Research team Fredrik Strand
Exploring the use of image-based artificial intelligence in precision screening and treatment optimization for breast cancer
We are living in exciting times. The use of Artificial Intelligence is on the rise. The true limits to what can be achieved are not yet known. Our focus are the more than 600,000 women who die of breast cancer each year in the world.
IMAGE-BASED AI FOR PRECISION SCREENING
Early detection is the most important measure, until now achieved by population-wide mammography screening every 1 to 3 years for women in a certain age groups. Nevertheless, around 30 percent of the cancers diagnosed for women who participate in screening are clinically detected in the interval between two screenings; and nearly 15 percent of screen-detected cancers are 2 cm or larger. Employing AI to tailor and improve the screening could bring the benefit of early detection to more women keeping in mind the scarcity of radiological resources - image-based AI for precision screening.
IMAGE-BASED AI FOR PRECISION MEDICINE
Oncological treatment options have become more diverse and have contributed to prolonged survival. However, each cancer resides in its own microenvironment of evolutionary pressure, which means no two cancers are alike. The use of preoperative, neoadjuvant, medical treatment has increased since it offers an opportunity to study how the cancer reacts to a certain medication while it remains in the breast. The need to predict which treatment is likely best for each patient, and the need to study how the cancer reacts over time, puts increasing demands on the precision of imaging and on cross-disciplinary analysis between radiology (non-invasive imaging of the whole breast, and the whole cancer) and pathology (invasive sample of the cancer). In addition, surgery has become more precise and less extensive. Employing AI on medical images could improve mapping of the tumor extent, predicting the best therapy, and continuously assessing the response - image-based AI for precision treatment.
Our studies are cross-disciplinary. Since 2016 we have built a strong collaboration with computer science researchers at the Royal School of Engineering (KTH) in Stockholm.
CANCER DETECTION – SCREENTRUST CAD
To explore what happens when AI is employed in clinical practice, we are heading the clinical study ScreenTrust CAD, with clinical partner Capio S:t Göran Hospital, in which we explore the use of AI as an independent reader of screening mammograms. The study is a paired screen-positive design to determine whether AI plus one radiologist is non-inferior to two radiologists in double-reading. In addition, we collaborate with Jennifer Viberg Johansson at the Center for Research ethics and Bioethics at Uppsala University to understand the qualitative aspects of AI interaction among radiologists and among screening participants.
UNDETECTED CANCER – SCREENTRUST MRI
We have jointly developed deep networks that can be used to assess for each woman: the risk of getting breast cancer in the future, and how difficult it would be to visualize an early cancer in the mammogram. Those algorithms are now explored in our ongoing randomized clinical study ScreenTrustMRI which is mainly financed by Medtechlabs. The primary end-point is to determine whether our approach reduces the number of interval cancers and advanced cancers compared to mammography-only. We will also determine how many cancers are detected per 1000 women screened by MRI to enable a cost effectiveness analysis compared to regular screening.
ALGORITHM VALIDATION – VAI-B
Not all AI algorithms are alike. In a JAMA Oncology study in 2020, we demonstrated major differences between three commercial algorithms in our retrospective dataset of mammograms and clinical outcomes. The size of our dataset is around 500,000 mammography exams and 3,500 MRI exams and more than 1 million screening assessments. Continuing our independent evaluation work, we are heading the national project VAI-B aiming to establish a Swedish platform for validation of AI algorithms in breast imaging. The project is a collaboration between the regions and universities in Stockholm, Malmö and Linköping; and the breast cancer association. The project is funded by Vinnova and the Regional Cancer Centers. The aim is to create a scalable platform delivering value to hospitals: impact assessment and evaluations, as well as to AI developers: external validation and benchmarking.
TREATMENT RESPONSE PREDICTION - RADIOVAL
Recently, the EU project RadioVal was awarded funding in the Horizon Europe program. We form part of the consortium, in which we will be responsible for clinical validation of machine learning algorithms for the prediction of therapy response for neoadjuvant treatment. This work is carried out in collaboration with breast oncologist Theodoros Foukakis.
MULTI-MODALITY ALGORITHMS - qMRI
Our work has to a large extent been based on mammograms, but now we are adding other modalities such as MRI. Together with KTH we will start developing multi-modality networks possibly also including pathology data and images. The aim is to annotate more than 3000 MRI Breast exams, and to train networks to detect cancer, predict risk, and to predict histopathological characteristics, as well as to predict response to neoadjuvant therapy.
An important aspect of AI algorithms is what enables humans to trust them – or not. Previously it was popular to demand that algorithms should be able to explain how they reached a certain conclusion. Now, the focus has shifted towards trustworthiness. This could mean that even if the AI algorithm is not explainable in human terms, it can be demonstrated that it works to an extent that humans require, e.g., that the algorithm is accurate, robust, transparent, et cetera. It could also mean continuous surveillance of AI algorithms in clinical use to detect when they diverge from the expected performance. We are involved, in collaboration with KTH, to examine this important topic.
