Vignesh A. Arasu, MD, PhD

vignesh.a.arasu@kp.org

Arasu, Vignesh

Vignesh A. Arasu, MD, PhD, is a Research Scientist with the Kaiser Permanente Northern California Division of Research. He is also a practicing radiologist subspecializing in breast imaging at Kaiser Permanente Vallejo Medical Center.

Dr. Arasu conducts research at the intersection of medical imaging, breast cancer, and artificial intelligence (AI). As an embedded clinician researcher, he evaluates priority operational issues in breast cancer medical imaging to accelerate implementation and innovation. As a research principal investigator, he oversees two randomized trials investigating the use of AI for breast cancer screening.

Dr. Arasu earned his medical degree and completed his radiology residency and fellowship at the University of California, San Francisco (UCSF). He also holds a PhD in Epidemiology and Translational Science from UCSF.

Current Positions

Primary Research Interests

  • Breast cancer
  • Medical imaging
  • Risk-based screening
  • Artificial intelligence and computer vision deep learning

Studies

Reducing Low Value Surveillance in the Management of Adrenal Nodules

The primary goal of this retrospective cohort study is to determine whether routine imaging follow-up of small (<4cm) incidental adrenal nodules detected on cross-sectional imaging improves the early diagnosis of adrenocortical carcinoma or pheochromocytoma.

Investigator: Arasu, Vignesh

Funder: TPMG Delivery Science Projects Program

WISDOM: A Precision Medicine Platform to Improve Personalization of Screening and Support Targeted Risk Reduction (Project 3)

Our ongoing WISDOM trial of risk-based breast cancer screening and prevention is predicted to reduce harms from screening by 50% while continuing to reduce advanced breast cancers as much as annual screening; however, the underlying risk model has high potential for improvement. The overall goal of Project 3 is to continuously develop and improve models for predicting the risk of breast cancer by combining emerging advances in artificial intelligence, inherited genetics, and tumor molecular assays. We will use these advanced models to predict risk of fast- vs. slow-growing breast cancer subtypes and to create a next-generation WISDOM risk-based screening and prevention platform, which, based on our preliminary data, we expect will be substantially more useful than the current WISDOM risk model and enable further reduction in harms from both lethal breast cancers and abundant false positives.

Investigator: Arasu, Vignesh

Funder: National Cancer Institute/NIH/DHHS

Integration of mammographic AI, clinical, and genomic information to improve breast cancer subtype–specific risk-based screening and prevention (WISDOM P01, Project 3)

Our ongoing WISDOM trial of risk-based breast cancer screening and prevention is predicted to reduce harms from screening by 50% while continuing to reduce advanced breast cancers as much as annual screening. However, the underlying risk model has high potential for improvement. The overall goal of Project 3 is to continuously develop and improve models for predicting the risk of breast cancer by combining emerging advances in artificial intelligence, inherited genetics, and tumor molecular assays. We will use these advanced models to predict risk of fast- vs. slow-growing breast cancer subtypes and to create a next-generation WISDOM risk-based screening and prevention platform, which, based on our preliminary data, we expect will be substantially more useful than the current WISDOM risk model and enable further reduction in harms from both lethal breast cancers and abundant false positives.

Investigator: Arasu, Vignesh

Funder: National Cancer Institute

Evaluating AI Bias and Implementing AI in the WISDOM trial

This study will evaluate disparities in performance of mammographic AI Mirai algorithm among patient subgroups in a retrospective Kaiser Permanente Northern California cohort; and evaluate workflow and technical challenges to implementation of AI across national WISDOM sites.

Investigator: Arasu, Vignesh

Funder: McGovern Foundation

Grasshopper: A Novel Clinical Dashboard for Radiologists

The primary goal of this project is to evaluate differences in radiology reporting specificity when radiologists use a novel dashboard that surfaces pertinent clinical information at the time of image interpretation. Secondary goals include the evaluation of downstream clinical impact of reporting specificity, including but not limited to decreased utilization of prescription medications and follow-up imaging.

