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Research problem and specific questions: Approximately 4% of middle-aged women in Sweden have breast implants, the large majority (~80%) for cosmetic reasons. At the same time there are rising concerns for adverse health effects, including reports of multiple adverse symptoms (‘breast implant illness’) as well as some psychiatric- and autoimmune conditions among women with breast implants. Yet, comprehensive research efforts are still needed to determine if these risks are preexisting or caused by the procedure itself. Therefore, the aim of the BRISK study is to leverage unique Swedish population-based data sources to determine pre- and post- implant procedure risks of: 1) mental disorders and psychotropic drug use

2) autoimmune and chronic fatigue conditions

3) sick-leave and disability pension among women with cosmetic breast implants. Data and method: We will identify 28 835 women with cosmetic breast implants through the Breast Implant Register (BRIMP

N=20 779 operated 2014-22) and ongoing mammography studies (N=8 056 with breast implants, identified 2008-21). By record linkage to the population-based Patient, Primary Care- and Prescription Medicines Registers, and Databases for Health Insurance and Labor Market, we will compare the rates of psychiatric- and autoimmune conditions as well as sick leave and disability pension among women with breast implants to that of their full sisters and an age- and region matched cohort of women (1:10 unexposed women). We will assess pre-surgery rates of these conditions and take them into account when assessing the post-surgery rates of the studied health outcomes.Societal relevance and utilization: Solid evidence on major health outcomes associated with breast implant surgery is currently lacking, leaving women considering this surgery option largely uninformed on long-term health effects. This comprehensive investigation will provide valuable information for health care policy and the growing population of women with breast implants. Plan for project realization:  We seek three years of funding for a post-doc, record linkages, database management and presentation of the results to stakeholders and the scientific community. Implementation of record linkages and preliminary analyses will be completed during the first year (2025), statistical analyses during the second year (2026), and publication of three scientific papers in leading international scientific journals by the end of the third year (2027).

Around 4% of middle-aged women have breast implants, the large majority for cosmetic reasons. There are rising concerns for adverse health effects of breast implants, yet comprehensive research efforts are needed to determine if the indicated health risks are preexisting or caused by the procedure itself. Therefore, the aim of the BRISK study is to leverage unique Swedish population-based data to determine risks of mental disorders, autoimmune disease, chronic fatigue, and other ̽»¨¾«Ñ¡ conditions among women with cosmetic breast implants. In this three-year project, we will identify 29 011 women with cosmetic breast implants through the Breast Implant Quality Register (operated 2014-22) and ongoing mammography studies (identified 2008-21), as well as ~5 000 women undergoing permanent removal of breast implants (1997-2022). By record linkage to the population-based Patient, Primary Care- and Prescription Registers, we will compare the rates of the studied health risks of women with breast implants to that of their full sisters and an age- and region matched cohort of women. We will assess pre-surgery rates of these conditions and take them into account when assessing post-surgery rates. Solid evidence on major health outcomes of breast implant surgery is currently lacking, leaving women considering this surgery option largely uninformed on long-term health effects. The BRISK study will provide valuable evidence for this patient group and health care policy worldwide.

Around 25% of all women who are diagnosed with breast cancer eventually die from the disease. The treatment of breast cancer patients can be improved. We have conducted retrospective studies demonstrating that AI models may reach radiologist-level performance for screening mammography. The proposed project is aiming at the diagnostic process following detection. I plan to develop AI models based on magnetic resonance imaging (MRI), which provide images richer in information compared to mammogrpahy.The project will leverage my postdoc experience with machine-learning models for breast MRI and my continued collaboration with KTH in terms of state-of-the-art AI models. In our joint AI model development for mammography, with KTH, we used convolutional neural networks. Recently, a new approach, vision transformer has been shown to be able to outperform the convolutional neural networks for image-based tasks. Therefore, my plan is to apply vision transformers to breast MRI images to address three important areas in the diagnostic process for breast cancer: image segmentation aiding radiologists to identify anatomic structures in the MRI images

radiology-pathology correlation to ascertain that biopsies correlate with image findings

therapy response prediction to inform the choice of neoadjuvant therapy. The research will tie into a recently approved EU horizon project where we will gain access to a common pool of breast cancer imaging data, and for which I am the Swedish PI.

Purpose and aimsThis project aims to develop tools for prediction of response to neoadjuvant (pre-operative) therapy (NAT) and prognostication of post-surgery risk of recurrence in breast cancer. To this end, input from radiology, digital pathology, genomics and informative clinical variables will be integrated using a machine learning (ML)-based multi-modal fusion strategy. Project organisation, time plan and scientific methodsThree academic clinical trials and one population-based cohort of NAT (N=2500) will be used to train single-source predictive model priors that will be ensembled into integrative multi-omics predictive models. These will be validated externally in independent cohorts of ~3000 patients.The project will be divided into work packages (WP), corresponding to each of the data modalities. WP1 data and material collection (year 1-4)

WP2-3 transcriptomics and genomics in tissue and blood (y 1-3)

WP4 radiomics using mammography and magnetic resonance imaging (y 1-3)

WP5 pathomics (y 1-3)

WP6 model integration (y 3-4)

WP7 external validation (y 4-5). ImportanceThe project will contribute with novel ML methodology for clinical medicine and a precision oncology solution for optimizing NAT selection and risk stratification that will lead to less over- and under treatment, sparing patients from unnecessary toxicities and reducing financial burden to healthcare systems, and ultimately improving prognosis for patients with breast cancer.