Our main funders are currently Medtechlabs, Regional Cancer Centers in Collaboration, Vinnova, Region Stockholm, EU Horizon Europe, and the Swedish Breast Cancer Association.
Core Team members
Fredrik Strand, MD, PhD, Radiologist, Team Leader
Fernando Cossío Ramirez, BSc, Research Engineer
Karin Dembrower, MD, PhD, Radiologist
Dimitra Ntoula, MD, Radiologist
Shirin Rasoul Olyaei, Radiographer
Mattie Salim, MD, PhD student, Radiologist in training
Yanlu Wang, MD, PhD, MRI physicist
Athanasios Zouzos, MD, PhD student, Radiologist
Apostolia Tsirikoglou, PhD, Postdoc
Haiko Schurz, PhD, Postdoc
Collaborative Team members
Hossein Azizpour, MSc, PhD, Computer scientist
Martin Eklund, MSc, PhD, Biostatistician
Theodoros Foukakis, MD, PhD, Oncologist
Hanna Fredholm, MD, PhD, Surgeon
Irma Fredriksson, MD, PhD, Surgeon
Johan Hartman, MD, PhD, Pathologist
Yue Liu, MSc, PhD student in Computer science
Mattias Rantalainen, MSc, PhD, Biostatistician
Kevin Smith, MSc, PhD, Computer scientist
Moein Sorkhei, MSc in Computer science
A Call for Controlled Validation Data Sets: Promoting the Safe Introduction of Artificial Intelligence in Breast Imaging.
Strand F, Patel BK, Allen B
J Am Coll Radiol 2021 11;18(11):1564-1565
Toward robust mammography-based models for breast cancer risk.
Yala A, Mikhael PG, Strand F, Lin G, Smith K, Wan YL, Lamb L, Hughes K, Lehman C, Barzilay R
Sci Transl Med 2021 01;13(578):
Widespread Parenchymal Abnormalities and Pulmonary Embolism on Contrast-Enhanced CT Predict Disease Severity and Mortality in Hospitalized COVID-19 Patients.
Jalde FC, Beckman MO, Svensson AM, Bell M, Sköld M, Strand F, Nyren S, Kistner A
Front Med (Lausanne) 2021 ;8():666723
A Multi-million Mammography Image Dataset and Population-Based Screening Cohort for the Training and Evaluation of Deep Neural Networks-the Cohort of Screen-Aged Women (CSAW).
Dembrower K, Lindholm P, Strand F
J Digit Imaging 2020 04;33(2):408-413
Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction.
Dembrower K, Liu Y, Azizpour H, Eklund M, Smith K, Lindholm P, Strand F
Radiology 2020 02;294(2):265-272
Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy.
Nguyen AA, Arasu VA, Strand F, Li W, Onishi N, Gibbs J, Jones EF, Joe BN, Esserman LJ, Newitt DC, Hylton NM
Tomography 2020 06;6(2):101-110
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study.
Dembrower K, Wåhlin E, Liu Y, Salim M, Smith K, Lindholm P, Eklund M, Strand F
Lancet Digit Health 2020 09;2(9):e468-e474
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.
Schaffter T, Buist DSM, Lee CI, Nikulin Y, Ribli D, Guan Y, Lotter W, Jie Z, Du H, Wang S, Feng J, Feng M, Kim HE, Albiol F, Albiol A, Morrell S, Wojna Z, Ahsen ME, Asif U, Jimeno Yepes A, Yohanandan S, Rabinovici-Cohen S, Yi D, Hoff B, Yu T, Chaibub Neto E, Rubin DL, Lindholm P, Margolies LR, McBride RB, Rothstein JH, Sieh W, Ben-Ari R, Harrer S, Trister A, Friend S, Norman T, Sahiner B, Strand F, Guinney J, Stolovitzky G, , Mackey L, Cahoon J, Shen L, Sohn JH, Trivedi H, Shen Y, Buturovic L, Pereira JC, Cardoso JS, Castro E, Kalleberg KT, Pelka O, Nedjar I, Geras KJ, Nensa F, Goan E, Koitka S, Caballero L, Cox DD, Krishnaswamy P, Pandey G, Friedrich CM, Perrin D, Fookes C, Shi B, Cardoso Negrie G, Kawczynski M, Cho K, Khoo CS, Lo JY, Sorensen AG, Jung H
JAMA Netw Open 2020 03;3(3):e200265
External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms.
Salim M, Wåhlin E, Dembrower K, Azavedo E, Foukakis T, Liu Y, Smith K, Eklund M, Strand F
JAMA Oncol 2020 10;6(10):1581-1588
Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening.
