Dynamic Prediction Improved 5-Year Breast Cancer Risk Prediction Performance

On Friday, August 2, 2024, Joy Jiang, PhD, Associate Professor in the Division of Public Health Sciences at Washington University School of Medicine presented a talk entitled "Dynamic prediction with repeated mammograms improved 5-year breast cancer risk prediction performance". Dr. Jiang is a tenured Associate Professor in the Division of Public Health Sciences at Washington University School of Medicine. Her research focuses on the development of novel statistical methods?for dynamic risk prediction with censored outcomes, especially on feature extraction in high-dimensional time-varying risk factors and images. Dr. Jiang's method can help identify women at high risk who might benefit from supplemental screening or risk reduction strategies. This talk was hosted by the AI Precision Health Institute at the University of Hawai'i Cancer Center as part of a popular seminar series featuring leading AI researchers from around the world.

Abstract

Current image-based long term risk prediction models do not fully utilize existing data. Dynamic risk prediction models that incorporate current and prior screening mammogram images have not been investigated for use in routine care.?Dr. Jiang trained a dynamic model using repeated screening mammograms?at Washington University to predict 5-year risk. She then applied the model to the external validation data to evaluate discrimination performance measured by AUC and calibrated to US incidence from SEER.?Using 3 years of prior mammogram images available at the current screening mammogram, she obtained a 5-year AUC of 0.80 (95% CI, 0.78, 0.83) in the external validation when controlling for age and BI-RADS density. This represents a significant improvement over only using the current visit mammogram AUC?0.74 (95% CI, 0.71, 0.77)?(p<0.01). When calibrated to SEER incidence rates, a risk ratio of 21.1 was observed comparing high (>0.04%) to very low (<0.003%) 5-year risk. Adding previous screening mammogram images improves 5-year breast cancer risk prediction beyond static models used in clinics today. It can identify women at high risk who might benefit from supplemental screening or risk reduction strategies.

Joy Jiang, PhD

Joy Jiang, PhD is a tenured Associate Professor in the Division of Public Health Sciences at Washington University School of Medicine. She has obtained her PhD in Statistics in 2018 at the University of Waterloo, Canada, and subsequently pursued a postdoctoral fellowship in Biostatistics at Harvard School of Public Health. Her research focuses on the development of novel statistical methods?for dynamic risk prediction with censored outcomes, especially on feature extraction in high-dimensional time-varying risk factors and images.

AI Precision Health Institute

In 2022 we formed the AI Precision Health Institute Affinity Group Seminar Series to discuss current trends and applications of AI in cancer research and clinical practice. The group brings together AI researchers in a variety of fields including computer science, engineering, nutrition, epidemiology, and radiology with clinicians and advocates. The goal is to foster collaborative interactions to solve problems in cancer that were thought to be unsolvable a decade ago before the broad use of deep learning and AI in medicine.

Past Seminars In This Seminar Series

Large Language Models For Biomedical Research, July 2024

What Happens If We Use Synthetic Data Without Any Curation, June 2024

Predictive AI Models - Data Standards In Action, May 2024

AI Powered Dermatology Tools and Consumer Decision Making, April 2024

AI Decodes Waveforms To Help Prevent Sudden Cardiac Death, March 2024

Mitigating Unintended Consequences of AI in Biomedicine, February 2024

How To Build Responsible, Safe, Trusted AI For Precision Health, January 2024

Robust Interpretability Methods For Large Language Models, December 2023

Machine Learning Captures Insights Into Brain Tumor Biology, November 2023

Comparing AI Algorithms To Predict 5 Year Breast Cancer Risk, October 2023

Disrupting the Indigenous DNA SupplyChain, September 2023

AI Based Lab Test Approved To Phenotype, Grade Breast Cancer, July 2023

Trustworthy AI and Clinical Validation In Breast Cancer Imaging, June 2023

AI For Ultrasound For Real-Time Breast Cancer Decision Support, May 2023

Deep Learning To Diagnose Breast Cancer With High Accuracy, April 2023

Precision Oncology: Empowering Radiologists With AI, January 2023

Machine Learning For Personalized Cancer Screening, December 2022

AI Driven Surgical Robots To Diagnose/Treat Prostate Cancer, November 2022

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Copyright ? 2024 Margaretta Colangelo. All Rights Reserved.

This article was written by Margaretta Colangelo. Margaretta is a leading AI analyst who tracks significant milestones in AI in healthcare. She consults with AI healthcare companies and writes about some of the companies she consults with. Margaretta serves on the advisory board of the AI Precision Health Institute at the University of Hawai?i?Cancer Center @realmargaretta