bibtext: @InProceedings{ Kar_Longitudinal_MICCAI2024,
                   author = { Karaman, Batuhan K. and Dodelzon, Katerina and Akar, Gozde B. and Sabuncu, Mert R. },
                   title = { { Longitudinal Mammogram Risk Prediction } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: Breast cancer is one of the leading causes of mortality among women worldwide. Early detection and risk assessment play a crucial role in improving survival rates. Therefore, annual or biennial mammograms are often recommended for screening in high-risk groups. Mammograms are typically interpreted by expert radiologists based on the Breast Imaging Reporting and Data System (BI-RADS), which provides a uniform way to describe findings and categorizes them to indicate the level of concern for breast cancer. Recently, machine learning (ML) and computational approaches have been developed to automate and improve the interpretation of mammograms. However, both BI-RADS and the ML-based methods focus on the analysis of data from the present and sometimes the most recent prior visit. While it has been shown that temporal changes in image features of longitudinal scans are valuable for quantifying breast cancer risk, no prior work has systematically studied this. In this paper, we extend a state-of-the-art ML model to ingest an arbitrary number of longitudinal mammograms and predict future breast cancer risk. On a large scale dataset, we demonstrate that our model, LoMaR, achieves state-of-the-art performance when presented with only the present mammogram. Furthermore, we use LoMaR to characterize the predictive value of prior visits. Our results show that longer histories (e.g., up to four prior annual mammograms) can significantly boost the accuracy of predicting future breast cancer risk, particularly beyond the short-term. Our code and model weights are available at https://github.com/batuhankmkaraman/LoMaR.
title: Longitudinal Mammogram Risk Prediction
authors: Karaman, Batuhan K. Dodelzon, Katerina Akar, Gozde B. Sabuncu, Mert R.