Eriksson M, Czene K, Strand F, Zackrisson S, Lindholm P, Lång K, Förnvik D, Sartor H, Mavaddat N, Easton D, Hall P
Radiology 2020 11;297(2):327-333
Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.
Li W, Newitt DC, Gibbs J, Wilmes LJ, Jones EF, Arasu VA, Strand F, Onishi N, Nguyen AA, Kornak J, Joe BN, Price ER, Ojeda-Fournier H, Eghtedari M, Zamora KW, Woodard SA, Umphrey H, Bernreuter W, Nelson M, Church AL, Bolan P, Kuritza T, Ward K, Morley K, Wolverton D, Fountain K, Lopez-Paniagua D, Hardesty L, Brandt K, McDonald ES, Rosen M, Kontos D, Abe H, Sheth D, Crane EP, Dillis C, Sheth P, Hovanessian-Larsen L, Bang DH, Porter B, Oh KY, Jafarian N, Tudorica A, Niell BL, Drukteinis J, Newell MS, Cohen MA, Giurescu M, Berman E, Lehman C, Partridge SC, Fitzpatrick KA, Borders MH, Yang WT, Dogan B, Goudreau S, Chenevert T, Yau C, DeMichele A, Berry D, Esserman LJ, Hylton NM
NPJ Breast Cancer 2020 Nov;6(1):63
Predictive Value of Breast MRI Background Parenchymal Enhancement for Neoadjuvant Treatment Response among HER2- Patients.
Arasu VA, Kim P, Li W, Strand F, McHargue C, Harnish R, Newitt DC, Jones EF, Glymour MM, Kornak J, Esserman LJ, Hylton NM,
J Breast Imaging 2020 Aug;2(4):352-360
Range of Radiologist Performance in a Population-based Screening Cohort of 1 Million Digital Mammography Examinations.
Salim M, Dembrower K, Eklund M, Lindholm P, Strand F
Radiology 2020 10;297(1):33-39
The association between breast cancer risk factors and background parenchymal enhancement at dynamic contrast-enhanced breast MRI.
Hellgren R, Saracco A, Strand F, Eriksson M, Sundbom A, Hall P, Dickman PW
Acta Radiol 2020 Dec;61(12):1600-1607
Breast cancer imaging - A rapidly evolving discipline.
Strand F, Zackrisson S
Breast 2019 Aug;46():58-63
Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case-case analysis.
Hinton B, Ma L, Mahmoudzadeh AP, Malkov S, Fan B, Greenwood H, Joe B, Lee V, Strand F, Kerlikowske K, Shepherd J
Med Phys 2019 Mar;46(3):1309-1316
Discontinuation of adjuvant hormone therapy among breast cancer patients not previously attending mammography screening.
He W, Eriksson L, Törnberg S, Strand F, Hall P, Czene K
BMC Med 2019 01;17(1):24
Localized mammographic density is associated with interval cancer and large breast cancer: a nested case-control study.
Strand F, Azavedo E, Hellgren R, Humphreys K, Eriksson M, Shepherd J, Hall P, Czene K
Breast Cancer Res 2019 01;21(1):8
The future of breast cancer screening: what do participants in a breast cancer screening program think about automation using artificial intelligence?
Jonmarker O, Strand F, Brandberg Y, Lindholm P
Acta Radiol Open 2019 Dec;8(12):2058460119880315
Long-term prognostic implications of risk factors associated with tumor size: a case study of women regularly attending screening.
Strand F, Humphreys K, Holm J, Eriksson M, Törnberg S, Hall P, Azavedo E, Czene K
Breast Cancer Res 2018 04;20(1):31
Longitudinal fluctuation in mammographic percent density differentiates between interval and screen-detected breast cancer.
Strand F, Humphreys K, Eriksson M, Li J, Andersson TM, Törnberg S, Azavedo E, Shepherd J, Hall P, Czene K
Int J Cancer 2017 Jan;140(1):34-40
Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study.
Strand F, Humphreys K, Cheddad A, Törnberg S, Azavedo E, Shepherd J, Hall P, Czene K
Breast Cancer Res 2016 10;18(1):100
Common and unique components of inhibition and working memory: an fMRI, within-subjects investigation.
McNab F, Leroux G, Strand F, Thorell L, Bergman S, Klingberg T
Neuropsychologia 2008 Sep;46(11):2668-82
Phonological working memory with auditory presentation of pseudo-words -- an event related fMRI Study.
Strand F, Forssberg H, Klingberg T, Norrelgen F
Brain Res 2008 May;1212():48-54
Oral pretreatment of donors with UDCA results in an enrichment of UDCA in the portal vein and in the bile
Strand F, Ericzon Bg, Marschall Hu, Kallen R, Nils A, Wernersson A, Nowak G
LIVER TRANSPLANTATION 2004;10(6):C49-C49