Investigator: Arasu, Vignesh

Funder: TPMG Delivery Science Projects Program

Publications

Integrating breast cancer polygenic risk scores at scale in the WISDOM Study: a national randomized personalized screening trial

Authors: Fergus KB;Arasu, Vignesh A;Shieh Y; et al.

Genome Med. 2025 Aug 28;17(1):97. Epub 2025-08-28.

PubMed abstract

Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women

Authors: Habel, Laurel A;Achacoso, Ninah;Sieh, Weiva;et al.

Breast Cancer Res. 2023 Aug 06;25(1):92. Epub 2023-08-06.

PubMed abstract

Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study

Authors: Arasu, Vignesh A;Kornak, John;Lewis, Donald A;Yoon, Hyo-Chun;Lee, Catherine;et al.

Radiology. 2023 Jun;307(5):e222733.

PubMed abstract

CT Use Reduction In Ostensive Ureteral Stone (CURIOUS)

Authors: Durant, Edward J; Engelhart, Darcy C; Ma, Annie A; Warton, E Margaret; Arasu, Vignesh A; Bernal, Raymond; Rauchwerger, Adina S; Reed, Mary E; Vinson, David R

Am J Emerg Med. 2023 May;67:168-175. Epub 2023-02-24.

PubMed abstract

Telehealth for Preoperative Evaluation of Patients With Breast Cancer During the COVID-19 Pandemic

Authors: Tang, Annie; Arasu, Vignesh A; Liu, Raymond; Habel, Laurel A; Kushi, Lawrence H; Chang, Sharon B; et al.

Perm J. 2022 06 29;26(2):54-63. Epub 2022-06-15.

PubMed abstract

Care in the time of COVID-19: impact on the diagnosis and treatment of breast cancer in a large, integrated health care system

Authors: Tang, Annie; Arasu, Vignesh A; Liu, Raymond; Habel, Laurel A; Kushi, Lawrence H; Permanente Medical Group Breast Research Collaborative,; et al.

Breast Cancer Res Treat. 2022 Feb;191(3):665-675. Epub 2022-01-06.

PubMed abstract

Implementation of Intraoperative Ultrasound Localization for Breast-Conserving Surgery in a Large, Integrated Health Care System is Feasible and Effective

Authors: Chakedis, Jeffery M; Arasu, Vignesh A; Permanente Medical Group Breast Research Collaborative,; et al.

Ann Surg Oncol. 2021 Oct;28(10):5648-5656. Epub 2021-08-26.

PubMed abstract

ASO Visual Abstract: Implementation of Intraoperative Ultrasound Localization for Breast-Conserving Surgery in a Large, Integrated Health Care System is Feasible and Effective

Authors: Chakedis, Jeffery M; Arasu, Vignesh A; Permanente Medical Group Breast Research Collaborative,; et al.

Ann Surg Oncol. 2021 Sep 01.

PubMed abstract

Neodymium Magnetic Bead Ingestion in a Toddler

Authors: Hui, Kenneth J; Arasu, Vignesh A; Vinson, David R; Cotton, Dale M

Perm J. 2020;24. Epub 2020-04-16.

PubMed abstract

Detecting right ventricular dysfunction in patients diagnosed with low-risk pulmonary embolism: is routine computed tomographic pulmonary angiography sufficient?

Authors: Vinson DR; Arasu VA; Trujillo-Santos J

Eur Heart J. 2019 10 21;40(40):3356.

PubMed abstract

Related Stories

Mammography AI may have a role in breast cancer risk prediction

Mammography AI may have a role in breast cancer risk prediction

June 6, 2023

Artificial intelligence algorithms used to read mammograms were a better predictor of a woman’s future breast cancer risk than a commonly used risk assessment tool.
Our Team
Back To Top