members from IQVIA carefully sampled from the entire IQVIA population (IQVIA acquires ~94% of the US claims data) with 4 years of history. The information extracted from the dataset included demographics, and medical history in the form of diagnostic and treatment codes, laboratory tests, and medications. The dataset consisted of 250k women aged 18-70 with a breast cancer diagnosis and a random sample of 250k women aged 18-70 without a known breast cancer diagnosis. For the selected cohorts, a 4-year history before the first breast cancer diagnosis was obtained. The control group was carefully chosen to possess similar statistical properties as the group with breast cancer. All women had a minimum enrollment period of 4 years before diagnosis with the same insurance provider. The GRAM was developed with 4 main steps: 1) member selection - members with breast cancer based on selection criteria defined above were in the test group, members without in the control group; 2) feature engineering to ensure that GRAM learns from medical history before a first diagnosis; 3) model development & testing - training/fine tuning using the ensemble methods and neural network, followed by hyperparameter tuning using a held out validation dataset; and 4) model performance on the held out validation set and identification of key risk factors contributing to the breast cancer risk. Results and Conclusion: By addressing these challenges and harnessing the potential of machine learning, GRAM has the capacity to revolutionize breast cancer risk estimation. Its integration into clinical practice can lead to more targeted and efficient screening strategies, resulting in earlier detection and improved patient outcomes. Preliminary results have shown that the GRAM surpasses the current standard of care models (GAIL and Tyrer Cusick) by at least 20% in sensitivity, and AUC ROC with no decline in specificity. GRAM achieved 91% AUC ROC (balanced data), 86% sensitivity, and 80% specificity on a held out dataset. As research progresses, continuous refinement and validation, the GRAM model holds promise to make significant contributions in the fight against breast cancer and identifying the correlated risk factors. Citation Format: Priyanshi Jain, Shaleen K Theiler, Kaitlin Christine. Advancing Breast Cancer Risk Prediction: Introducing the Gabbi Risk Assessment Model (GRAM) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B080.