Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging

• URL: http://arxiv.org/abs/2509.00900v1
• Date: Sun, 31 Aug 2025 15:25:19 GMT
• Title: Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
• Authors: Manon A. Dorster, Felix J. Dorfner, Mason C. Cleveland, Melisa S. Guelen, Jay Patel, Dania Daye, Jean-Philippe Thiran, Albert E. Kim, Christopher P. Bridge,
• Abstract summary: Current risk prediction models do not incorporate digital breast tomosynthesis (DBT) imaging, which was FDA-approved for breast cancer screening in 2011.<n>We present a deep learning (DL)-based framework capable of forecasting an individual patient's 5-year breast cancer risk directly from screening.
• Score: 8.57802267735077
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As early detection of breast cancer strongly favors successful therapeutic outcomes, there is major commercial interest in optimizing breast cancer screening. However, current risk prediction models achieve modest performance and do not incorporate digital breast tomosynthesis (DBT) imaging, which was FDA-approved for breast cancer screening in 2011. To address this unmet need, we present a deep learning (DL)-based framework capable of forecasting an individual patient's 5-year breast cancer risk directly from screening DBT. Using an unparalleled dataset of 161,753 DBT examinations from 50,590 patients, we trained a risk predictor based on features extracted using the Meta AI DINOv2 image encoder, combined with a cumulative hazard layer, to assess a patient's likelihood of developing breast cancer over five years. On a held-out test set, our best-performing model achieved an AUROC of 0.80 on predictions within 5 years. These findings reveal the high potential of DBT-based DL approaches to complement traditional risk assessment tools, and serve as a promising basis for additional investigation to validate and enhance our work.

Related papers

• Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports [4.447609555191978]
  Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with most cases diagnosed at stage IV and a five-year overall survival rate below 5%.<n>We developed and evaluated a deep learning fusion model that integrates radiology reports and CT imaging to predict PDAC risk.
  arXiv  Detail & Related papers  (2025-03-31T21:13:42Z)
• Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer [71.91773485443125]
  Transfer learning is a technique that leverages acquired features from a domain with richer data to enhance the performance of a domain with limited data. In this paper, we investigate the improvement of clinically significant prostate cancer prediction in T2-weighted images through transfer learning from breast cancer.
  arXiv  Detail & Related papers  (2024-05-13T15:57:27Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
  Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer. The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts. This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:40:56Z)
• Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data [3.982926115291704]
  Multi-modal Transformer (MMT) is a neural network that utilizes mammography and ultrasound synergistically. MMT tracks temporal tissue changes by comparing current exams to prior imaging. For 5-year risk prediction, MMT attains an AUROC of 0.826, outperforming prior mammography-based risk models.
  arXiv  Detail & Related papers  (2023-11-06T16:01:42Z)
• Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures [3.883460584034766]
  The survival rates for breast cancer patients in certain third-world countries, like South Africa, are alarmingly low. Medical specialists and researchers have turned to domain-specific AI approaches, specifically deep learning models, to develop end-to-end solutions. This research focuses on evaluating the performance of various cutting-edge convolutional neural network (CNN) architectures in comparison to a relatively new model called the Vision Trans-former (ViT)
  arXiv  Detail & Related papers  (2023-05-31T15:21:34Z)
• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
  The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
  arXiv  Detail & Related papers  (2023-04-12T15:08:34Z)
• A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data [82.74877848011798]
  Cancer-Net BCa is a multi-institutional open-source benchmark dataset of volumetric CDI$s$ imaging data of breast cancer patients. Cancer-Net BCa is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer.
  arXiv  Detail & Related papers  (2023-04-12T05:41:44Z)
• Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images [8.756888862171195]
  We present a new method, PRIME+, for breast cancer risk prediction that leverages prior mammograms using a transformer decoder. We validate our approach on a dataset with 16,113 exams and demonstrate that it effectively captures patterns of changes from prior mammograms. Experimental results show that our model achieves a statistically significant improvement over the state-of-the-art based model, with a C-index increase from 0.68 to 0.73.
  arXiv  Detail & Related papers  (2023-03-28T03:05:25Z)
• Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging [66.63200823918429]
  We investigate enhancing clinical support for breast cancer with deep learning models. We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
  arXiv  Detail & Related papers  (2022-11-10T03:02:12Z)
• Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model [4.3359550072619255]
  We have curated and made publicly available a large-scale dataset of digital breast tomosynthesis images. It contains 22,032 reconstructed volumes belonging to 5,610 studies from 5,060 patients. We developed a single-phase deep learning detection model and tested it using our dataset to serve as a baseline for future research.
  arXiv  Detail & Related papers  (2020-11-13T18:33:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.