Virtual Clinical Trials (VCTs) of medical imaging
Project description: Virtual Clinical Trials (VCTs) in medical imaging have been used to design, evaluate, and optimize imaging systems; to prototype clinical trials; and to assist with regulatory approval. VCTs are used as a rapid and cost-effective alternative to conventional clinical trials, allowing researchers to answer fundamental questions using in-silico simulations. My team of researchers at the Univ of Pennsylvania (UPenn) developed a VCT framework that encompasses the use of computer models of human anatomy, imaging modalities, and image interpretation. I have been developing anthropomorphic breast model to support simulations of breast imaging since 1998. By 2009 a software framework to design and optimize breast imaging systems using VCTs was completed. Numerous academic laboratories, industrial developers, and governmental regulatory bodies have since adopted VCTs. Several VCT use-cases have been published, including evaluation and optimization of DBT, denoising of breast x-ray images, and dermatology imaging. Within LUCI, my VCT project is focused on supporting preclinical (and potentially co-clinical) studies of breast cancer screening. In addition, I actively participate on several collaborative subprojects on using VCTs in other organs/diseases.
Implications for healthcare: VCTs offer an efficient evaluation and optimization of medical imaging systems, reducing the limitations of conventional clinical trials (by cost, duration and dependence on available patients). This is important for more efficient and sustainable technology development – and its adoption into clinical practice. In addition, VCTs can be used to design and test future clinical trials, preventing potential pitfalls and inconclusive outcomes.
Implications from patient perspective: VCTs offer benefits of faster evaluation and adoption of novel imaging methods. VCTs can test imaging or image analysis methods, and identify patents that benefit the most – for personalized treatment.
Implications for research: VCTs enable affordable platforms for computational research and development of novel imaging systems and/or image analysis methods. This improves democratization of science -- as valuable clinical research may be performed even without immediate access to busy and costly clinical infrastructure.
Lund Univ & SUS: M Dustler, S Zackrisson, A Tingberg, H Tomic, R Axelsson, K Johnson, D Förnvik, P Timberg, A Bjerkén, G. Hellgren, H Isaksson, M Cinthio
Penn: B Barufaldi, A Maidment, E Conant, R Acciavatti
Others: S Ng (Real Time Tomography), O Diaz (Univ Barcelona), I Sechopoulos, J Teuwen (Radboud Univ), H Bosmans (Katholieke Univ Leuven), V Vasudev, T Kimpe (Barco Healthcare), S Makrogiannis (Delaware State Univ), M Lago (FDA), D Pokrajac (Boeing, Inc.),
Funding: EU H2020 Marie Curie fellowship, Bröstcancerförbundet, MAS Onc
- Refine breast anatomy simulation: The level of realism required in VCTs is an open question, perpetually appearing in VCT discussions (and review of VCT related publications and funding applications). I believe that VCTs need to be as realistic as needed for the simulation task in question. To that end, I constantly push the boundary of simulated tissue details and physical properties related to a wide range of VCT tasks. Especially important is the topic of simulating breast lesions, and their growth over time. It allows to design VCTs of multiple years of screening, taking into account the changes in lesions size and features between successive screening episodes. In addition, we are currently expanding the simulation of mechanical properties of the breast tissue, to support VCTs of MI and DBTMI. This subproject is closely related to the use of VCTs in other organ/disease which require appropriate modeling of the respective anatomy and pathological changes (e.g., skin cancer; subcutaneous tissue, trabecular bones, lung parenchyma, etc.)
Funding: EU H2020 Marie Curie fellowship, BF
Publications (recent): Torlegård (IWBI 2020); Tomic (SPIE 2021, ECR 2021); Axelsson (SPIE 2021); Bakic (RPD 2021, MIA 2021); Vasudev (SPIE 2019, 2020, 2021)
- Refine and expand acquisition modeling: The level of VCT realism is also related to the adequate simulation of medical imaging methods. My VCT simulation pipeline has been originally motivated by the breast x-ray imaging: digital mammography (DM) and DBT. The pipeline is, however, modality agnostic – it allows to exchange modules for the acquisition simulation. We have recently expanded the VCT pipeline to include detailed simulation of MI imaging, to support VCTs of MI and DBTMI. Also, as mentioned previously, this subproject also provides appropriate image acquisition models for VCTs of other organs/diseases (e.g., dermatoscopy).
Funding: EU H2020 Marie Curie fellowship, BF
Publications (recent): Axelsson (SPIE 2021); Bakic (RPD 2021, MIA 2021); Vasudev (SPIE 2019, 2020, 2021)
- Design and run VCTs of breast cancer imaging: The VCT approach is of interest in testing novel imaging methods or hypothetical screening strategies. At LUCI, this is of importance for supporting the evaluation of DBTMI in breast cancer screening. In addition, VCTs can be used to explore by simulation the effects of MBTST (or even expanded MBTST with DBTMI) – or their followup -- on specific patient populations.
Funding: EU H2020 Marie Curie fellowship
- (future) Explore other applications and benefits of VCTs: My VCT framework represents a research testbed to explore hypothetical imaging applications. Potential research topics include (i) Use of VCT simulated images in Transfer Learning in AI to reduce the demand on large clinical datasets, or (ii) Combine virtual and clinical data into co-clinical trials.
- Design and run VCTs beyond breast imaging: In addition to my primary focus on breast imaging, I have bene using VCTs in other organs and diseases, including dermatology (collab w/ Barco Healthcare), subcutaneous fat in mid-thigh (collab w/ Delaware State Univ). Potential future research topics may include VCTs of echocardiography (for the analysis of cardiotoxicity in oncological treatment); lung parenchyma (for COVID-19 or COPD research); trabecular bone (for osteoporosis), etc.
Funding: Unfunded supervision of PhD student @ Barco/Univ Ghent; consulting with Delaware State Univ
Publications: Vasudev (SPIE 2019, 2020, 2021)